Image Segmentation Algorithm Combined with Regularized P-M De-Noising Model and Improved Watershed Algorithm
Aiming at the problems of noise sensitivity and unclear contour in existing MRI image segmentation algorithms, a segmentation method combining regularized P-M de-noising model and improved watershed algorithm is proposed. First, the brain MRI image is pre-processed to obtain a brain
nuclear image. Then, the brain nuclear image is de-noised by a regularized P-M model. After that, the image is preliminarily segmented by the traditional watershed algorithm to extract the features of each small region. Finally, the small regions are merged by Fuzzy Clustering with Spatial
Pattern (FCSP) to obtain the segmentation image with smooth edges. The experimental results show that the algorithm can accurately segment the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. The average AOM and ME of the segmentation results on the BrainWeb dataset
reached 0.93 and 0.04, respectively.
Keywords: BRAIN MRI IMAGE; FUZZY CLUSTERING WITH SPATIAL PATTERN; IMAGE SEGMENTATION; REGULARIZED P-M DE-NOISING MODEL; WATERSHED ALGORITHM
Document Type: Research Article
Publication date: 01 February 2020
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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