hidden markov random fields and cuckoo search method for medical image segmentation

2020/05/19 by El-Hachemi Guerrout, Ramdane Mahiou, Guerrout, EL-Hachemi +7 · 2 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Retrieval and Classification Techniques #Image and Object Detection Techniques #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2005.09377

openalex publication_date 2020/05/19 · openalex created_date 2020/05/29 · openalex updated_date 2026/07/28

Abstract

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.

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