Mass detection and false positive reduction in mammographic images

University of Technology Sydney. Faculty of Engineering and Information Technology.Breast cancer is the most common type of cancer for women in America. Currently the most effective method for early detection of breast cancer is mammography. Mammography is the only widely accepted imaging method used for routine breast cancer screening. Masses are one of the important signs of breast cancer. However it is difficult to detect masses because masses have different size and shape and their features can be obscured or similar to the normal breast parenchyma. Reading mammograms is a demanding job for radiologists. A computer aided detection (CAD) system can provide a consistent second opinion to a radiologist and greatly improve the mass detection accuracy. In this thesis, a computer aided detection system is developed which can segment the breast region from the background in the whole mammographic image, detect the suspicious regions from the breast region and then classify the suspicious regions to mass or normal breast tissue. The suspicious regions in the full mammographic image can be found by contrast limited adaptive histogram equalization and thresholding. These suspicious regions can be masses or normal breast tissue (false positives). To reduce the number of false positives in mass detection, a feature selection and classification approach using particle swarm optimization (PSO) and support vector machine (SVM) is proposed. Firstly, texture features are derived from the gray level co-occurrence matrix (GLCM) of each suspicious region. A PSO and SVM based feature selection is proposed to determine the significant features. The significant features found by PSO-SVM based feature selection are used by the SVM classifier to classify the suspicious region to mass or normal breast tissue. One advantage of the proposed mass detection system is that it can detect different types of masses, including spiculated, circumscribed and ill-defined masses from the whole mammographic image. The number of false positives in mass detection can be reduced by the PSO and SVM based feature selection and mass classification method proposed in this thesis. Experimental results show that the proposed PSO-SVM based feature selection technique can find the significant features that can improve the classification accuracy of SVM and perform better than other widely used feature selection methods. The proposed mass classification approach using PSO and SVM has better or comparable performance when compared to other state-of-the-art mass classification techniques, using sensitivity and specificity as the evaluation criteria. In order to perform accurate image segmentation of the mass from the suspicious region, a mass segmentation method by PSO based image clustering is proposed. Two new fitness functions are proposed which can improve the performance of image clustering by generating more compact clusters and larger inter-cluster distance. The proposed PSO based image clustering, with the new fitness function, can improve the segmentation of the mass from mammographic image. It has been shown experimentally that PSO based image clustering can have better mass segmentation performance when compared to K-means, a widely used clustering technique

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extrcated from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database of screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories

An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network

In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Three layers artificial neural network (ANN) with seven features was proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologist's sensitivity 75%.Comment: 13 pages, 10 figure

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