Review paper on histopathological image analysis approach for automatic detection of glandular structures in human tissue

Abstract

In the last few decades, a dynamic growth within the range of analysis works conducted within the space of organ structure designation. This paper gives short reviews computer assisted histopathology image analysis for gland detection, segmentation and classification. The term Histopathology refers to the study of changes in biopsy sample taken by a pathologist under microscope. Main task of pathologist is to analyzing, locating and classifying most of the diseases, similarly appear at the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and syndrome in image. It is very important because the gland in human tissues is the area where cancer can be experiential. But this process is too time consuming and lead to intra and inter observer variability. To remove this drawback automatic detection of images is needed for quantitative diagnosis of Tissue. In this paper we have consolidated such recent techniques and its unique features. The survey done provides different approaches for detecting glands and the parameters considered for same. Discussion is also made on various Databases used for detection of glandular structure.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

“…Apart from complex components of the tubule, there are other lumen-like structures, such as adipose tissue or cavity gap in tissue slice, which makes tubule segmentation extremely challenging. 83 Several algorithms have been proposed to segment tubules in BC H&E images. However, a morphological operation that proposed earlier time 84 showed false-positive results.…”