Enhancing Mammogram Images with Segmentation and Colorization for Assisting Breast Cancer Detection
Tien Pham, William
Tat Pham, Trung
Illescas Maldonado, Pamela
This paper presents a combined sequence of the K-mean clustering of mammogram images to identify the region of interest and false colorization of the region of interest to enhance digital mammograms in the context of assisting the analysis for the detection of breast cancer. The K-means clustering technique was selected for its computational efficiency and for its not requiring prior knowledge of the statistical nature of the data. The region of interest is selected among the resulting clusters so that it can be colorized in a magnifying manner along a digitally simulated visible spectrum to enhance visualization of details that is so important for medical experts in their detection of breast cancer. Numerical results are presented in the study as the proof of concept, and demonstration of workability, applicability, and practicality of the newly developed sequential hybrid technique of image enhancement.