Generalized nonconvex optimization for medical image segmentation

Design of a generalized technique for medical image segmentation is a challenging task. Currently a number of approaches are being investigated for 2-D and 3-D medical image segmentation for diagnostic and research applications. The methodology used in this work is aimed at obtaining a generalized solution of non-convex optimization problems by including a structural constraint of mass or density and the concept of additivity properties of entropy to a recently developed statistical approach to clustering and classification (6). The original computationally intensive procedure is made more efficient both in processing time and accuracy by employing a new similarity parameter for generating the initial clusters that are updated by minimizing an energy function relating the image entropy and expected distortion. The application of the computationally intensive yet generalized solution to nonconvex optimization to a limited set of medical images has resulted in excellent segmentation (8) when compared to other clustering based segmentation approaches 13 The addition of the parametric approach to determine the initial number of clusters allows significant reduction in processing time and better design of automated segmentation procedure. This research work generalizes a deterministic annealing (4) i.e. a specific statistical approach to solve nonconvex optimization problems by developing a more efficient technique applicable to medical image segmentation. Deterministic annealing (DA) is an extremely elegant and useful procedure for solving nonconvex optimization problems (getting trapped in local minima). However, the DA approach is extremely computationally intensive for applications such as image segmentation. The new integrated approach developed in this work allows this optimization technique to be used for medical image segmentation.