Abstract
Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.
Highlights
Image segmentation plays a pivotal role in computer-aided diagnosis and therapy
This paper proposes a hybrid method for magnetic resonance (MR) image segmentation
We evaluate the correctness of the segmentation using real brain scans with ground truth by expert segmentations obtained from the Internet Brain Segmentation Repository (IBSR) [18]
Summary
Image segmentation plays a pivotal role in computer-aided diagnosis and therapy. The objective of image segmentation is to partition an image into nonoverlapping, constituent regions that are homogeneous with respect to some attributes such as intensity and texture [1]. Based on the advantages of Journal of Biomedicine and Biotechnology thresholding and fuzzy clustering algorithms for image segmentation, some authors have proposed hybrid techniques combining various FCM-based methods with thresholding. Chaabane Ben et al proposed a hybrid method that combines automatic thresholding with FCM [13] This technique yielded good results such that significant peaks and valleys are identified properly. Another hybrid approach was introduced by Tan and Isa, and it provided a good solution to overcome the FCM’s sensitiveness to the initialization condition of cluster centroids and selection of the number of clusters by using the histogram thresholding [14].
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