Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods

Fuzzy Least Squares Twin Support Vector Machines

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM

Construcción de una base de datos de imágenes de mamografía para la identificación de microcalcificaciones

La mamografía es el tipo de imagen más utilizado en la deteccción de cáncer de mama, sin embargo, se caracteriza por tener un bajo contraste y un alto contenido de información no deseada. La detección de microcalcificaciones (mcals) en una mamografía es una tarea díficil aún para un especialista experimentado. Actualmente, la comunidad académica no cuenta con un banco de imágenes mamográficas que cuantifiquen la población local (Latinoamérica, Colombia) y además que permitan la validación del diagnóstico médico confirmado con biopsia, mediante algoritmos de procesamiento de imágenes. En este documento se presenta una metodología para la creación de una base de datos de registros mamográficos de una población local para la detección de mcals. El proceso se divide en dos etapas principales: en la primera etapa, se realiza la construcción de la base de datos, se analizan las fases de almacenamiento, validación médica y etiquetado de las imágenes. En la segunda etapa se realiza el procesamiento de las mamografías para la validación del reporte médico con biopsia confirmada. El proceso inicia con una etapa de preprocesamiento para eliminar artefactos derivados de la adquisición de las imágenes, seguido de un análisis de textura mediante técnicas de análisis de textura fractal (SFTA) y patrones locales binarios (LBP). Finalmente se realiza una clasificación basado en máquinas de aprendizaje para la identificación de los hallazgos reportados por el personal médico. Los resultados evidencian que la base de datos construida, cumple con los parámetros epidemiológicos para representar una población local, y la metodología de identificación de micro-calcificaciones basada en descriptores de textura evidencia porcentajes de acierto del 93;2% lo cual permite la correcta validación de los hallazgos patológicos de la base de datos recopilada.Breast cancer is a disease of great global impact and its the most common type of cancer affecting womens. This disease its the second death cause in the world. The incidence of this pathology has grown in recent years. Moreover, this is reflected in Colombia, which has passed from fifth to second in rank frequency. For early detection of this cancer, the only effective method is mammography. In this exam is possible to detect microcalcifications (mcals), which are the earliest manifestation of breast cancer [3]. However, the diffeerence between malignant and benign lesions represents a very complex problem, even for an experienced radiologist. Because the characteristics of such images (low contrast and sharpness) features make sometimes necessary to apperal to a second diagnostic or invasive tests. Besides this pathology, resulting in increased costs of analysis and episodes of unnecessary stress in patients [4] , [5]. For these reasons, is important to build mamographic databases from patient records in a local area, which allow us, a medical validation of biopsy diagnosis using imaging processing techniques. In this work we proposed a methodology for mamographic imaging database building, which allow us quantify, the variability of microcalcification in a local poblationa and brings to the reasearch community an open records to the medical image analysis. The construction of the database was conducted in four stages: First authorization usage of medical records was collect. Second, the acquisition and storage of this data was described. Here, the analysis and validation of the information contained in the medical report was accomplish labeling all types or microcalcification. Finally, we introduce a microcalcification recognition method based on texture descriptors for medical image processing, to automatically detect an abnormality in a breast image given. The results shows that the proposed model can efficiently detect a microcalcification with 93;2% accuracy. This brings to the medical community an open source to robustly perform a microcalcification recognition

Automatic BIRAD scoring of breast cancer mammograms

A computer aided diagnosis system (CAD) is developed to fully characterize and classify mass to benign and malignancy and to predict BIRAD (Breast Imaging Reporting and Data system) scores using mammographic image data. The CAD includes a preprocessing step to de-noise mammograms. This is followed by an active counter segmentation to deforms an initial curve, annotated by a radiologist, to separate and define the boundary of a mass from background. A feature extraction scheme wasthen used to fully characterize a mass by extraction of the most relevant features that have a large impact on the outcome of a patient biopsy. For this thirty-five medical and mathematical features based on intensity, shape and texture associated to the mass were extracted. Several feature selection schemes were then applied to select the most dominant features for use in next step, classification. Finally, a hierarchical classification schemes were applied on those subset of features to firstly classify mass to benign (mass with BIRAD score 2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c). Accuracy of segmentation performance were evaluated by calculating the degree of overlapping between the active counter segmentation and the manual segmentation, and the result was 98.5%. Also reproducibility of active counter 3 using different manual initialization of algorithm by three radiologists were assessed and result was 99.5%. Classification performance was evaluated using one hundred sixty masses (80 masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result for classification of data to benign and malignance was found using a combination of sequential forward floating feature (SFFS) selection and a boosted tree hybrid classifier with Ada boost ensemble method, decision tree learner type and 100 learners’ regression tree classifier, achieving 100% sensitivity and specificity in hold out method, 99.4% in cross validation method and 98.62 % average accuracy in cross validation method. For further sub classification of eighty malignance data with BIRAD score of over 4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with BIRAD score 4c), the best result achieved using the boosted tree with ensemble method bag, decision tree learner type with 200 learners Classification, achieving 100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41% average accuracy for ten times run in cross validation method. Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score 3 were gathered. Which means patient is recommended to be tested in another medical imaging technique and also is recommended to do follow-up in six months. The CAD system was trained with mass with BIRAD score 2 and over 4 also 4 it was further tested using 13 masses with a BIRAD score of 3 and the CAD results are shown to agree with the radiologist’s classification after confirming in six months follow up. The present results demonstrate high sensitivity and specificity of the proposed CAD system compared to prior research. The present research is therefore intended to make contributions to the field by proposing a novel CAD system, consists of series of well-selected image processing algorithms, to firstly classify mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews