Accurate Detection of Distorted Pectoral Muscle in Mammograms Using Specific Patterned Isocontours

Abstract

Automatic detection of the pectoral muscle in mammograms is widely used in computeraided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study,we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.

A New Fat-Removal-Based Preprocessing Pipeline for MLO View in Digital Mammograms

“…The MLO projection includes the breast and the pectoral muscle because both structures are adjacent to each other [13]. Generally, the removal of the pectoral muscle is a necessary task because it contains high levels of intensity that can be easily confused by a classification method such as high density [21].…”

“…For example, variability in the type of muscle shape (e.g., linear, concave, mixed), low contrast in the lower area of the muscle, confusion of the muscle border with the axillary cavity, and variability in size and contrast [2]. All these variations arise due to, among other reasons, the muscle's anatomy, the position of the patients during image acquisition, and skin folds [13].…”

“…Some works concerning removing the pectoral muscle are based on edge detection techniques [1], such as the Canny algorithm [6], [15] and contour maps [13]. These methods obtain the border of the pectoral muscle, but some limitations are slight distortions in the edge and disjoint areas.…”

“…Typical MLO mammogram preprocessing techniques involve enhancing and delimiting breast tissue area and removing pectoral muscles [13], [14]. Several methods in the literature are focused on the segmentation process for breast and pectoral muscle delimitation [15].…”

Pectoral Muscle Removal in Digital Mammograms Using Region Based Standard Otsu Technique

“…Mammography uses a low dose of X-ray 0.4mSv (millisieverts or mSv is a unit of measure for radiation dose) [5], [9] to produce a mammogram (breast image) usually taken in two views which are: Cranio Caudal (CC) and Medio-Lateral Oblique (MLO). These views allow a degree of visualizing dense breasts to an extent, and the MLO view depicts more breast tissue and Pectoral Muscle (PM) area when compared with the CC view [14], [15]. Thus, mammograms in MLO projection are used for this study.…”