An automated detection process to detect ovarian tissues using type P63 digitized color images
IEEE Computer Society
School of Computer and Security Science
Dramatic improvements have been made in the field of digital image processing especially for biomedical image analysis over the past decade. With the availability of modern digital scanners, histopathology slides can be easily stored in digitized color image format. Therefore, histopathology digitized images have become a popular data source for both computer vision and machine learning techniques. There are several computer aided algorithms currently available to assist pathology experts to carry out their routine examination for detecting various tissues such as ovarian cancer cells and ovarian reproductive tissues. Automated detection of ovarian reproductive tissues is one of the important diagnosis interests for pathologists these days. One of the popular diagnosis preferences to identify ovarian tissues is ultrasound scanner. However, due to different shape, size and color, identification of ovarian tissues is a challenging task for ultrasound scanners as it process gray scale images. At present, pathological microscopic manual analysis is considered the best laboratory analysis practice for ovarian tissue cells although it is time-consuming, laborious and prone to errors. An alternate option would be to analyze these ovarian tissues automatically using color digitized images acquired from microscopic slides. In this paper a fully automated detection approach for color digitized image acquired from microscopic slides is presented and analyzed. The proposed method was found to be faster in comparison to other approaches. The approach also is beneficial as experts will not need to tune processing parameters for new batches of images. Experimental results from an analysis of the proposed approach using batch processing of a large number of images indicated high degree of accuracy and performance compared to the manual microscopic analysis. © 2015 IEEE.
Not open access