A Novel Algorithm for Segmentation of Suspicious Microcalcification Regions on Mammograms

• Published in: Bioinformatics and Biomedical Engineering pp. 222 - 230
• Main Authors: Kurt, Burçin, Nabiyev, Vasif V., Turhan, Kemal
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: image enhancement; Mammography image; microcalcification; Otsu’s N thresholding; segmentation
• ISBN: 9783319164823; 3319164821
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16483-0_23

Microcalcifications can be defined as the earliest sign of breast cancer and the early detection is very important. However, detection process is difficult because of their small size. Computer-based systems can assist the radiologist to increase the diagnostic accuracy. In this paper, we presented an automatic suspicious microcalcification regions segmentation system which can be used as a preprocessing step for microcalcifications detection. Our proposed system includes two main steps; preprocessing and segmentation. In the first step, we have implemented mammography image enhancement using top-hat transform and breast region segmentation using 3x3 median filtering, morphological opening and connected component labeling (CCL) methods. In the second step, a novel algorithm has been improved for segmentation of suspicious microcalcification regions. In the proposed segmentation algorithm, first Otsu’s N=3 thresholding, then dilation process and CCL methods have been applied on preprocessed mammography image. After this process, we took the upper region from the biggest two regions and if the pixels number of the taken region was greater than the limit value, that means the upper region was the pectoral muscle region and should be removed from the image. The limit value was determined according to the database results and prevented the false region segmentation for mammography images which have no pectoral muscle region. Successful results have been obtained on MIAS database.