Computer Aided Detection System for
Microcalcifications
in Digital Mammograms
Hayat Mohamed1, Mai S. Mabrouk2*, Amr Sharawy3
1Biomedical Engineering, Cairo University, Giza, Egypt, yota_totos@hotmail.com
msm_eng@yahoo.com2* Biomedical Engineering, MUST University, 6th of October, Egypt,
3 Biomedical Engineering, Cairo University, Giza, Egypt,amrarsha@gmail.com
Abstract
Breast cancer continues to be a significant public health problem in the world. Early
detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is
considered the most reliable method in early detection of breast cancer. However, it is
difficult for radiologists to provide both accurate and uniform evaluation for the
enormous mammograms generated in widespread screening. Micro calcification clusters
(MCCs) and masses are the two most important signs for the breast cancer, and their
automated detection is very valuable for early breast cancer diagnosis. The main objective
is to discuss the computer-aided detection system that has been proposed to assist the
radiologists in detecting the specific abnormalities and improving the diagnostic accuracy
in making the diagnostic decisions by applying techniques splits into three-steps
procedure beginning with enhancement by using Histogram equalization(HE) and
Morphological. Enhancement, followed by segmentation based on Otsu's threshold the
region of interest for the identification of micro calcifications and mass lesions, and at last
classification stage, which classify between normal and micro- calcifications 'patterns and
then classify between benign and malignant micro- calcifications. In classification stage;
three methods were used, the voting K-Nearest Neighbor classifier (K-NN)with
prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction
accuracy of 83%, and Artificial Neural Network classifier(ANN) with prediction
accuracy of 77%.
Keywords: Micro calcifications (MCCs) Histogram equalization (HE), K-Nearest
Neighbor classifier (K-NN), Support Vector Machine (SVM), Artificial Neural Network
(ANN), Otsu's threshold.