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Texture Weighted Fuzzy C-Means Algorithm for 3D Brain MRI Segmentation
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- Authors
- Ji Young Lee, Dongyoun Kim, Jin Yeong Mun, Seok Kang, Seong Ho Son, Sung Shin
- Issue Date
- 2018-10
- Citation
- Research in Adaptive and Convergent Systems (RACS) 2018, pp.291-295
- Language
- English
- Type
- Conference Paper
- Abstract
- The segmentation of human brain Magnetic Resonance Image (MRI) is an essential component in the computer-aided medical image processing research. Fuzzy C-Means (FCM) algorithm is one of the practical algorithms for brain MRI segmentation. However, Intensity Non-Uniformity (INU) problem in brain MRI is still challenging to existing FCM. In this paper, we propose the Texture weighted FCM (TFCM) algorithm performed with Local Binary Patterns on Three Orthogonal Planes (LBP-TOP). By incorporating texture constraints, TFCM could take into account more global image information. The proposed algorithm is divided into following stages: Volume of Interest (VOI) is extracted by 3D skull stripping in the pre-processing stage. The initial FCM clustering and LBP-TOP feature extraction are performed to extract and classify each cluster's features. At the last stage, FCM with texture constraints refines the result of initial FCM. The proposed algorithm has been implemented to evaluate the performance of segmentation result with Dice's coefficient and Tanimoto coefficient compared with the ground truth. The results show that the proposed algorithm has the better segmentation accuracy than existing FCM models for brain MRI.
- KSP Keywords
- 3D brain MRI, Brain MRI segmentation, Brain magnetic resonance image, Dice's coefficient, FCM Clustering, Feature extractioN, Fuzzy C-means (FCM) algorithm, Fuzzy c-means algorithm, Global image information, Ground Truth, Human brain