Detection of Cancer from Microscopic Biopsy Images Using Image Processing Tools

Abstract

Presently, most cancer diagnosis is based on human visual examination of images in a qualitative manner. Human visual grading for microscopic biopsy images is very time-consuming, subjective, and inconsistent due to inter-and intra-observer variations. A more quantitative and reproducible approach for analyzing biopsy images is highly desired. In biopsy images, the characteristics of nuclei are the key to estimate the degree of malignancy. The microscopic biopsy images always suffer from the problem of impurities, undesirable elements, and uneven exposure. Thus, there is a need of an automatic cancer diagnosis system based on microscopic biopsy images using image-processing tools. Therefore, the cancer and its type will be detected in a very early stage for complete treatment and cure. This system helps pathologists to improve the accuracy and efficiency in detection of malignancy and to minimize the inter observer variation. In addition, the method may help physicians to analyze the image cell by using classification and clustering algorithms by staining characteristics of the cells. The various image-processing steps involved for cancer detection from biopsy images include acquisition, enhancement, segmentation, feature extraction, image representation, classification, and decision-making. With the help of image, processing tools the sizes of cells, nuclei, and cytoplasm as well as the mean distance between two nearest neighboring nuclei are estimated by the system.