Digital Image Processing Technique for Breast Cancer Detection and Analysis

Abstract

Biopsy is a time consuming process and requires a great deal of skill and experience. Digital image processing techniques can play an
important role in helping perform breast biopsies, especially of abnormal areas. In my research work, to understand the type of breast cancer cell and
attempt to analyses the histopathological slides with my proposed method to identify cancer parts just using simple technique of isolation of
insignificant portion of biopsy slide by color thresholding based segmentation. Many features used in the breast cancer detection and analysis of
histopathology image are inspired by clinical pathologists as important for diagnosis and characterization. A large majority of these features are
features of cell nuclei in biopsy image; as such, there is often the desire to segment the image into individual cell nuclei. In this paper, present an
analysis of the utility of color thresholding method for segmentation of cancer cell nuclei for classification of H&E stained histopathology image of
breast tissue. I am showing the cell level classification performance using these segmented nuclei in a benign versus malignant. Results indicate that
very good segmentation and classification accuracies can be achieved with color thresholding based segmentation of cancer cell nuclei. The
simplicity of algorithm is leads to less computational time. Thus, this approach is suitable for automated real time breast cancer detection and
analysis tool.

 

 

Keywords: Cancer, Biopsy, Surgical biopsy, Histopathological image, color thresholding, cell nuclei, color segmentation, Bi-color
monochrome image and Inverse image.