# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 3 Experimental Results
### Results  
This section presents the results on the OASIS and IBSR18 datasets. The performance of the proposed method is compared with state-of-the-art methods. In addition, the performance is also compared with the latest version of standard brain segmentation tools such as FSL[38], SPM12 [39] and FreeSurfer [40]. The following methods are considered for comparison:  
* HMRF-EM [8]: This method combines hidden Markov random field (HMRF) with an EM algorithm for MRI image segmentation. The main advantage of this method is it derives how the spatial information is encoded through the mutual influences of neighboring sites.
* FAST-PVE [41]: This method uses Markov random field(MRF) for brain tissue segmentation. To increase the speed, this method uses fast iterated conditional modes to solve MRFs.
* MSSEG [42]: This method deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps.
* R-FCM [43]: This method models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated.
* SFCM [44]: This method generalizes the objective function of a conventional FCM by incorporating a spatial penalty on the membership function.
* FANTASM [45]: This method is the extension of an adaptive FCM. In this method, an additional constraint is placed on the membership functions that force them to be spatially smooth.
* PVC [31]: This method uses a partial volume model for MRI brain tissue segmentation. First, it classifies nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. Further, the local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier.
* SPMS [46]: This method is based on a mixture of Gaussians. In addition, it is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps.
* GAMIXTURE [47]: This method employs finite mixture models (FMMs) for brain tissue segmentation. To deal with FMM complex optimization, this method employs a global optimization algorithm that uses blended crossover and a new permutation operator.
* ANN [48]: This method is based on a self-organizing map (SOM). Initially, the feature vector is extracted from the intensity of the pixel and its n nearest neighbors. Further, to improve the robustness, statistical transformation is applied to the extracted feature vector. Finally, each pixel is classified using SOM.
* KNN [49]: This method uses K-NN for brain tissue segmentation.
* BrainSuit09 [50]: This is an automatic brain image analysis tool. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentation in clinical time.
* SVPASEG [51]: This method uses local image models for brain tissue segmentation. This model combines the local models for tissue intensities and Markov Random Field (MRF) into a global probabilistic image model. Finally, the parameters for the local intensity models are obtained without supervision by maximizing the weighted likelihood of a certain finite mixture model.
* EGC-SOM [52]: This method uses self-organizing maps (SOM) for brain tissue segmentation. Initially, first- and second-order features are extracted using overlappingwindows. Further, evolutionary computing is used for feature selection. Finally, map units are grouped using SOM.
* RF-CRF [53]: This method uses a conditional random field with a random forest for brain tissue segmentation. This method uses intensities, gradients, probability maps, and locations as features.