A comparative study of computerized approaches for type P63 ovarian tissues using histopathology digitized color images

Document Type

Conference Proceeding

Publication Title

Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2016, Game and Entertainment Technologies 2016 and Computer Graphics, Visualization, Computer Vision and Image Processing 2016

Publisher

International Association for Development of the Information Society

School

School of Science

RAS ID

23247

Comments

Sazzad, T.M., Armstrong, L., Tripathy, A.K. (2016). A comparative study of computerized approaches for type P63 ovarian tissues using histopathology digitized color images. In Proceedings of the International Conferences on Interfaces and Human Computer Interaction 2016, Game and Entertainment Technologies 2016 and Computer Graphics, Visualization, Computer Vision and Image Processing 2016. 187 - 194. Available here

Abstract

Computer based histopathology tissue analysis especially human ovarian reproductive tissue analysis is an important laboratory routine analysis for reproductive tissue identification. This allows experts to provide necessary treatment for women who face conceiving complications. Existing scanning modalities are not optimal to analyze human ovarian reproductive tissues as they mostly analyze grayscale images which do not provide satisfactory results. In this situation manual microscopic analysis is the best practice for the experts. The problem associated in manual analysis is this process is time consuming and inconsistent in experts experimental result. To minimize the labor cost, effort and time it is essential to design a computer based analysis approach which can automatically identify the ovarian reproductive tissues. In this paper analysis and comparison of existing fully automated approaches on type P63 ovarian tissue images using digitized histopathology images has been highlighted. This paper proposes a more suitable automated approach for type P63 digitized color images in comparison to manual microscopic identification approach based on accuracy. A comparison of various existing automated approaches with manual identification results by experts indicates excellent performance of the automated approach.

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