Abstract
The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.
Description
Image segmentation is an essential technique for processing MRI cerebral tissue images. Image segmentation is the separation of a digital image into numerous segments or pixel sets, in which pixels are grouped similarly according to homogeneous criteria, such as texture, intensity, or color, to discover items and borders inside the defined image [1]. Many algorithms, such as graph cut, level set, edge detection, and clustering, have been proposed for medical image segmentation [2]. Medical image segmentation aims to divide images into homogeneous partitions that concern other pixel neighborhoods to make these images significant in realizing the aims of medical images. The results of image segmentation are processed by extracting a set of segments, regions, or contours of the image. Pixels in a region possess several similar characteristics or computed properties, such as contrast, texture, color, and grayscale [3]. Fuzzy c-means (FCM) is an important algorithm used in MRI cerebral tissue segmentation [4, 5]. FCM is successfully improving the performance of medical image segmentation [5]. Jiang et al. [2] are introduced and proposed the current scheme with spatial constraints LCFCM_s algorithm for spatial information to the FCM algorithm which pixel enables to affect by its immediate neighborhood. This method entails increased time consumption because of the calculation in each iteration step to the spatial neighborhood [2].
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