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Huang Q, Ye L. Multi-Task/Single-Task Joint Learning of Ultrasound BI-RADS Features. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:691-701. [PMID: 34871170 DOI: 10.1109/tuffc.2021.3132933] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Breast cancer is one of the most common cancers among women in the world. The breast imaging reporting and data system (BI-RADS) features effectively improve the accuracy and sensitivity of breast tumors. Based on the description of signs in BI-RADS, a quantitative scoring scheme is proposed based on ultrasound (US) data. This scheme includes feature extraction of high-level semantic, that is, an intermediate step interpreting the subsequent diagnosis. However, the scheme requires doctors to score the features of breast data, which is labor-intensive. To reduce the burden of doctors, we design a multi-task learning (MTL) framework, which can directly output the scores of different BI-RADS features from the raw US images. The MTL framework consists of a shared network that learns global features and K soft attention networks for different BI-RADS features. Thus, it enables the network not only to learn the potential correlation among different BI-RADS features, but also learn the unique specificity of each feature, which can assist each other and jointly improve the scoring accuracy. In addition, we group different BI-RADS features according to the correlation among tasks and build a multi-task/single-task joint framework. Experimental results on the US breast tumor dataset collected from 1859 patients with 4458 US images show that the proposed BI-RADS feature scoring framework achieves an average scoring accuracy of 84.91% for 11 BI-RADS features on the test dataset, which is helpful for the subsequent diagnosis of breast tumors.
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Assari Z, Mahloojifar A, Ahmadinejad N. A bimodal BI-RADS-guided GoogLeNet-based CAD system for solid breast masses discrimination using transfer learning. Comput Biol Med 2021; 142:105160. [PMID: 34995955 DOI: 10.1016/j.compbiomed.2021.105160] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 12/14/2022]
Abstract
Numerous solid breast masses require sophisticated analysis to establish a differential diagnosis. Consequently, complementary modalities such as ultrasound imaging are frequently required to evaluate mammographically further detected masses. Radiologists mentally integrate complementary information from images acquired of the same patient to make a more conclusive and effective diagnosis. However, it has always been a challenging task. This paper details a novel bimodal GoogLeNet-based CAD system that addresses the challenges associated with combining information from mammographic and sonographic images for solid breast mass classification. Each modality is initially trained using two distinct monomodal models in the proposed framework. Then, using the high-level feature maps extracted from both modalities, a bimodal model is trained. In order to fully exploit the BI-RADS descriptors, different image content representations of each mass are obtained and used as input images. In addition, using an ImageNet pre-trained GoogLeNet model, two publicly available databases, and our collected dataset, a two-step transfer learning strategy has been proposed. Our bimodal model achieves the best recognition results in terms of sensitivity, specificity, F1-score, Matthews Correlation Coefficient, area under the receiver operating characteristic curve, and accuracy metrics of 90.91%, 89.87%, 90.32%, 80.78%, 95.82%, and 90.38%, respectively. The promising results indicate that the proposed CAD system can facilitate bimodal suspicious mass analysis and thus contribute significantly to improving breast cancer diagnostic performance.
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Affiliation(s)
- Zahra Assari
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ali Mahloojifar
- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
| | - Nasrin Ahmadinejad
- Medical Imaging Center, Cancer Research Institute, Imam Khomeini Hospital Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences (TUMS), Tehran, Iran
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Ilesanmi AE, Chaumrattanakul U, Makhanov SS. Methods for the segmentation and classification of breast ultrasound images: a review. J Ultrasound 2021; 24:367-382. [PMID: 33428123 PMCID: PMC8572242 DOI: 10.1007/s40477-020-00557-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Breast ultrasound (BUS) is one of the imaging modalities for the diagnosis and treatment of breast cancer. However, the segmentation and classification of BUS images is a challenging task. In recent years, several methods for segmenting and classifying BUS images have been studied. These methods use BUS datasets for evaluation. In addition, semantic segmentation algorithms have gained prominence for segmenting medical images. METHODS In this paper, we examined different methods for segmenting and classifying BUS images. Popular datasets used to evaluate BUS images and semantic segmentation algorithms were examined. Several segmentation and classification papers were selected for analysis and review. Both conventional and semantic methods for BUS segmentation were reviewed. RESULTS Commonly used methods for BUS segmentation were depicted in a graphical representation, while other conventional methods for segmentation were equally elucidated. CONCLUSIONS We presented a review of the segmentation and classification methods for tumours detected in BUS images. This review paper selected old and recent studies on segmenting and classifying tumours in BUS images.
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Affiliation(s)
- Ademola E. Ilesanmi
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
| | | | - Stanislav S. Makhanov
- School of ICT, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12000 Thailand
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Lin L, Tao X, Yang W, Pang S, Su Z, Lu H, Li S, Feng Q, Chen B. Quantifying Axial Spine Images Using Object-Specific Bi-Path Network. IEEE J Biomed Health Inform 2021; 25:2978-2987. [PMID: 33788697 DOI: 10.1109/jbhi.2021.3070235] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Automatic estimation of indices from medical images is the main goal of computer-aided quantification (CADq), which speeds up diagnosis and lightens the workload of radiologists. Deep learning technique is a good choice for implementing CADq. Usually, to acquire high-accuracy quantification, specific network architecture needs to be designed for a given CADq task. In this study, considering that the target organs are the intervertebral disc and the dural sac, we propose an object-specific bi-path network (OSBP-Net) for axial spine image quantification. Each path of the OSBP-Net comprises a shallow feature extraction layer (SFE) and a deep feature extraction sub-network (DFE). The SFEs use different convolution strides because the two target organs have different anatomical sizes. The DFEs use average pooling for downsampling based on the observation that the target organs have lower intensity than the background. In addition, an inter-path dissimilarity constraint is proposed and applied to the output of the SFEs, taking into account that the activated regions in the feature maps of two paths should be different theoretically. An inter-index correlation regularization is introduced and applied to the output of the DFEs based on the observation that the diameter and area of the same object express an approximately linear relation. The prediction results of OSBP-Net are compared to several state-of-the-art machine learning-based CADq methods. The comparison reveals that the proposed methods precede other competing methods extensively, indicating its great potential for spine CADq.
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Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features. SENSORS 2020; 20:s20236838. [PMID: 33265900 PMCID: PMC7730057 DOI: 10.3390/s20236838] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/19/2020] [Accepted: 11/22/2020] [Indexed: 12/24/2022]
Abstract
This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.
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Shiji TP, Remya S, Lakshmanan R, Pratab T, Thomas V. Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179709] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- T. P. Shiji
- Department of Electronics Engineering, Model Engineering College, Kochi, India
| | - S. Remya
- Department of Electronics Engineering, Model Engineering College, Kochi, India
| | - Rekha Lakshmanan
- Department of Computer Engineering, KMEA College of Engineering, Kerala, India
| | | | - Vinu Thomas
- Department of Electronics Engineering, Model Engineering College, Kochi, India
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Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9224926] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.
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Fleury E, Marcomini K. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images. Eur Radiol Exp 2019; 3:34. [PMID: 31385114 PMCID: PMC6682836 DOI: 10.1186/s41747-019-0112-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 07/02/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this work was to evaluate computable Breast Imaging Reporting and Data System (BI-RADS) radiomic features to classify breast masses on ultrasound B-mode images. METHODS The database consisted of 206 consecutive lesions (144 benign and 62 malignant) proved by percutaneous biopsy in a prospective study approved by the local ethical committee. A radiologist manually delineated the contour of the lesions on greyscale images. We extracted the main ten radiomic features based on the BI-RADS lexicon and classified the lesions as benign or malignant using a bottom-up approach for five machine learning (ML) methods: multilayer perceptron (MLP), decision tree (DT), linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). We performed a 10-fold cross validation for training and testing of all classifiers. Receiver operating characteristic (ROC) analysis was used for providing the area under the curve with 95% confidence intervals (CI). RESULTS The classifier with the highest AUC at ROC analysis was SVM (AUC = 0.840, 95% CI 0.6667-0.9762), with 71.4% sensitivity (95% CI 0.6479-0.8616) and 76.9% specificity (95% CI 0.6148-0.8228). The best AUC for each method was 0.744 (95% CI 0.677-0.774) for DT, 0.818 (95% CI 0.6667-0.9444) for LDA, 0.811 (95% CI 0.710-0.892) for RF, and 0.806 (95% CI 0.677-0.839) for MLP. Lesion margin and orientation were the optimal features for all the machine learning methods. CONCLUSIONS ML can aid the distinction between benign and malignant breast lesion on ultrasound images using quantified BI-RADS descriptors. SVM provided the highest ROC-AUC (0.840).
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Affiliation(s)
- Eduardo Fleury
- Instituto Brasileiro de Controle do Câncer (IBCC), São Paulo, Brazil. .,Centro Universitário São Camilo, Curso de Medicina, São Paulo, Brazil.
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Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Huang Y, Han L, Dou H, Luo H, Yuan Z, Liu Q, Zhang J, Yin G. Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images. Biomed Eng Online 2019; 18:8. [PMID: 30678680 PMCID: PMC6346503 DOI: 10.1186/s12938-019-0626-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 01/16/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs). METHODS This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage. RESULTS We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5". CONCLUSION The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.
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Affiliation(s)
- Yunzhi Huang
- Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China
- College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China
| | - Luyi Han
- College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China
| | - Haoran Dou
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Honghao Luo
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Zhen Yuan
- Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Qi Liu
- College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
| | - Jiang Zhang
- College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China
| | - Guangfu Yin
- Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China.
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Breast tumor classification using different features of quantitative ultrasound parametric images. Int J Comput Assist Radiol Surg 2019; 14:623-633. [PMID: 30617720 DOI: 10.1007/s11548-018-01908-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 12/28/2018] [Indexed: 12/13/2022]
Abstract
RATIONALE AND OBJECTIVES The ultrasound B-mode-based morphological and texture analysis and Nakagami parametric imaging have been proposed to characterize breast tumors. Since these three feature categories of ultrasonic tissue characterization supply information on different physical characteristics of breast tumors, by combining the above methods is expected to provide more clues for classifying breast tumors. MATERIALS AND METHODS To verify the validity of the concept, raw data were obtained from 160 clinical cases. Six different types of morphological-feature parameters, four texture features, and the Nakagami parameter of benignancy and malignancy were extracted for evaluation. The Pearson's correlation matrix was used to calculate the correlation between different feature parameters. The fuzzy c-means clustering and stepwise regression techniques were utilized to determine the optimal feature set, respectively. The logistic regression, receiver operating characteristic curve, and support vector machine were used to estimate the diagnostic ability. RESULTS The best performance was obtained by combining morphological-feature parameter (e.g., standard deviation of the shortest distance), texture feature (e.g., variance), and the Nakagami parameter, with an accuracy of 89.4%, a specificity of 86.3%, a sensitivity of 92.5%, and an area under receiver operating characteristic curve of 0.96. There was no significant difference between using fuzzy c-means clustering, logistic regression, and support vector machine based on the optimal feature set for breast tumors classification. CONCLUSION Therefore, we verified that different physical ultrasonic features are functionally complementary and thus improve the performance in diagnosing breast tumors. Moreover, the optimal feature set had the maximum discriminating performance should be irrelative to the power of classifiers.
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Kaur P, Singh G, Kaur P. A Review of Denoising Medical Images Using Machine Learning
Approaches. Curr Med Imaging 2018; 14:675-685. [PMID: 30532667 PMCID: PMC6225344 DOI: 10.2174/1573405613666170428154156] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2017] [Revised: 03/25/2017] [Accepted: 04/07/2017] [Indexed: 12/12/2022]
Abstract
BACKGROUND This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach. DISCUSSION The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper. CONCLUSION The problem faced by the researchers during image denoising techniques and machine learning applications for clinical settings have also been discussed.
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Affiliation(s)
- Prabhpreet Kaur
- Address correspondence to this author at the Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar-143001, Punjab, India; E-mail:
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Moon WK, Chen IL, Yi A, Bae MS, Shin SU, Chang RF. Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:129-137. [PMID: 29903479 DOI: 10.1016/j.cmpb.2018.05.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 04/14/2018] [Accepted: 05/03/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES Axillary lymph node (ALN) status is a key indicator in assessing and determining the treatment strategy for patients with newly diagnosed breast cancer. Previous studies suggest that sonographic features of a primary tumor have the potential to predict ALN status in the preoperative staging of breast cancer. In this study, a computer-aided prediction (CAP) model as well as the tumor features for ALN metastasis in breast cancers were developed using breast ultrasound (US) images. METHODS A total of 249 malignant tumors were acquired from 247 female patients (ages 20-84 years; mean 55 ± 11 years) to test the differences between the non-metastatic (130) and metastatic (119) groups based on various features. After applying semi-automatic tumor segmentation, 69 quantitative features were extracted. The features included morphology and texture of tumors inside a ROI of breast US image. By the backward feature selection and linear logistic regression, the prediction model was constructed and established to estimate the likelihood of ALN metastasis for each sample collected. RESULTS In the experiments, the texture features showed higher performance for predicting ALN metastasis compared to morphology (Az, 0.730 vs 0.667). The difference, however, was not statistically significant (p-values > 0.05). Combining the textural and morphological features, the accuracy, sensitivity, specificity, and Az value achieved 75.1% (187/249), 79.0% (94/119), 71.5% (93/130), and 0.757, respectively. CONCLUSIONS The proposed CAP model, which combines textural and morphological features of primary tumor, may be a useful method to determine the ALN status in patients with breast cancer.
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Affiliation(s)
- Woo Kyung Moon
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - I-Ling Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ann Yi
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Min Sun Bae
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Sung Ui Shin
- Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 2018; 21:105-118. [PMID: 29681007 DOI: 10.1007/s40477-018-0297-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2018] [Accepted: 03/17/2018] [Indexed: 12/17/2022] Open
Abstract
PURPOSE To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions. METHODS 61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment and categorized lesions with BI-RADS 2013 lexicon. Integration of S-detect to in-training operators evaluations was performed by giving priority to S-detect analysis in case of disagreement. 2 × 2 contingency tables and ROC analysis were used to assess the diagnostic performances; inter-rater agreement was measured with Cohen's k; Bonferroni's test was used to compare performances. A significance threshold of p = 0.05 was adopted. RESULTS All operators showed sensitivity > 90% and varying specificity (50-75%); S-detect showed sensitivity > 90 and 70.8% specificity, with inter-rater agreement ranging from moderate to good. Lower specificities were improved by the addition of S-detect. The addition of elastography did not lead to any improvement of the diagnostic performance. CONCLUSIONS S-detect is a feasible tool for the characterization of breast lesions; it has a potential as a teaching tool for the less experienced operators.
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Affiliation(s)
- Mattia Di Segni
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy.
| | - Valeria de Soccio
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Vito Cantisani
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Giacomo Bonito
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Antonello Rubini
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | | | - Sveva Lamorte
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Valentina Magri
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Corrado De Vito
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, Italy
| | - Giuseppe Migliara
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, Italy
| | | | - Alessio Metere
- Department of Surgical Sciences, Sapienza University of Rome, viale del Policlinico 155, 00161, Rome, Italy
| | - Laura Giacomelli
- Department of Surgical Sciences, Sapienza University of Rome, viale del Policlinico 155, 00161, Rome, Italy
| | - Carlo de Felice
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
| | - Ferdinando D'Ambrosio
- U.O.C. Diagnostica per Immagini, P. O San Paolo - ASL Roma 4, Largo dei Donatori del Sangue 1, 00053, Civitavecchia (RM), Italy
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Rodríguez-Cristerna A, Gómez-Flores W, de Albuquerque Pereira WC. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:33-40. [PMID: 29157459 DOI: 10.1016/j.cmpb.2017.10.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 08/23/2017] [Accepted: 10/02/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Conventional computer-aided diagnosis (CAD) systems for breast ultrasound (BUS) are trained to classify pathological classes, that is, benign and malignant. However, from a clinical perspective, this kind of classification does not agree totally with radiologists' diagnoses. Usually, the tumors are assessed by using a BI-RADS (Breast Imaging-Reporting and Data System) category and, accordingly, a recommendation is emitted: annual study for category 2 (benign), six-month follow-up study for category 3 (probably benign), and biopsy for categories 4 and 5 (suspicious of malignancy). Hence, in this paper, a CAD system based on BI-RADS categories weighted by pathological information is presented. The goal is to increase the classification performance by reducing the common class imbalance found in pathological classes as well as to provide outcomes quite similar to radiologists' recommendations. METHODS The BUS dataset considers 781 benign lesions and 347 malignant tumors proven by biopsy. Moreover, every lesion is associated to one BI-RADS category in the set {2, 3, 4, 5}. Thus, the dataset is split into three weighted classes: benign, BI-RADS 2 in benign lesions; probably benign, BI-RADS 3 and 4 in benign lesions; and malignant, BI-RADS 4 and 5 in malignant lesions. Thereafter, a random forest (RF) classifier, denoted by RFw, is trained to predict the weighted BI-RADS classes. In addition, for comparison purposes, a RF classifier is trained to predict pathological classes, denoted as RFp. RESULTS The ability of the classifiers to predict the pathological classes is measured by the area under the ROC curve (AUC), sensitivity (SEN), and specificity (SPE). The RFw classifier obtained AUC=0.872,SEN=0.826, and SPE=0.919, whereas the RFp classifier reached AUC=0.868,SEN=0.808, and SPE=0.929. According to a one-way analysis of variance test, the RFw classifier statistically outperforms (p < 0.001) the RFp classifier in terms of the AUC and SEN. Moreover, the classification performance of RFw to predict weighted BI-RADS classes is given by the Matthews correlation coefficient that obtained 0.614. CONCLUSIONS The division of the classification problem into three classes reduces the imbalance between benign and malignant classes; thus, the sensitivity is increased without degrading the specificity. Therefore, the CAD based on weighted BI-RADS classes improves the classification performance of the conventional CAD systems. Additionally, the proposed approach has the advantage of being capable of providing a multiclass outcome related to radiologists' recommendations.
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Affiliation(s)
- Arturo Rodríguez-Cristerna
- Center for Research and Advanced Studies of the National Polytechnic Institute, ZIP 87130, Ciudad Victoria, Tamaulipas, Mexico
| | - Wilfrido Gómez-Flores
- Center for Research and Advanced Studies of the National Polytechnic Institute, ZIP 87130, Ciudad Victoria, Tamaulipas, Mexico.
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Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Comput Biol Med 2018; 92:168-175. [DOI: 10.1016/j.compbiomed.2017.11.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 11/15/2017] [Accepted: 11/15/2017] [Indexed: 01/12/2023]
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Kim Y, Furlan A, Borhani AA, Bae KT. Computer-aided diagnosis program for classifying the risk of hepatocellular carcinoma on MR images following liver imaging reporting and data system (LI-RADS). J Magn Reson Imaging 2017; 47:710-722. [PMID: 28556283 DOI: 10.1002/jmri.25772] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Accepted: 05/08/2017] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To develop and evaluate a computer-aided diagnosis (CAD) program for liver lesions on magnetic resonance (MR) images for classification of the risk of hepatocellular carcinoma (HCC) following the liver imaging reporting and data system (LI-RADS). MATERIALS AND METHODS Liver MR images from 41 patients with hyperenhancing liver lesions categorized as LR 3, 4, and 5 were evaluated by two radiologists. The major LI-RADS features of each index liver lesion were recorded, including size (maximum transverse diameter), presence of hyperenhancement, washout appearance, and capsule appearance. A CAD program was implemented to register MR images at different contrast-enhancement phases, segment liver lesions, extract lesion features, and classify lesions according to LI-RADS. The LI-RADS features quantified by CAD were compared with those assessed by radiologists using the intraclass correlation coefficient (ICC) and receiver operator curve (ROC) analyses. The LI-RADS categorization between CAD and radiologists was evaluated using the weighted Cohen's kappa coefficient. RESULTS The mean and standard deviation of the lesion diameters were 21 ± 11 mm (range, 7-70 mm) by radiologists and 22 ± 11 mm (range, 8-72 mm) by CAD (ICC, 0.96-0.97). The area under the curve (AUC) for the washout assessment by CAD was 0.79-0.93 with sensitivity 0.69-0.82 and specificity 0.79-1. The AUC for the capsule assessment by CAD was 0.79-0.9 with sensitivity 0.75-0.9 and specificity 0.82-0.96. The classifications by the radiologists and CAD coincided in 76-83% lesions (k = 0.57-0.71), while the agreements between radiologists were in 78% lesions (k = 0.59). CONCLUSION We developed a CAD program for liver lesions on MR images and showed a substantial agreement in the LI-RADS-based classification of the risk of HCCs between the CAD and radiologists. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:710-722.
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Affiliation(s)
- Youngwoo Kim
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Alessandro Furlan
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Amir A Borhani
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kyongtae T Bae
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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Li-Chun Hsieh K, Chen CY, Lo CM. Quantitative glioma grading using transformed gray-scale invariant textures of MRI. Comput Biol Med 2017; 83:102-108. [PMID: 28254615 DOI: 10.1016/j.compbiomed.2017.02.012] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Revised: 02/23/2017] [Accepted: 02/24/2017] [Indexed: 12/29/2022]
Abstract
BACKGROUND A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings. METHOD In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement. RESULTS The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334. CONCLUSIONS More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.
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Affiliation(s)
- Kevin Li-Chun Hsieh
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Cheng-Yu Chen
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Research Center of Translational Imaging, College of Medicine, Taipei Medical University, Taipei, Taiwan; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
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Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI. PLoS One 2017; 12:e0171342. [PMID: 28158235 PMCID: PMC5291485 DOI: 10.1371/journal.pone.0171342] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 01/19/2017] [Indexed: 01/01/2023] Open
Abstract
The effects of a computer-aided diagnosis (CAD) system based on quantitative intensity features with magnetic resonance (MR) imaging (MRI) were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105), a sensitivity of 79% (27/34), a specificity of 90% (64/71), and an area under the receiver operating characteristic curve (Az) of 0.89. In the evaluation, the radiologists' Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists' confidence rating.
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Hsieh KLC, Lo CM, Hsiao CJ. Computer-aided grading of gliomas based on local and global MRI features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:31-38. [PMID: 28187893 DOI: 10.1016/j.cmpb.2016.10.021] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 09/11/2016] [Accepted: 10/18/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. RESULTS Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement κ = 0.698, p < 0.001. CONCLUSIONS Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.
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Affiliation(s)
- Kevin Li-Chun Hsieh
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan; Translational Imaging Research Center, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
| | - Chih-Jou Hsiao
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
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Flores WG, Pereira WCDA. A contrast enhancement method for improving the segmentation of breast lesions on ultrasonography. Comput Biol Med 2017; 80:14-23. [PMID: 27875741 DOI: 10.1016/j.compbiomed.2016.11.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Revised: 11/10/2016] [Accepted: 11/12/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE This paper presents an adaptive contrast enhancement method based on sigmoidal mapping function (SACE) used for improving the computerized segmentation of breast lesions on ultrasound. METHODS First, from the original ultrasound image an intensity variation map is obtained, which is used to generate local sigmoidal mapping functions related to distinct contextual regions. Then, a bilinear interpolation scheme is used to transform every original pixel to a new gray level value. Also, four contrast enhancement techniques widely used in breast ultrasound enhancement are implemented: histogram equalization (HEQ), contrast limited adaptive histogram equalization (CLAHE), fuzzy enhancement (FEN), and sigmoid based enhancement (SEN). In addition, these contrast enhancement techniques are considered in a computerized lesion segmentation scheme based on watershed transformation. The performance comparison among techniques is assessed in terms of both the quality of contrast enhancement and the segmentation accuracy. The former is quantified by the measure, where the greater the value, the better the contrast enhancement, whereas the latter is calculated by the Jaccard index, which should tend towards unity to indicate adequate segmentation. RESULTS The experiments consider a data set with 500 breast ultrasound images. The results show that SACE outperforms its counterparts, where the median values for the measure are: SACE: 139.4, SEN: 68.2, HEQ: 64.1, CLAHE: 62.8, and FEN: 7.9. Considering the segmentation performance results, the SACE method presents the largest accuracy, where the median values for the Jaccard index are: SACE: 0.81, FEN: 0.80, CLAHE: 0.79, HEQ: 77, and SEN: 0.63. CONCLUSION The SACE method performs well due to the combination of three elements: (1) the intensity variation map reduces intensity variations that could distort the real response of the mapping function, (2) the sigmoidal mapping function enhances the gray level range where the transition between lesion and background is found, and (3) the adaptive enhancing scheme for coping with local contrasts. Hence, the SACE approach is appropriate for enhancing contrast before computerized lesion segmentation.
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Affiliation(s)
- Wilfrido Gómez Flores
- Technology Information Laboratory, Center for Research and Advanced Studies of the National Polytechnic Institute, Ciudad Victoria 87130, Tamaulipas, Mexico.
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A Fusion-Based Approach for Breast Ultrasound Image Classification Using Multiple-ROI Texture and Morphological Analyses. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:6740956. [PMID: 28127383 PMCID: PMC5227307 DOI: 10.1155/2016/6740956] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 10/31/2016] [Accepted: 11/15/2016] [Indexed: 11/18/2022]
Abstract
Ultrasound imaging is commonly used for breast cancer diagnosis, but accurate interpretation of breast ultrasound (BUS) images is often challenging and operator-dependent. Computer-aided diagnosis (CAD) systems can be employed to provide the radiologists with a second opinion to improve the diagnosis accuracy. In this study, a new CAD system is developed to enable accurate BUS image classification. In particular, an improved texture analysis is introduced, in which the tumor is divided into a set of nonoverlapping regions of interest (ROIs). Each ROI is analyzed using gray-level cooccurrence matrix features and a support vector machine classifier to estimate its tumor class indicator. The tumor class indicators of all ROIs are combined using a voting mechanism to estimate the tumor class. In addition, morphological analysis is employed to classify the tumor. A probabilistic approach is used to fuse the classification results of the multiple-ROI texture analysis and morphological analysis. The proposed approach is applied to classify 110 BUS images that include 64 benign and 46 malignant tumors. The accuracy, specificity, and sensitivity obtained using the proposed approach are 98.2%, 98.4%, and 97.8%, respectively. These results demonstrate that the proposed approach can effectively be used to differentiate benign and malignant tumors.
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Lo CM, Chan SW, Yang YW, Chang YC, Huang CS, Jou YS, Chang RF. Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:1201-1210. [PMID: 26825468 DOI: 10.1016/j.ultrasmedbio.2015.12.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 11/20/2015] [Accepted: 12/02/2015] [Indexed: 06/05/2023]
Abstract
A tumor-mapping algorithm was proposed to identify the same regions in different passes of automated breast ultrasound (ABUS). A total of 53 abnormal passes with 41 biopsy-proven tumors and 13 normal passes were collected. After computer-aided tumor detection, a mapping pair was composed of a detected region in one pass and another region in another pass. Location criteria, including the radial position as on a clock, the relative distance and the distance to the nipple, were used to extract mapping pairs with close regions. Quantitative intensity, morphology, texture and location features were then combined in a classifier for further classification. The performance of the classifier achieved a mapping rate of 80.39% (41/51), with an error rate of 5.97% (4/67). The trade-offs between the mapping and error rates were evaluated, and Az = 0.9094 was obtained. The proposed tumor-mapping algorithm was capable of automatically providing location correspondence information that would be helpful in reviews of ABUS examinations.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Si-Wa Chan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Ya-Wen Yang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Yi-Sheng Jou
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Shan J, Alam SK, Garra B, Zhang Y, Ahmed T. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:980-8. [PMID: 26806441 DOI: 10.1016/j.ultrasmedbio.2015.11.016] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 11/09/2015] [Accepted: 11/13/2015] [Indexed: 05/18/2023]
Abstract
This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.
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Affiliation(s)
- Juan Shan
- Department of Computer Science, Seidenberg School of Computer Science and Information Systems, Pace University, New York, New York, USA.
| | - S Kaisar Alam
- Improlabs Pte Ltd, Valley Point, Singapore; Computational Biomedicine Imaging and Modeling Center (CBIM), Rutgers University, Piscataway, New Jersey, USA; Department of Electrical & Electronic Engineering, Islamic University of Technology, Gazipur, Bangladesh
| | - Brian Garra
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA; Washington DC Veterans Affairs Medical Center, Washington, DC, USA
| | - Yingtao Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Tahira Ahmed
- Washington DC Veterans Affairs Medical Center, Washington, DC, USA
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Lo CM, Lai YC, Chou YH, Chang RF. Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 122:354-361. [PMID: 26421696 DOI: 10.1016/j.cmpb.2015.09.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 08/27/2015] [Accepted: 09/01/2015] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVES A computer-aided diagnosis (CAD) system based on the quantified color distributions in shear-wave elastography (SWE) was developed to evaluate the malignancies of breast tumors. METHODS For 57 benign and 31 malignant tumors, 18 SWE features were extracted from regions of interest (ROI), including the tumor and peritumoral areas. In the ROI, a histogram in each color channel was described using moments such as the mean, variance, skewness, and kurtosis. Moreover, three color channels were combined as a vector to evaluate tissue elasticity. The SWE features were then combined in a logistic regression classifier for breast tumor classification. RESULTS The performance of the CAD system achieved an accuracy of 81%. Combining the CAD system with a BI-RADS assessment obtained an Az improvement from 0.77 to 0.89 (p-value <0.05). CONCLUSIONS The combination of the proposed CAD system based on SWE features and the BI-RADS assessment would provide a promising diagnostic suggestion.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Chen Lai
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yi-Hong Chou
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; Department of Medicine, National Yang-Ming University, Taipei, Taiwan.
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Quantitative breast mass classification based on the integration of B-mode features and strain features in elastography. Comput Biol Med 2015; 64:91-100. [DOI: 10.1016/j.compbiomed.2015.06.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 06/17/2015] [Accepted: 06/17/2015] [Indexed: 12/21/2022]
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Lo CM, Moon WK, Huang CS, Chen JH, Yang MC, Chang RF. Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:2039-2048. [PMID: 25843514 DOI: 10.1016/j.ultrasmedbio.2015.03.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Revised: 02/22/2015] [Accepted: 03/01/2015] [Indexed: 06/04/2023]
Abstract
Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.
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Affiliation(s)
- Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jeon-Hor Chen
- Center for Functional Onco-Imaging and Department of Radiologic Science, University of California at Irvine, Irvine, California, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Min-Chun Yang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
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Zhou Z, Wu S, Chang KJ, Chen WR, Chen YS, Kuo WH, Lin CC, Tsui PH. Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features. J Med Biol Eng 2015; 35:178-187. [PMID: 25960706 PMCID: PMC4414937 DOI: 10.1007/s40846-015-0031-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Accepted: 06/16/2014] [Indexed: 12/01/2022]
Abstract
Posterior acoustic shadowing (PAS) can bias breast tumor segmentation and classification in ultrasound images. In this paper, half-contour features are proposed to classify benign and malignant breast tumors with PAS, considering the fact that the upper half of the tumor contour is less affected by PAS. Adaptive thresholding and disk expansion are employed to detect tumor contours. Based on the detected full contour, the upper half contour is extracted. For breast tumor classification, six quantitative feature parameters are analyzed for both full contours and half contours, including standard deviation of degree (SDD), which is proposed to describe tumor irregularity. Fifty clinical cases (40 with PAS and 10 without PAS) were used. Tumor circularity (TC) and SDD were both effective full- and half-contour parameters in classifying images without PAS. Half-contour TC [74 % accuracy, 72 % sensitivity, 76 % specificity, 0.78 area under the receiver operating characteristic curve (AUC), p > 0.05] significantly improved the classification of breast tumors with PAS compared to that with full-contour TC (54 % accuracy, 56 % sensitivity, 52 % specificity, 0.52 AUC, p > 0.05). Half-contour SDD (72 % accuracy, 76 % sensitivity, 68 % specificity, 0.81 AUC, p < 0.05) improved the classification of breast tumors with PAS compared to that with full-contour SDD (62 % accuracy, 80 % sensitivity, 44 % specificity, 0.61 AUC, p > 0.05). The proposed half-contour TC and SDD may be useful in classifying benign and malignant breast tumors in ultrasound images affected by PAS.
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Affiliation(s)
- Zhuhuang Zhou
- Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124 China
| | - Shuicai Wu
- Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124 China
| | - King-Jen Chang
- Department of Surgery, Cheng Ching General Hospital, Chung Kang Branch, Taichung, 407 Taiwan
- Department of Surgery, National Taiwan University Hospital, Taipei, 10048 Taiwan
| | - Wei-Ren Chen
- Department of Electrical Engineering, Yuan Ze University, Chung Li, 32003 Taiwan
| | - Yung-Sheng Chen
- Department of Electrical Engineering, Yuan Ze University, Chung Li, 32003 Taiwan
| | - Wen-Hung Kuo
- Department of Surgery, National Taiwan University Hospital, Taipei, 10048 Taiwan
| | - Chung-Chih Lin
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, 33302 Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, 33302 Taiwan
- Institute of Radiological Research, Chang Gung University and Hospital, Taoyuan, 33302 Taiwan
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Miranda GHB, Felipe JC. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 2014; 64:334-46. [PMID: 25453323 DOI: 10.1016/j.compbiomed.2014.10.006] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Revised: 09/20/2014] [Accepted: 10/01/2014] [Indexed: 11/29/2022]
Abstract
BACKGROUND Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions. The user provides parameters such as contour, shape and density and the system gives a suggestion about the BI-RADS classification. METHODS Initially, values of malignancy were defined for each image descriptor, according to the BI-RADS standard. When analyzing contour, for example, our method considers the matching of features and linguistic variables. Next, we created the fuzzy inference system. The generation of membership functions was carried out by the Fuzzy Omega algorithm, which is based on the statistical analysis of the dataset. This algorithm maps the distribution of different classes in a set. RESULTS Images were analyzed by a group of physicians and the resulting evaluations were submitted to the Fuzzy Omega algorithm. The results were compared, achieving an accuracy of 76.67% for nodules and 83.34% for calcifications. CONCLUSIONS The fit of definitions and linguistic rules to numerical models provided by our method can lead to a tighter connection between the specialist and the computer system, yielding more effective and reliable results.
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Affiliation(s)
- Gisele Helena Barboni Miranda
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo at Ribeirão Preto, Avenida Bandeirantes, 3900, Ribeirão Preto 14040-901, SP, Brazil.
| | - Joaquim Cezar Felipe
- Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo at Ribeirão Preto, Avenida Bandeirantes, 3900, Ribeirão Preto 14040-901, SP, Brazil.
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Lo CM, Chen YP, Chang YC, Lo C, Huang CS, Chang RF. Computer-Aided Strain Evaluation for Acoustic Radiation Force Impulse Imaging of Breast Masses. ULTRASONIC IMAGING 2014; 36:151-166. [PMID: 24894867 DOI: 10.1177/0161734613520599] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Acoustic radiation force impulse (ARFI) is a newly developed elastography technique that uses acoustic radiation force to provide additional stiffness information to conventional sonography. A computer-aided diagnosis (CAD) system was proposed to automatically specify the tumor boundaries in ARFI images and quantify the statistical stiffness information to reduce user dependence. The level-set segmentation was used to delineate tumor boundaries in B-mode images, and the segmented boundaries were then mapped to the corresponding area in ARFI images for a gray-scale calculation. A total of 61 benign and 51 malignant tumors were evaluated in the experiment. The CAD system based on the proposed ARFI features achieved an accuracy of 80% (90/112), a sensitivity of 80% (41/51), and a specificity of 80% (49/61), which is significantly better than that of the quantitative B-mode features (p < 0.05). The ARFI features were further combined with the B-mode features, including shape and texture features, to further improve performance (area under the curve [AUC], 0.90 vs. 0.86). In conclusion, the CAD system based on the proposed ARFI features is a promising and efficient diagnostic method.
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Affiliation(s)
- Chung-Ming Lo
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Yen-Po Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Chiao Lo
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan, Republic of China
| | - Ruey-Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
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