Computer-aided detection of architectural distortion in prior mammograms of interval cancer

Context-Based Human Influence and Causal Responsibility for Assisted Decision-Making

OBJECTIVE

The impact of the context in which automation is introduced to a decision-making system was analyzed theoretically and empirically.

BACKGROUND

Previous work dealt with causality and responsibility in human-automation systems without considering the effects of how the automation's role is presented to users.

METHODS

An existing analytical model for predicting the human contribution to outcomes was adapted to accommodate the context of automation. An aided signal detection experiment with 400 participants was conducted to assess the correspondence of observed behavior to model predictions.

RESULTS

The context in which the automation's role is presented affected users' tendency to follow its advice. When automation made decisions, and users only supervised it, they tended to contribute less to the outcome than in systems where the automation had an advisory capacity. The adapted theoretical model for human contribution was generally aligned with participants' behavior.

CONCLUSION

The specific way automation is integrated into a system affects its use and the perceptions of user involvement, possibly altering overall system performance.

APPLICATION

The research can help design systems with automation-assisted decision-making and provide information on regulatory requirements and operational processes for such systems.

Efficacy of ultrasonography and mammography in detecting features of breast cancer

Introduction

Breast cancer (BC) is considered one of the most commonly diagnosed cancers. Early detection is critical for effective management. This study aims to assess the utility of ultrasonography (US) and mammography (MG) in detecting BC features.

Methods

This retrospective cross-sectional study involved the electronic records of 263 female patients diagnosed with BC. The mean age was 45.71 ± 12.25 years (17-90 years). A cross-tabulation test was performed to correlate the presence of each malignant feature (Yes/No) on both US and MG and the final ultrasonography diagnosis (benign/malignant). The compatibility between the presence of each feature on both imaging techniques was measured by the percentage of agreement in reporting the feature that was reported as Kappa. The sensitivity and specificity for each feature were calculated, and the receiver operating characteristic curve was used to measure the area under the curve for each feature on both modalities.

Results

The strong compatibility between the two techniques was 87.1%, 94.29%, 66.92%, 79.85%, 77.56%, 77.18, and 79.84% for irregular shape, uncircumscribed, spiculated margins, tissue distortion, nipple retraction, skin thickening, and the presence of lymphadenopathy, respectively (P < 0.001). Boxplots show that the sensitivity of the US ranged from 37% to 95%, and the specificity ranged from 27% to 91%. However, MG's sensitivity ranged from 44% to 93%, and the specificity ranged from 36% to 73%.

Conclusion

US and MG images show similar morphological changes, enhancing diagnostic accuracy in breast lesions. US characterizes echogenicity, provides real-time imaging, and uses color and pulsed Doppler techniques for vascularity and lymphadenopathy detection, while MG is better for identifying different calcification types.

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images

Simple Summary

Breast cancer diagnosis at an early stage using mammography is important, as it assists clinical specialists in treatment planning to increase survival rates. The aim of this study is to construct an effective method to classify breast images into four classes with a low error rate. Initially, unwanted regions of mammograms are removed, the quality is enhanced, and the cancerous lesions are highlighted with different artifacts removal, noise reduction, and enhancement techniques. The number of mammograms is increased using seven augmentation techniques to deal with over-fitting and under-fitting problems. Afterwards, six fine-tuned convolution neural networks (CNNs), originally developed for other purposes, are evaluated, and VGG16 yielded the highest performance. We propose a BreastNet18 model based on the fine-tuned VGG16, changing different hyper parameters and layer structures after experimentation with our dataset. Performing an ablation study on the proposed model and selecting suitable parameter values for preprocessing algorithms increases the accuracy of our model to 98.02%, outperforming some existing state-of-the-art approaches. To analyze the performance, several performance metrics are generated and evaluated for every model and for BreastNet18. Results suggest that accuracy improvement can be obtained through image pre-processing techniques, augmentation, and ablation study. To investigate possible overfitting issues, a k-fold cross validation is carried out. To assert the robustness of the network, the model is tested on a dataset containing noisy mammograms. This may help medical specialists in efficient and accurate diagnosis and early treatment planning.

Abstract

Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.

Screening through Temperature and Thermal Pattern Analysis in DMBA - Induced Breast Cancer in Wistar Rats

Background:

Based on thermal temperatures around the breast, thermography is considered a promising approache providing information about the condition of the breast without any side effects.

Objective:

Using thermography, breast screening is highly dependent on the process of heat recognition. The angular effects in the process of thermal patterns recognition can increase false detection. The effect can be observed in breasts with growing mammary glands. This study aims to develop a system to identify breast conditions through analysis of temperature and thermal patterns.

Material and Methods:

In this experimental study, analysis of thermal patterns are performed using the Canny method, specifically detection of anomalies in the breast. Twenty-four Wistar female rats were used as experimental animal models with group 1 (normal), group 2 (induced with DMBA), group 3 (rats with growing mammary gland). At the end of 8 weeks, all rats were sacrificed and histopathology analysis was performed. The body temperature was measured every week using the Infrared Camera type TiS20 brand Fluke camera.

Results:

Histopathology indicated average temperature of 36.66 °C, 37.77 °C and above 38.87 °C in normal, growing mammary glands, and cancerous breasts, respectively. These results revealed significantly higher heat in breasts with cancerous lesions. In the analysis of thermal pattern recognition for breast, no curve was formed in the normal group, while cancerous and growing mammary glands demonstrated a perfectly closed curve and an imperfect curve pattern, respectively.

Conclusion:

Breast screening through the analysis of temperature and thermal patterns can distinguish normal, cancerous and breast with growing mammary glands.

A behavioral training protocol using visual perceptual learning to improve a visual skill

We describe a behavioral training protocol using visual perceptual learning (VPL) to improve visual detection skills in non-experts for subtle mammographic lesions indicative of breast cancer. This protocol can be adapted for the professional training of experts (radiologists) or to improve visual skills for other tasks, such as the detection of targets in photo or video surveillance.

For complete details on the use and execution of this protocol, please refer to Frank et al. (2020a).

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Behavioral training using VPL induces long-lasting improvements of a visual skill

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Training should be conducted using detailed feedback about response accuracy

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Training can be conducted with minimal technical equipment

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VPL protocol can be used for clinical or other professional training

A Novel Fusion-Based Texture Descriptor to Improve the Detection of Architectural Distortion in Digital Mammography

Architectural distortion (AD) is the earliest sign of breast cancer that can be detected on a mammogram, and it is usually associated with malignant tumors. Breast cancer is one of the major causes of death among women, and the chance of cure can increase significantly when detected early. Computer-aided detection (CAD) systems have been used in clinical practice to assist radiologists with the task of detecting breast lesions. However, due to the complexity and subtlety of AD, its detection is still a challenge, even with the assistance of CAD. Recently, the fusion of descriptors has become a trend for improving the performance of computer vision algorithms. In this work, we evaluated some local texture descriptors and their possible combinations, considering different fusion approaches, for application in CAD systems to improve AD detection. In addition, we present a novel fusion-based texture descriptor, the Completed Mean Local Mapped Pattern (CMLMP), which is based on complementary information between three LMP operators (signal, magnitude and center) and the local differences between pixel values and the mean value of a neighborhood. We compared the performance of the proposed descriptor with two other well-known descriptors: the Completed Local Binary Pattern (CLBP) and the Completed Local Mapped Pattern (CLMP), for the task of detecting AD in 350 digital mammography clinical images. The results showed that the descriptor proposed in this work outperforms the others, for both individual and fused approaches. Moreover, the choice of the fusion operator is crucial because it results in different detection performances.

Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images

There have been long-standing debates regarding whether supervised or unsupervised learning mechanisms are involved in visual perceptual learning (VPL) [1-14]. However, these debates have been based on the effects of simple feedback only about response accuracy in detection or discrimination tasks of low-level visual features such as orientation [15-22]. Here, we examined whether the content of response feedback plays a critical role for the acquisition and long-term retention of VPL of complex natural images. We trained three groups of human subjects (n = 72 in total) to better detect "grouped microcalcifications" or "architectural distortion" lesions (referred to as calcification and distortion in the following) in mammograms either with no trial-by-trial feedback, partial trial-by-trial feedback (response correctness only), or detailed trial-by-trial feedback (response correctness and target location). Distortion lesions consist of more complex visual structures than calcification lesions [23-26]. We found that partial feedback is necessary for VPL of calcifications, whereas detailed feedback is required for VPL of distortions. Furthermore, detailed feedback during training is necessary for VPL of distortion and calcification lesions to be retained for 6 months. These results show that although supervised learning is heavily involved in VPL of complex natural images, the extent of supervision for VPL varies across different types of complex natural images. Such differential requirements for VPL to improve the detectability of lesions in mammograms are potentially informative for the professional training of radiologists.

The diagnostic value of MRI for architectural distortion categorized as BI-RADS category 3-4 by mammography

BACKGROUND

Architectural distortion is a common mammographic sign that can be benign or malignant. This study investigated the diagnostic value of magnetic resonance imaging (MRI) for architectural distortions that were category 3-4 under the breast imaging reporting and data system (BI-RADS) by mammography.

METHODS

We retrospectively analyzed 219 pathologically confirmed lesions in 208 patients who had BI-RADS category 3-4 architectural distortion in mammography images. Two radiologists described and categorized the architectural distortion and assigned the BI-RADS categories to the corresponding lesions on MRI images. Using the postoperative pathological diagnosis as the gold standard, we performed receiver operating characteristic (ROC) analysis for the efficacy of mammography and MRI in differentiating patients with benign or malignant lesions.

RESULTS

Totally 151 benign lesions and 68 malignant lesions were confirmed. According to the full-field digital mammography (FFDM), 82 lesions were in BI-RADS category 3, 104 lesions in 4A, 29 lesions in 4B, and 4 lesions in 4C. The positive predictive values of FFDM for BI-RADS categories 3, 4A, 4B, and 4C were 13.4% (11/82), 27.9% (29/104), 82.8% (24/29), and 100.0% (4/4), respectively. According to MRI, 59 lesions were in BI-RADS categories 1-2, 87 lesions in 3, 39 lesions in 4, and 34 lesions in 5, with their positive predictive values being 0.0% (0/58), 2.3% (2/87), 89.7% (35/39), and 100.0% (34/34), respectively. The area under the ROC curve (AUC) of breast benign and malignant lesions differentiated by FFDM was 0.647, and the diagnostic sensitivity, specificity, and Youden index were 86.3%, 41.7%, and 0.280, respectively. The AUC of FFDM combined with dynamic contrast-enhanced MRI (DCE-MRI) in differentiating breast benign vs. malignant lesions was 0.851, and the diagnostic sensitivity, specificity, and Youden index were 89.2%, 80.7%, and 0.699, respectively. The AUC of FFDM combined with DCE-MRI and the apparent diffusion coefficient (ADC) in differentiating benign vs. malignant lesions was 0.983, and the diagnostic sensitivity, specificity, and Youden index were 98.1%, 97.5%, and 0.956, respectively.

CONCLUSIONS

MRI can improve the diagnostic efficiency of mammography in diagnosing BI-RADS category 3-4 architectural distortions and can help in the qualitative diagnosis of architectural distortion lesions.

MRI in the differential diagnosis of primary architectural distortion detected by mammography

PURPOSE

We aimed to evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) values in lesions that manifest with architectural distortion (AD) on mammography.

METHODS

All full-field digital mammography (FFDM) images obtained between August 2010 and January 2013 were reviewed retrospectively, and 57 lesions showing AD were included in the study. Two independent radiologists reviewed all mammograms and MRI data and recorded lesion characteristics according to the BI-RADS lexicon. The gold standard was histopathologic results from biopsies or surgical excisions and results of the two-year follow-up. Receiver operating characteristic curve analysis was carried out to define the most effective threshold ADC value to differentiate malignant from benign breast lesions. We investigated the sensitivity and specificity of FFDM, DCE-MRI, FFDM+DCE-MRI, and DCE-MRI+ADC.

RESULTS

Of the 57 lesions analyzed, 28 were malignant and 29 were benign. The most effective threshold for the normalized ADC (nADC) was 0.61 with 93.1% sensitivity and 75.0% specificity. The sensitivity and specificity of DCE-MRI combined with nADC was 92.9% and 79.3%, respectively. DCE-MRI combined with nADC showed the highest specificity and equal sensitivity compared with other modalities, independent of the presentation of calcification.

CONCLUSION

DCE-MRI combined with nADC values was more reliable than mammography in differentiating the nature of disease manifesting as primary AD on mammography.

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Architecture distortion (AD) is an important and early sign of breast cancer, but due to its subtlety, it is often missed on the screening mammograms. The objective of this study is to create a quantitative approach for texture classification of AD based on various texture models, using support vector machine (SVM) classifier. The texture analysis has been done on the region of interest (ROI) selected from the original mammogram. A comprehensive analysis has been done on samples from three databases; out of which, two data sets are from the public domain, and the third data set is for clinical evaluation. The public domain databases are IRMA version of digital database for screening mammogram (DDSM) and Mammographic Image Analysis Society (MIAS). For clinical evaluation, the actual patient's database has been obtained from ACE Healthways, Diagnostic Centre Ludhiana, India. The significant finding of proposed study lies in appropriate selection of the size of ROIs. The experiments have been done on fixed size of ROIs as well as on the ground truth (variable size) ROIs. Best results pertain to an accuracy of 92.94 % obtained in case of DDSM database for fixed-size ROIs. In case of MIAS database, an accuracy of 95.34 % is achieved in AD versus non-AD (normal) cases for ground truth ROIs. Clinically, an accuracy of 88 % was achieved for ACE dataset. The results obtained in the present study are encouraging, as optimal result has been achieved for the proposed study in comparison with other related work in the same area.

Characterizing Architectural Distortion in Mammograms by Linear Saliency

Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A _z ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.

Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions

The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC  =  0.793  ±  0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.

In the digital era, architectural distortion remains a challenging radiological task

AIM

To compare readers' performance in detecting architectural distortion (AD) compared with other breast cancer types using digital mammography.

MATERIALS AND METHODS

Forty-one experienced breast screen readers (20 US and 21 Australian) were asked to read a single test set of 30 digitally acquired mammographic cases. Twenty cases had abnormal findings (10 with AD, 10 non-AD) and 10 cases were normal. Each reader was asked to locate and rate any abnormalities. Lesion and case-based performance was assessed. For each collection of readers (US; Australian; combined), jackknife free-response receiver operating characteristic (JAFROC), figure of merit (FOM), and inferred receiver operating characteristic (ROC), area under curve (Az) were calculated using JAFROC v.4.1 software. Readers' sensitivity, location sensitivity, JAFROC, FOM, ROC, Az scores were compared between cases groups using Wilcoxon's signed ranked test statistics.

RESULTS

For lesion-based analysis, significantly lower location sensitivity (p=0.001) was shown on AD cases compared with non-AD cases for all reader collections. The case-based analysis demonstrated significantly lower ROC Az values (p=0.02) for the first collection of readers, and lower sensitivity for the second collection of readers (p=0.04) and all-readers collection (p=0.008), for AD compared with non-AD cases.

CONCLUSIONS

The current work demonstrates that AD remains a challenging task for readers, even in the digital era.

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (p-rank) and z-normalizations were used. For the p-rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the z-normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list.

A novel image toggle tool for comparison of serial mammograms: automatic density normalization and alignment-development of the tool and initial experience

PURPOSE

The purpose is to develop a new image toggle tool with automatic density normalization (ADN) and automatic alignment (AA) for comparing serial digital mammograms (DMGs).

MATERIALS AND METHODS

We developed an ADN and AA process to compare the images of serial DMGs. In image density normalization, a linear interpolation was applied by taking two points of high- and low-brightness areas. The alignment was calculated by determining the point of the greatest correlation while shifting the alignment between the current and prior images. These processes were performed on a PC with a 3.20-GHz Xeon processor and 8 GB of main memory. We selected 12 suspected breast cancer patients who had undergone screening DMGs in the past. Automatic processing was retrospectively performed on these images. Two radiologists subjectively evaluated them.

RESULTS

The process of the developed algorithm took approximately 1 s per image. In our preliminary experience, two images could not be aligned approximately. When they were aligned, image toggling allowed detection of differences between examinations easily.

CONCLUSIONS

We developed a new tool to facilitate comparative reading of DMGs on a mammography viewing system. Using this tool for toggling comparisons might improve the interpretation efficiency of serial DMGs.

Detection of mammographically occult architectural distortion on digital breast tomosynthesis screening: initial clinical experience

OBJECTIVE

Digital breast tomosynthesis (DBT) has been shown to improve the sensitivity of screening mammography. DBT may have the most potential impact in cases of subtle mammographic findings such as architectural distortion (AD). The objective of our study was to determine whether DBT provides better visualization of AD than digital mammography (DM) and whether sensitivity for cancer detection is increased by the addition of DBT as it relates to cases of mammographically occult AD.

MATERIALS AND METHODS

Retrospective review of BI-RADS category 0 reports from 9982 screening DM examinations with adjunct DBT were searched for the term "architectural distortion" and were reviewed in consensus by three radiologists. ADs were classified by whether they were seen better on DM or DBT, were seen equally well on both, or were occult on either modality. The electronic medical record was reviewed to identify additional imaging studies, biopsy results, and surgical excision pathology results.

RESULTS

Review identified 26 cases of AD, 19 (73%) of which were seen only on the DBT images. Of the remaining seven ADs, six were seen better on DBT than DM. On diagnostic workup, nine lesions were assigned to BI-RADS category 4 or 5. Surgical pathology revealed two invasive carcinomas, two ductal carcinoma in situ lesions, three radial scars, and two lesions showing atypia. The cancer detection rate of DBT in mammographically occult AD was 21% (4/19). The positive predictive value of biopsy was 44%.

CONCLUSION

DBT provides better visualization of AD than DM and identifies a subset of ADs that are occult on DM. Identification of additional ADs on DBT increases the cancer detection rate.

Automatic detection of the nipple in screen-film and full-field digital mammograms using a novel Hessian-based method

Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.

Detection of architectural distortion in prior mammograms via analysis of oriented patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms

PURPOSE

We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms.

METHODS

The expected orientation of the breast tissue at each pixel was derived by using automatically detected landmarks including the breast boundary, the nipple, and the pectoral muscle (in mediolateral-oblique views). We hypothesize that the presence of architectural distortion changes the normal expected orientation of breast tissue patterns in a mammographic image. The angular deviation of the oriented structures in a given mammogram as compared to the expected orientation was analyzed to detect potential sites of architectural distortion using a measure of divergence of oriented patterns. Each potential site of architectural distortion was then characterized using measures of spicularity and angular dispersion specifically designed to represent spiculating patterns. The novel features for the characterization of spiculating patterns include an index of divergence of spicules computed from the intensity image and Gabor magnitude response using the Gabor angle response; radially weighted difference and angle-weighted difference (AWD) measures of the intensity, Gabor magnitude, and Gabor angle response; and AWD in the entropy of spicules computed from the intensity, Gabor magnitude, and Gabor angle response.

RESULTS

Using the newly proposed features with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, through feature selection and pattern classification with an artificial neural network, an area under the receiver operating characteristic curve of 0.75 was obtained. Free-response receiver operating characteristic analysis indicated a sensitivity of 0.80 at 5.3 false positives (FPs) per patient. Combining the features proposed in the present paper with others described in our previous works led to significant improvement with a sensitivity of 0.80 at 3.7 FPs per patient.

CONCLUSION

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, but the FP rate needs to be reduced.

Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms

PURPOSE

Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.

METHODS

Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon's entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.

RESULTS

Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.

CONCLUSION

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.

Detection of architectural distortion in prior mammograms

We present methods for the detection of sites of architectural distortion in prior mammograms of interval-cancer cases. We hypothesize that screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase portrait analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' measures, and Haralick's 14 features were computed. The areas under the receiver operating characteristic curves obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a single-layer feed-forward neural network. Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1 false positives per image, respectively, with the Bayesian classifier and the leave-one-image-out method.