An Approach Based on Fusion Concepts for Improving Brain Magnetic Resonance Images (MRIs) Segmentation

作者：Zanaty E A^*

来源：Journal of Medical Imaging and Health Informatics, 2013, 3(1): 30-37.

DOI：10.1166/jmihi.2013.1122

• Decision Fusion
• Fuzzy Clustering
• Seed Region Growing
• Performance Measure
• MRI Brain Database

In this paper, we present a segmentation approach based on fusing the data obtained from fuzzy k-means clustering (FKM), seed region growing, and average overlap metric (AOM) algorithms for improving MRIs segmentation. The source images are subdivided by FKM to get approximate centers of the detailed clusters. The detailed clusters are passed to seed region growing (SRG) method to isolate suitable closed regions. The seeds of region growing method are selected automatically as the output centers of the FKM method. Then, AOM method is used to classify the output regions of seed region growing method into groups according to similarity measure. Since the seed region growing produces crisp outputs while the largest group of fusion methods combines soft decisions, Gaussian membership function is used to convert the hard decisions to soft. The different fusion rules are applied to these groups to produce segments of points that label the similar membership values. The proposed algorithm is applied to challenging applications: MRI datasets, 3D simulated MRIs, and gray matter/white matter of brain segmentations. The experimental results show that the proposed technique produces accurate and stable results.

• 出版日期2013-3