Fuzzy framework for joint segmentation and registration of brain MRI with prior information

El-Melegy, Moumen T.; Mokhtar, Hashim

doi:10.1109/icces.2010.5674904

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…There has been a lot of research done on the FCM method [30], [31], [32], [8], [1], [33], [34], [35]. The brain images of MRI need a computer analysis for the precise delineation of the tumors and then the reproducible method of image segmentation process.…”

Section: F Fuzzy C-means (Fcm)mentioning

confidence: 99%

A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Nilakant

Menon

Vikram

2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper presents a survey of advanced methods for segmenting the MRI (Magnetic Resonance Imaging) image of the brain. Segmentation of the brain is a challenging task because it requires more emphasized methods to differentiate each of the regions present in the brain image. The intensity differences between the different regions in the brain MRI image are very less, making it difficult to automate the entire segmentation process. Hence, a thorough understanding of the existing segmentation algorithm is essential for accurate segmentation. The segmentation algorithms surveyed in this work are Neural Network Model, Self-Organizing Maps, Radial Basis Function, Back Propagation, Fuzzy C-Means, Deformable Models, Level Set Models, Genetic Algorithm, Differential Evolutionary Algorithm, Hybrid Clustering and Artificial Intelligence. Such a survey would be helpful for researchers working in the field of brain image segmentation. The paper discusses the complexities in the segmentation algorithm and also the challenges in segmenting the brain MRI images. The segmentation outputs and analysis of the existing literature has also been discussed. The major criteria and their advantages in the segmentation of each algorithm have been reported accordingly in the observations.

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