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Chen L, Zhang P, Shen L, Zhu H, Wang Y, Xu K, Tang S, Sun Y, Yan X, Lai B, Ouyang G. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders. Open Life Sci 2023; 18:20220765. [PMID: 38152585 PMCID: PMC10752001 DOI: 10.1515/biol-2022-0765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/10/2023] [Accepted: 10/12/2023] [Indexed: 12/29/2023] Open
Abstract
This study aimed to assess the feasibility of diagnosing secondary pulmonary fungal infections (PFIs) in patients with hematological malignancies (HM) using computerized tomography (CT) imaging and a support vector machine (SVM) algorithm. A total of 100 patients with HM complicated by secondary PFI underwent CT scans, and they were included in the training group. Concurrently, 80 patients with the same underlying disease who were treated at our institution were included in the test group. The types of pathogens among different PFI patients and the CT imaging features were compared. Radiomic features were extracted from the CT imaging data of patients, and a diagnostic SVM model was constructed by integrating these features with clinical characteristics. Aspergillus was the most common pathogen responsible for PFIs, followed by Candida, Pneumocystis jirovecii, Mucor, and Cryptococcus, in descending order of occurrence. Patients typically exhibited bilateral diffuse lung lesions. Within the SVM algorithm model, six radiomic features, namely the square root of the inverse covariance of the gray-level co-occurrence matrix (square root IV), the square root of the inverse covariance of the gray-level co-occurrence matrix, and small dependency low gray-level emphasis, significantly influenced the diagnosis of secondary PFIs in patients with HM. The area under the curve values for the training and test sets were 0.902 and 0.891, respectively. Therefore, CT images based on the SVM algorithm demonstrated robust predictive capability in diagnosing secondary PFIs in conjunction with HM.
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Affiliation(s)
- Lieguang Chen
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Pisheng Zhang
- Department of Hematology, The Affiliated People’s Hospital of Ningbo University, Ningbo, 315040, Zhejiang, China
| | - Lixia Shen
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Huiling Zhu
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Yi Wang
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Kaihong Xu
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Shanhao Tang
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Yongcheng Sun
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Xiao Yan
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Binbin Lai
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
| | - Guifang Ouyang
- Department of Hematology, Ningbo First Hospital, Ningbo, 315010, Zhejiang, China
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Ming M, Lu N, Qian W. Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection. Front Comput Neurosci 2023; 17:1115167. [PMID: 37602316 PMCID: PMC10436326 DOI: 10.3389/fncom.2023.1115167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 07/11/2023] [Indexed: 08/22/2023] Open
Abstract
This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment.
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Affiliation(s)
- Mao Ming
- Department of Infectious Disease, South of Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Na Lu
- Department of Colorectal Surgery, South of Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Wei Qian
- Department of Intensive Care Unit, South of Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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Wu W, Lei R, Niu K, Yang R, He Z. Automatic segmentation of colon, small intestine, and duodenum based on scale attention network. Med Phys 2022; 49:7316-7326. [PMID: 35833330 DOI: 10.1002/mp.15862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 06/23/2022] [Accepted: 06/29/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Automatic segmentation of colon, small intestine, and duodenum is a challenging task because of the great variability in the scale of the target organs. Multi-scale features are the key to alleviating this problem. Previous works focused on extracting discriminative multi-scale features through a hierarchical structure. Instead, the purpose of this work is to exploit these powerful multi-scale features more efficiently. METHODS A Scale Attention Module (SAM) was proposed to recalibrate multi-scale features by explicitly modeling their importance score adaptively. The SAM was introduced into the segmentation model to construct the Scale Attention Network (SANet). The multi-scale features extracted from the encoder were first re-extracted to obtain more specific multi-scale features. Then the SAM was applied to recalibrate the features. Specifically, for the feature of each scale, a summation of Global Average Pooling and Global Max Pooling was used to create scale-wise feature representations. According to the representations, a lightweight network was used to generate the importance score of each scale. The features were recalibrated based on the scores, and a simple pixel-by-pixel summation was used to fuse the multi-scale features. The fused multi-scale feature was fed into a segmentation head to complete the task. RESULTS The models were evaluated using fivefold cross-validation on 70 upper abdominal computed tomography scans of patients in a volume manner. The results showed that SANet could effectively alleviate the scale-variability problem and achieve better performance compared with UNet, Attention UNet, UNet++, Deeplabv3p, and CascadedUNet. The Dice similarity coefficients (DSCs) of colon, small intestine, and duodenum were (84.06 ± 3.66)%, (76.79 ± 5.12)%, and (61.68 ± 4.32)%, respectively. The HD95 were (7.51 ± 2.45) mm, (11.08 ± 2.45) mm, and (12.21 ± 1.95) mm, respectively. The values of relative volume difference were (3.4 ± 0.8)%, (11.6 ± 11.81)%, and (6.2 ± 3.71)%, respectively. The values of center-of-mass distance were 7.85 ± 2.82, 9.89 ± 2.70, and 9.94 ± 1.58, respectively. Compared with other attention modules and multi-scale feature exploitation approaches, SAM could obtain a 0.83-2.71 points improvement in terms of DSC with a comparable or even less number of parameters. The extensive experiments confirmed the effectiveness of SAM. CONCLUSIONS The SANet can efficiently exploit multi-scale features to alleviate the scale-variability problem and improve the segmentation performance on colon, small intestine, and duodenum of the upper abdomen.
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Affiliation(s)
- Wenbin Wu
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
| | - Runhong Lei
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Kai Niu
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Zhiqiang He
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
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Liu Y, Wen T, Sun W, Liu Z, Song X, He X, Zhang S, Wu Z. Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images. Sensors (Basel) 2022; 22:5666. [PMID: 35957222 PMCID: PMC9371218 DOI: 10.3390/s22155666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled 'black-box' by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.
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Affiliation(s)
- Yiwen Liu
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
| | - Tao Wen
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Wei Sun
- School of Computer Science, Neusoft Institute Guangdong, Foshan 528225, China;
| | - Zhenyu Liu
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xiaoying Song
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xuan He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China;
| | - Shuo Zhang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
| | - Zhenning Wu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
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刘 秀, 戚 帅, 熊 鹏, 刘 京, 王 洪, 杨 建. [An automatic pulmonary nodules detection algorithm with multi-scale information fusion]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020; 37:434-441. [PMID: 32597085 PMCID: PMC10319576 DOI: 10.7507/1001-5515.201910047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Indexed: 11/03/2022]
Abstract
Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.
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Affiliation(s)
- 秀玲 刘
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
- 河北省数字医疗工程重点实验室(河北保定 071002)Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China
| | - 帅帅 戚
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
| | - 鹏 熊
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
- 河北省数字医疗工程重点实验室(河北保定 071002)Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China
| | - 京 刘
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
| | - 洪瑞 王
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
- 河北省数字医疗工程重点实验室(河北保定 071002)Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China
| | - 建利 杨
- 河北大学 电子信息工程学院(河北保定 071002)College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China
- 河北省数字医疗工程重点实验室(河北保定 071002)Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei 071002, P.R.China
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Haq R, Berry SL, Deasy JO, Hunt M, Veeraraghavan H. Dynamic multiatlas selection-based consensus segmentation of head and neck structures from CT images. Med Phys 2019; 46:5612-5622. [PMID: 31587300 DOI: 10.1002/mp.13854] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 09/11/2019] [Accepted: 09/16/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Manual delineation of head and neck (H&N) organ-at-risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection-based approach for fast and reproducible segmentation. METHODS Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel-wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)-radiodensity and modality-independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan-Kettering Cancer Center dataset (N = 45) and an external dataset (N = 32) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank-sum tests. RESULTS Both SWV and GWV methods produced significantly better segmentation accuracy than BA (P < 0.001) and MV (P < 0.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV's accuracy exceeded GWV's for submandibular glands (DSC = 0.60 vs 0.52, P = 0.019). CONCLUSIONS The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas-based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel-wise consensus between atlases within OARs during manual review.
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Affiliation(s)
- Rabia Haq
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Sean L Berry
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Margie Hunt
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
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Wollstein R, Moritomo H, Akio I, Omokawa S. Scaphoid Motion of the Wrist with Scapho-trapezio-trapezoidal Osteoarthritis-A Pilot Study. Curr Rheumatol Rev 2019; 16:206-209. [PMID: 30644347 DOI: 10.2174/1573397115666190115125430] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Revised: 11/27/2018] [Accepted: 01/01/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The purpose of this study was to investigate scaphoid motion within the scapho-trapezio-trapezoidal (STT) joint during wrist motion in the presence of STT joint osteoarthritis (OA). METHODS We studied 11 wrists with STT OA and 5 normal wrists. Computed tomography (CT) images were acquired in five wrist positions (maximum active flexion, extension, radial deviation, ulnar deviation, and neutral position). The 3-dimensional surface models of the radius and scaphoid were constructed and the motion of scaphoid relative to the radius was calculated. RESULTS AND CONCLUSIONS During wrist flexion/extension motion, the scaphoid rotated mostly in the flexion/extension plane. The angle tended to be smaller in STT OA than in normal. During wrist radioulnar deviation, the scaphoid was in an extended position in neutral wrist in STT OA. The motion of scaphoid in STT OA was divided into two types: a rigid type and mobile type. The mobile type rotated closer to the flexion/extension plane than the rigid type. Taking into account scaphoid motion during wrist movement before surgery may provide better results in the treatment of STT OA.
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Affiliation(s)
- Ronit Wollstein
- Department of Rheumatology, University of Pittsburgh, Pittsburgh, United States
| | - Hisao Moritomo
- Yukioka Hospital Hand Center, Osaka Yukioka College of Health Science, Yukioka, Japan
| | - Iida Akio
- Department of Orthopedic Surgery, Hanna Central Hospital, Nara, Japan
| | - Shohei Omokawa
- Department of Hand Surgery, Nara Medical University, Nara, Japan
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Seal A, Bhattacharjee D, Nasipuri M, Rodríguez-Esparragón D, Menasalvas E, Gonzalo-Martin C. PET-CT image fusion using random forest and à-trous wavelet transform. Int J Numer Method Biomed Eng 2018; 34:e2933. [PMID: 29078042 DOI: 10.1002/cnm.2933] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 10/12/2017] [Indexed: 06/07/2023]
Abstract
New image fusion rules for multimodal medical images are proposed in this work. Image fusion rules are defined by random forest learning algorithm and a translation-invariant à-trous wavelet transform (AWT). The proposed method is threefold. First, source images are decomposed into approximation and detail coefficients using AWT. Second, random forest is used to choose pixels from the approximation and detail coefficients for forming the approximation and detail coefficients of the fused image. Lastly, inverse AWT is applied to reconstruct fused image. All experiments have been performed on 198 slices of both computed tomography and positron emission tomography images of a patient. A traditional fusion method based on Mallat wavelet transform has also been implemented on these slices. A new image fusion performance measure along with 4 existing measures has been presented, which helps to compare the performance of 2 pixel level fusion methods. The experimental results clearly indicate that the proposed method outperforms the traditional method in terms of visual and quantitative qualities and the new measure is meaningful.
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Affiliation(s)
- Ayan Seal
- Department of Computer Science and Engineering, PDPM IIITDM Jabalpur, Jabalpur, India
| | | | - Mita Nasipuri
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
| | | | - Ernestina Menasalvas
- Center for Biomedical Technology, Universidad Politecnica de Madrid, Madrid, Spain
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Nadealian Z, Nazari B, Sadri S, Momeni M. Detection of Pulmonary Nodules in Low-dose Computed Tomography Using Localized Active Contours and Shape Features. J Med Signals Sens 2017; 7:203-212. [PMID: 29204377 PMCID: PMC5691559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Pulmonary nodules are symptoms of lung cancer. The shape and size of these nodules are used to diagnose lung cancer in computed tomography (CT) images. In the early stages, nodules are very small, and radiologist has to refer to many CT images to diagnose the disease, causing operator mistakes. Image processing algorithms are used as an aid to detect and localize nodules. METHODS In this paper, a novel lung nodules detection scheme is proposed. First, in the preprocessing stage, our algorithm segments two lung lobes to increase processing speed and accuracy. Second, template-matching is applied to detect the suspicious nodule candidates, including both nodules and some blood vessels. Third, the suspicious nodule candidates are segmented by localized active contours. Finally, the false-positive errors produced by vessels are reduced using some two-/three-dimensional geometrical features in three steps. In these steps, the size, long and short diameters and sphericity are used to decrease the false-positive rate. RESULTS In the first step, some vessels that are parallel to CT cross-plane are identified. In the second step, oblique vessels are detected using shift of center of gravity in two successive slices. In step three, vessels vertical to CT cross-plane are identified. Using these steps, vessels are separated from nodules. Early Lung Cancer Action Project is used as a popular dataset in this work. CONCLUSIONS Our algorithm achieved a sensitivity of 90.1% and a specificity of 92.8%, quite acceptable in comparison to other related works.
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Affiliation(s)
- Zahra Nadealian
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran,Address for correspondence: Ms. Zahra Nadealian, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. E-mail:
| | - Behzad Nazari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Saeid Sadri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Mohammad Momeni
- Department of Radiology, Alzahra Hospital, Isfahan University of Medical Sciences, Isfahan 84156-83111, Iran
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