Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Abstract

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.

Comparison between two packages for pectoral muscle removal on mammographic images

“…As a matter of fact, important issues with deep models for pectoral removal are the robustness of the methods and the training phase. Before the advent of deep learning, feature-based methods dominated the field [34][35][36][37]. The robustness of these kinds of systems remains to be improved as variations in the images could lead to wrong removal.…”

Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

“…Gómez et al [14] proposed a region-growing method with seed and threshold methods. The initial seed value used was (10,10).…”

Wavelet-Based Automatic Pectoral Muscle Segmentation for Mammograms

“…Another limiting factor may be related to the assumption that the pectoral muscle region must be triangular. The similar pixel intensities from these two regions can be utilized in a region-growing approach in which a uniform intensity value (UIV) is calculated based upon the mean and standard deviation of pixel values, excluding those equal to zero, across the grayscale mammogram [27]. The UIV can then be used to fully segment the pectoral muscle region, in which curve fitting is included as the final step to insure the entire pectoral muscle region has been segmented.…”