Pavement Image Segmentation Based on FCM Algorithm Using Neighborhood Information
نویسندگان
چکیده
Standard FCM algorithm takes the pixel gray-scale information into account only, while ignoring the spatial location of pixels, so the standard FCM algorithm is sensitive to noise. This paper present a pavement image segmentation algorithm based on FCM algorithm using neighborhood information. The presented algorithm introduces neighborhood information into membership function to improve the standard FCM algorithm. It can eliminate noise effectively and retain the boundary information. The experiments by synthetic images and real pavement images show that the presented algorithm in this paper performs more robust to noise than the standard FCM algorithm and retain the boundary information effectively.
منابع مشابه
Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing
Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentati...
متن کاملBrain magnetic resonance image segmentation using novel improvement for expectation maximizing.
OBJECTIVE To improve the quality of expectation maximizing (EM) for brain image segmentation, and to evaluate the accuracy of segmentation results. METHODS This brain segmentation study was conducted in Universiti Putra Malaysia in Serdong, Malaysia between February and November 2010 on simulated and real images using novel improvement for EM. The EM-1 (proposed algorithm) was compared with n...
متن کاملImage segmentation based on fuzzy clustering with neighborhood information
In this paper, an improved fuzzy c-means (IFCM) clustering algorithm for image segmentation is presented. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial context information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighborhood information into the original FCM algorithm...
متن کاملMR Brain Image Segmentation Using an Improved Kernel Fuzzy Local Information C-Means Based Wavelet, Particle Swarm Optimization (PSO) Initialization and Outlier Rejection with Level Set Methods
This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...
متن کاملLocalized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation
This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy c-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image do...
متن کامل