Evolution of a fuzzy rule-based system for automatic chromosome recognition

One of the longest standing problems in medical image analysis is that of the automated recognition of chromosomes from images of a metaphase spread of a cell. This process of visualizing and categorizing the chromosomes within a cell, called karyotyping, is a key factor in many medical procedures. It is a labor-intensive activity, and hence, is a great candidate for automation. There are many sources of uncertainty in this problem domain, making a fuzzy logic-based approach a very appealing proposition. We describe the evolution of the fuzzy rule-base in an attempt to optimize its performance as an automatic chromosome classifier on a subset of the problem domain. A comparison to neural networks is included.