Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

New Method to Optimize Initial Point Values of Spatial Fuzzy c-means Algorithm

Fuzzy based segmentation algorithms are known to be performing well on medical images. Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers from optimum selection of seed point initialization which is done either manually or randomly. In this paper, an enhanced SFCM algorithm is proposed by optimizing the SFCM initial point values. In this method in order to increasing the algorithm speed first the approximate initial values are determined by calculating the histogram of the original image. Then by utilizing the GWO algorithm the optimum initial values could be achieved. Finally By using the achieved initial values, the proposed method shows the significant improvement in segmentation results. Also the proposed method performs faster than previous algorithm i.e. SFCM and has better convergence. Moreover, it has noticeably improved the clustering effect

Enhanced SVD watermarking algorithms

inbox unpub unspecified FSKSM 2011-00 completed 75 Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System masters thesi

An enhanced fuzzy c-means medical segmentation algorithm

Fuzzy-based algorithms have been widely used for medical segmentation. Fuzzy c-means (FCM) is one of the popular algorithms which is being used in this field. However this method of segmentation suffers mainly from two issues. Firstly, noisy images highly reduce the quality of segmentation. Secondly, the edges of the segmented images are not sharp and clear. Therefore the boundary between the two regions cannot clearly be identified. Our goal of this research is to propose a segmentation algorithm that cancels the negative noise effect on the final result and performs the segmentation with high edge accuracy by combining Sobel edge detection with FCM. Our algorithm is evaluated against three brain magnetic resonance image (MRI) datasets of real patients. The obtained analysis indicates that the edges of the segmented images by our method are sharp and accurate

Two enhanced SVD based watermarking algorithms using U matrix

Protecting images against illegal duplications is an important fact and recently many watermarking algorithms have been published to protect these images. In this paper two SVD-based watermarking algorithms are proposed and the results are compared with Chang’s method. In the proposed methods U component is explored for embedding the watermark. Experimental results show that imperceptibility and robustness of the algorithm is good