Mammographic mass classification according to Bi‐RADS lexicon

Abstract

The goal of this study is to propose a computer-aided diagnosis system to differentiate between four breast imaging reporting and data system (Bi-RADS) classes in digitised mammograms. This system is inspired by the approach of the doctor during the radiologic examination as it was agreed in BI-RADS, where masses are described by their form, their boundary and their density. The segmentation of masses in the authors' approach is manual because it is supposed that the detection is already made. When the segmented region is available, the features extraction process can be carried out. 22 visual characteristics are automatically computed from shape, edge and textural properties; only one human feature is used in this study, which is the patient's age. Classification is finally done using a multi-layer perceptron according to two separate schemes; the first one consists of classify masses to distinguish between the four BI-RADS classes (2, 3, 4 and 5). In the second one the authors classify abnormalities on two classes (benign and malign). The proposed approach has been evaluated on 480 mammographic masses extracted from the digital database for screening mammography, and the obtained results are encouraging. This paper is organised as the following: Section 2 presents the practical background related to the BI-RADS assessment; in Section 3, we review related researches; in Section 4, we explain the features extraction methods investigated and classification schemes used in the present work; in Section 5, we show the

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

“…In addition to the mass size, the BI-RADS mammography [7] reported that the patient's age is necessary for the categorization of mammograms as it has the ability to change the assessment category of the lesion (downgrade or upgrade the BI-RADS category). This issue was also formerly discussed in the works of Lo et al [43], Gupta et al [5], and Chokri and Farida [18], in which the age has been found as an important discriminatory feature for the BI-RADS-based CAD systems. Table 1 provides a summary of the aforementioned quantified BI-RADS features.…”

“…However, the above-mentioned works focused on discriminating mammogram benign lesions from malignant or normal from abnormal, while few studies have reported the BI-RADS classification of breast masses using mammograms. The authors in [18] introduced an automatic CAD system to classify mammographic masses either as benign or as malignant or in four BI-RADS categories (B-2, B-3, B-4, and B-5). A set of 23 handcrafted features was extracted and fed into a multilayer perceptron for classification.…”

“…Due to the complex morphology of mass lesions that often appear with ambiguous and jagged contours and the nature of their surrounding fibroglandular breast tissue, the segmentation of masses is much more difficult compared with other findings such as calcification or architectural distortion. Several segmentation methods have been proposed in the literature to extract the precise contour of mammographic lesions which are roughly categorized into automatic [11,31,32], semiautomatic [33,34], and manually performed by the radiologist [18,35]. However, previous studies [36] have shown that there is no "one-fit-all" segmentation technique and none of the automatic and manual methods can provide fully accurate results for diverse mass lesions in mammograms.…”

“…Thus, we set the number of waveforms to 32, the waveform length to 64, and the angle θ for the sequential interval to place a new waveform along the margin as π/ 16. More details of this process can be found in [18,41]. To reduce the number of the extracted features, we used different statistical functions: mean, maximum, minimum, standard deviation, variance, skewness, and kurtosis for each of the three extracted features over 32 waveforms.…”

“…From a clinical point of view, this binary classification is not congruent and disagrees with radiologist assessments. Indeed, in the clinical practice, radiologists should primarily put each lesion into one of the above-mentioned BI-RADS assessment categories, because they cannot claim the malignancy or benignity of the detected abnormality without carrying out a follow-up study or biopsy [18]. In addition, the entire diagnostic process for classification of mass lesions using CAD systems usually involves several stages: preprocessing, segmentation, feature extraction, feature selection, and classification.…”