Automated mitosis detection based on eXclusive Independent Component Analysis

In this paper, we propose an approach for automated mitosis detection, which provides critical information during performing breast cancer prognosis. Essentially, the problem of mitotic detection involves irregular shape object classification. It is a very challenging task. In this paper, a novel algorithm, named eXclusive Independent Component Analysis (XICA) is proposed. The XICA is an extension of a generic ICA, but focusing the components of differences (called exclusive basis set) between two classes of training patterns rather than the major (independent) components. Based on the residuals obtained from the relative computing involving the exclusive basis set of the relative training patterns, the automated mitosis detection is performed. By computing the residual of the relative exclusive basis set, we are able to classify the given testing patterns. The proposed approach has been tested on a mitosis image set provided by a ICPR2012 contest. It contains 226 mitosis in 35 color images. It achieved accurate rate 100% in training patterns and 83.513% in testing patterns.