Image segmentation plays a preliminary and indispensable step in medical image processing. Magnetic resonance (MR) segmentation used for brain tissues extraction white matter (WM), gray matter (GM) and cerebrospinal fluids (CSF). These tissues help in many medical image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning and Alzheimer disease. This paper presents a novel manipulation or utilization of Fuzzy C-Means (FCM) Clustering by using wavelet Decomposition for feature extraction and feature vector treat as input to FCM. This algorithm is called Wavelet Fuzzy C-means (WFCM), the algorithm results are compared with standard FCM and Kernelized Fuzzy C-Means (KFCM). The performance of the proposed segmentation algorithm provides satisfactory results compared with the other two algorithms.

Image annotation is a task of assigning a set of semantic tags or keywords to unlabeled image based on a training set of data. This machine learning process depends on extraction and clustering the lowlevel features of images then mapping them to the semantic which is high-interest image retrieval. Many annotation techniques with reasonable performance have proposed in the last decade. The proposed algorithm for automatic annotation in this paper depends on similarity computation and label transferring to the query image. Similarity computing uses low-level image features and significant distance estimates. The feature vector of the test image compared with the feature matrix of similar and dissimilar image pairs in the training data sets. Then the label or keyword transfer from the similar image pair to the test image is performed by counting the local frequency of neighbor's keywords. Performance is evaluated using precision and recall.