Abstract
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 extracted 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 for 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.
Highlights
Breast cancer is the most invasive and deadliest cancer in women worldwide
It outperforms the state-of-the-art counterparts and achieves the best current performance of Breast Imaging Reporting and Data System (BI-RADS) breast mass classification in mammography (ii) In order to ensure more accurate segmentation results, we introduce a semiautomatic segmentation method based on the region growing technique to separate mass lesions from surrounding breast tissues (iii) To improve the classification performance, we propose a modified genetic algorithm (GA)-based feature selection method where we first look for the best feature subset from each number of features we explore; the optimal subset among them is deduced for the classification
We have proposed a new and effective computer-aided diagnosis (CAD) system to classify mammogram masses into four BI-RADS categories (B2, B-3, B-4, and B-5), which can support the radiologists’ diagnosis
Summary
Breast cancer is the most invasive and deadliest cancer in women worldwide. Recent statistical reports from the International Agency for Research on Cancer reported as many as 8.2 million deaths from cancer worldwide in 2012, and breast cancer ranked second after lung cancer with an incidence rate of 1.67 million [1]. A broad range of CAD systems have been proposed and achieved remarkable performance to predict breast cancer from mammography images [9,10,11,12,13,14,15,16,17] Most of these works focused on the classification of the detected breast abnormalities as either benign or malignant (i.e., pathology classes). It outperforms the state-of-the-art counterparts and achieves the best current performance of BI-RADS breast mass classification in mammography (ii) In order to ensure more accurate segmentation results, we introduce a semiautomatic segmentation method based on the region growing technique to separate mass lesions from surrounding breast tissues (iii) To improve the classification performance, we propose a modified GA-based feature selection method where we first look for the best feature subset from each number of features we explore; the optimal subset among them is deduced for the classification.
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