Description

The system propose a modified suppressed FCM with spatial information. The membership function is modified by an optimal-selection-based suppressed method. The self-tuning non local spatial information is used. A gray level histogram is constructed by using the proposed spatial information. The experiments on noisy images demonstrate the superiority of the proposed method. Based on the concept of machine learning with the capability of learning to improve the performance of a task on the basis of the previous experience, we propose an OSFCM-SNLS algorithm that performs clustering and selects the parameter an in S-FCM simultaneously. The learning process of is based on an exponential separation strength between clusters and is updated at each iteration. Numerical examples will serve to illustrate the effectiveness of the proposed MS-FCM with the ability of performing clustering and also selecting the parameter with a proto-type-driven learning. Finally, we use the MS-FCM algorithm to an MRI segmentation in ophthalmology. In The proposed algorithm every point of the data set has a weight in relation to every cluster. Therefore this weight permits to have a better classification especially in the case of noise data. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results demonstrate that the presented algorithm performs more robust to noise than the standard FCM algorithm.