Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Automatic Breast Density Classification on Tomosynthesis Images

Breast cancer (BC) is the type of cancer that most greatly affects women globally hence its early detection is essential to guarantee an effective treatment. Although digital mammography (DM) is the main method of BC detection, it has low sensitivity with about 30% of positive cases undetected due to the superimposition of breast tissue when crossed by the X-ray beam. Digital breast tomosynthesis (DBT) does not share this limi tation, allowing the visualization of individual breast slices due to its image acquisition system. Consecutively, DBT was the object of this study as a means of determining one of the main risk factors for BC: breast density (BD). This thesis was aimed at developing an algorithm that, taking advantage of the 3D nature of DBT images, automatically clas sifies them in terms of BD. Thus, a quantitative, objective and reproducible classification was obtained, which will contribute to ascertain the risk of BC. The algorithm was developed in MATLAB and later transferred to a user interface that was compiled into an executable application. Using 350 images from the VICTRE database for the first classification phase – group 1 (ACR1+ACR2) versus group 2 (ACR3+ACR4), the highest AUC value of 0,9797 was obtained. In the classification within groups 1 and 2, the AUC obtained was 0,7461 and 0,6736, respectively. The algorithm attained an accuracy of 82% for these images. Sixteen exams provided by Hospital da Luz were also evaluated, with an overall accuracy of 62,5%. Therefore, a user-friendly and intuitive application was created that prioritizes the use of DBT as a diagnostic method and allows an objective classification of BD. This study is a first step towards preparing medical institutions for the compulsoriness of assessing BD, at a time when BC is still a very present pathology that shortens the lives of thousands of people

Mammographic Ellispe Modelling for Risk Estimation

AbstractIt has been shown that breast density and parenchymal patterns are significant indicators in mammographic risk assessment. In addition, studies have shown that the sensitivity of computer aided tools decreases significantly with increase in breast density. As such, mammographic density estimation and classification plays an important role in CAD systems. In this paper, we present the classification of mammographic images according to breast parenchymal structures through a multi-scale ellipse blob detection technique. Our classification is based on classifying the mammographic images of the MIAS dataset into high/low risk mammograms based on features extracted from a blob detection technique which is based on breast tissue structure. In addition, it evaluates the relation between the BIRADS classes and low/high risk mammograms. Results demonstrate the probability of estimating breast density using computer vision techniques to improve classification of mammographic images as low/high risk

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models