6533b823fe1ef96bd127f851
RESEARCH PRODUCT
Conjugate Gradient Method for Brain Magnetic Resonance Images Segmentation
El-hachemi GuerroutDominique MichelucciRamdane MahiouSamy Ait-aoudiasubject
Ground truthComputer sciencebusiness.industryThe Conjugate Gradient algorithmComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONBrain image segmentationPattern recognition02 engineering and technologyImage segmentationImage (mathematics)Nonlinear conjugate gradient method03 medical and health sciences0302 clinical medicineDice Coefficient metricHidden Markov Random FieldConjugate gradient methodComputer Science::Computer Vision and Pattern Recognition0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingSegmentation[INFO]Computer Science [cs]Artificial intelligencebusinessHidden Markov random field030217 neurology & neurosurgerydescription
Part 8: Pattern Recognition and Image Processing; International audience; Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugate Gradient algorithm (CG) for image segmentation, in combination with the Hidden Markov Random Field modelization. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-05-08 |