An Automatic Mass Detection System in Mammograms Based on Complex Texture Features

Abstract

It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss approximately 10-30% of tumors because of the ambiguous margins of lesions and visual fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer. This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection. This system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study also proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify abnormal regions by selecting and rating the individual performance of each feature. Results show that the proposed system achieves satisfactory detection performance.

Microcalcification and Macrocalcification Detection in Mammograms Based on GLCM and ODCM Texture Features Using SVM Classifier

“…The Otsu thresholding method is applied to the digital mammogram to find the foreground of concern, which contains a breast region in most mediolateral oblique (MLO) views of mammograms [9]. The otsu thresholding is applied to the mammogram input image to separate the breast region from the background.…”

“…Therefore, some pattern recognition methods use a gray level co-occurrence matrix (GLCM) to extract characteristics [9], [14]. Statistical distributions of observed combinations of intensities at specified positions relative to each other in an image are used to obtain statistical textural features.…”

“…is to describe textures by a matrix of pair gray level appearing probabilities. The fourteen texture features are defined as follows: Entropy, Energy, Local homogeneous, Contrast, Intensity, Correlation, Inverse difference moment, Sum average, Sum of squares variance, Sum entropy, Difference entropy, Inertia, Cluster Shade, and Cluster Prominence [9]. GLCM texture considers the relation between two pixels at a time, called the reference and the neighbour pixel.…”

“…This type of complex texture feature extraction method extracts the information of local intensity relation and discrete photometric distribution. The proposed scheme computes four co-occurrence matrices with one pixel distance in four directions: left diagonal, right diagonal, vertical, and horizontal [9]. Another complex feature extraction method is also constructed that is similar to the proposed complex module, but translates the gray level co-occurrence matrix into the optical density cooccurrence matrix (ODCM) to characterize the photometric textures [14].…”

“…The optical density cooccurance matrix is a co-occurrence matrix of the optical density image. An optical density image can be obtained by converting the intensity of the gray level image into optical density image and linearly mapping all the optical density values one by one to an image that has an 8-bit depth information [9]. The minimum optical density value was mapped to 0, and the maximum optical density value was mapped to 255.…”