Title:	AN AUTOMATED OVARIAN TISSUE DETECTION APPROACH USING TYPE P63 COUNTER STAINED IMAGES TO MINIMIZE PATHOLOGY EXPERTS OBSERVATION VARIABILITY
Author(s):	T M Shahriar Sazzad, Leisa Armstrong and Amiya Kumar Tripathy
ISBN:	978-989-8533-66-1
Editors:	Yingcai Xiao and Ajith P. Abraham
Year:	2017
Edition:	Single
Keywords:	Histopathology, Color Digitized Microscopic Image, Image Artifacts, Mean Shift; Region Fusion, Cluster; Ovarian Reproductive Tissues
Type:	Full Paper
First Page:	124
Last Page:	130
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Pathology experts are more interested to work and analyze microscopic digitized color images in compare to other available image processing modalities especially electronic ultrasound scanners. Ultrasound has the capability to identify larger sized and more mature sized tissues in compare to smaller ones. Ovarian reproductive tissues are smaller in size in compare to other available tissues in the ovary for which it is hard for ultrasound scanners to analyze ovarian reproductive tissues. Microscopic digitized color images are more viable for which at present most appropriate approach to analyze ovarian reproductive tissues remains microscopic analysis process. Manual analysis process is costly, extensive analysis time requires with observation variations between experts. To improve accuracy and reduce processing time computer based approaches have become popular for the last two decades but mostly to analyze cancer cells. To analyze small ovarian reproductive tissues accurately type P63 counter stained images with 3 different magnifications a fully automated approach is presented in this paper. The study’s experimental results have higher accuracy rate in compare to manual analysis and electronic scanners.
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