Agglomerated feature extractionin medical images for breast cancer and its characteristic pattern generation
Jucheol Moon, Sung Y. Shin, Donghoon Kang, Soon‐Ik Jeon, Hyung Do Choi, Jung Y. Kim · 2011
About 1 in 8 women in the United States is expected to develop breast cancer over the course of herentire lifetime but a few medical imaging techniques have been applied for breast cancer screening. In addition, the feature extraction and comparison in medical images for breast cancer detection haverarely been reported in literature. We propose a new framework toextract agglomerated features in medical imagesand comparethem by relating original characteristic patterns thereof. Our method concentrates on three key aspects and they are: a comparison between intensity distributions of pixels collected by the hexagonal mask, detecting minimum gradient points in a radial intensity series, and generatinga characteristic pattern of the feature. The main contribution of ourproposed approach is improving a method of identifying features which is lesssensitive to noise in medical images for breast cancerdetectionand presenting an original design of relating features which is consistent to the orientation and size of the feature. Experimental results demonstrate that our proposed approach is more tolerant of image noise than prior research and generates an invariant characteristic pattern of various orientations and sizes.