6533b7cefe1ef96bd1257899
RESEARCH PRODUCT
Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation
Dominique MichelucciEl-hachemi GuerroutSamy Ait-aoudiaRamdane Mahiousubject
Segmentation-based object categorizationbusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONScale-space segmentationImage processing02 engineering and technologyImage segmentationMachine learningcomputer.software_genreSørensen–Dice coefficient0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentationArtificial intelligenceHidden Markov random fieldHidden Markov modelbusinesscomputerMathematicsdescription
The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grounds truths images using the Kappa index called also Dice Coefficient (DC). The results show the supremacy of the methods used compared to others methods.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 | Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods |