hidden markov random fields and cuckoo search method for medical image segmentation
This work addresses the need for automatic and robust segmentation in medical diagnostics, though it appears incremental as it applies an existing method to a new domain.
The paper tackled medical image segmentation by combining Hidden Markov Random Fields with the Cuckoo Search algorithm, achieving results that demonstrate efficiency in optimization for this task.
Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.