Information theoretic clustering for medical image segmentation

Analysis of medical images involves robust computational approaches to various optimization problems prior to interpretation of the embedded pathological changes. Major computational efforts are essential in unsupervised learning of structures in various medical images. Some unsupervised learning techniques that take advantage of information theory concepts provide a different perspective on the solution of automated learning methods. This chapter will review a recent approach to clustering examined under an information theoretic framework that efficiently finds a suitable number of clusters representing different tissue characteristics in a medical image. The proposed clustering approach optimizes an objective function which quantifies the quality of particular cluster configurations. Recent developments involving interesting relationships between spectral clustering (SC) and kernel principal component analysis (kPCA) are used in this technique to include the nonlinear domain. In this novel SC approach, the data is mapped to a new space where the points belonging to the same cluster are collinear if the parameters of a radial basis function (RBF) kernel are adequately selected. The effectiveness of this nonlinear approach is demonstrated in the segmentation of uterine cervix color images for early identification of cervical neoplasia, as an aid to cervical cancer diagnosis. The limitations of this method in the segmentation of specific medical images such as brain images with multiple sclerosis lesions and a strategy to overcome them are discussed.