A comprehensive analysis: Automated ovarian tissue detection using type P63 pathology color images

Document Type

Conference Proceeding

Publication Title

Machine Learning and Data Mining in Pattern Recognition

Publisher

Springer Verlag

School

School of Computer and Security Science

RAS ID

23348

Comments

Sazzad, T. S., Armstrong, L. J., & Tripathy, A. K. (2016). A Comprehensive Analysis: Automated Ovarian Tissue Detection Using Type P63 Pathology Color Images. In Machine Learning and Data Mining in Pattern Recognition (pp. 714-727). Springer. Available here

Abstract

Manual microscopic ovarian reproductive tissue analysis is a general routine examination process in the laboratory. This process requires longer processing time and prone to errors. Among all existing scanning devices ultrasound is commonly used but not optimal as it process grayscale images which do not provide satisfactory results. Computer based approaches could be a viable option as it can minimize the labor cost, effort and time. Additionally smaller tissues can be easily analyzed. In this paper a comprehensive analysis has been carried out and a new modified approach has been presented using type P63 histopathology ovarian tissues color images with different magnifications. Comparison of various existing automated approaches with manual identification results by experts indicates excellent performance of the proposed automated approach. © Springer International Publishing Switzerland 2016.

DOI

10.1007/978-3-319-41920-6_54

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