Abstract
Fuzzy C-means (FCM) clustering is the widest spread clustering approach for medical image segmentation because of its robust characteristics for data classification. But, it does not fully utilize the spatial information and is therefore very sensitive to noise and intensity inhomogeneity in magnetic resonance imaging (MRI). In this paper, we propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm to overcome the mentioned problem. The approach consists of two successive stages. First stage is achieved through the incorporation of local spatial interaction among adjacent pixels in the fuzzy membership function imposed by an auxiliary variable associated with each pixel. The variable describes the involvement level of each pixel for construction of membership functions and different clusters. Then, we adapted a kernel-induced distance to replace the original Euclidean distance in the FCM, which is shown to be more robust than FCM. The problem of sensitivity to noise and intensity inhomogeneity in MRI data is effectively reduced by incorporating a kernel-induced distance metric and local spatial information into a weighted membership function. The experimental results show that the proposed algorithm has advantages in accuracy and robustness against noise in comparison with the FCM, SFCM and CSFCM methods on MRI brain images.
Highlights
Segmentation is an essential preprocessing step in computer-guided medical image analysis and diagnosis [1]
The qualitative evaluation compares the output of the proposed algorithm with the following algorithms: fuzzy c-means (FCM) [23], SFCM [24], and conditional spatial fuzzy c– means algorithm (CSFCM) [7].The quantitative evaluation shows the final output with the reference segmented image and compares the segmentation results of the proposed method with three fuzzy-based mentioned algorithms along the same line based on ground-truth images
The parameters in the CSFCM [7] method were set as follows: (i) The parameters of weighted membership function were set to be p=2 and q=2 since it had been shown that the algorithm provides superior results using these values [CSFCM]. (ii) The size of the neighborhood was set to N(xk)=9 (3 × 3 window centered around each pixel)
Summary
Segmentation is an essential preprocessing step in computer-guided medical image analysis and diagnosis [1]. Some factors complicate segmentation in medical images such as noise, normal anatomic variation, post-surgical anatomic variation, vague and incomplete boundaries, variation of contrast, inhomogeneities in the boundaries of the object of interest, motion blurring artifacts and so on [2]. To address these difficulties, clustering methods have been extensively studied and widely used in MIS, with promising results [3]. We propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm that can effectively segment MRI brain images with the presence of noise and intensity inhomogeneity.
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