Extending case-based reasoning by discovering and using image features in IVF

Abstract

This paper describes the application of automated image analysis to evaluate morphology and developmental features of oocytes and embryos in the domain of in-vitro fertilization (IVF). Although humans can analyze images more flexibly, computer vision techniques make the process more objective and precise. We propose to use computer-based morphometry to precisely and objectively identify developmental features of oocytes and embryos. Extracted morphological information can be linked with symbolic information to better predict pregnancy outcome and suggest further medical procedures. Recognized features can then be used to support case-based reasoning and knowledge discovery. The combination of image analysis techniques and case-based reasoning can thus serve as: (1) a feature extraction technique; (2) an indexing approach; and (3) an analysis tool. A combination of symbolic and image information can then be used to identify morphological features of oocytes and embryos that are vital for successful IVF. Extracting image features and analyzing them helps to perform knowledge discovery from images. © 2000 ACM.