HyMaP: A hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images

作者：Adnan Mujahid Khan, Hesham Eldaly, Emma Simmons, Nasir Rajpoot · 发表于：Journal of Pathology Informatics · 年份：2013 · DOI：10.4103/2153-3539.109802 · 被引用次数：50 · 研究领域：AI in cancer detection、Medical Image Segmentation Techniques、Digital Imaging for Blood Diseases

BACKGROUND: Segmentation of areas containing tumor cells in standard H&E histopathology images of breast (and several other tissues) is a key task for computer-assisted assessment and grading of histopathology slides. Good segmentation of tumor regions is also vital for automated scoring of immunohistochemical stained slides to restrict the scoring or analysis to areas containing tumor cells only and avoid potentially misleading results from analysis of stromal regions. Furthermore, detection of mitotic cells is critical for calculating key measures such as mitotic index; a key criteria for grading several types of cancers including breast cancer. We show that tumor segmentation can allow detection and quantification of mitotic cells from the standard H&E slides with a high degree of accuracy without need for special stains, in turn making the whole process more cost-effective. METHOD: BASED ON THE TISSUE MORPHOLOGY, BREAST HISTOLOGY IMAGE CONTENTS CAN BE DIVIDED INTO FOUR REGIONS: Tumor, Hypocellular Stroma (HypoCS), Hypercellular Stroma (HyperCS), and tissue fat (Background). Background is removed during the preprocessing stage on the basis of color thresholding, while HypoCS and HyperCS regions are segmented by calculating features using magnitude and phase spectra in the frequency domain, respectively, and performing unsupervised segmentation on these features. RESULTS: All images in the database were hand segmented by two expert pathologists. The algorithms considered he...