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.

Abstract-Visual inspection by a human operator has been mostly used up till now to detect cracks in sewer pipes. In this paper, we address the problem of automated detection of such cracks. We propose a model which detects crack fractures that may occur in weak areas of a network of pipes. The model also predicts the level of dangerousness of the detected cracks among five crack levels. We evaluate our results by comparing them with those obtained by using the Canny algorithm. The accuracy percentage of this model exceeds 90% and outperforms other approaches.

Abstract-Medical image analysis process usually starts with segmentation step, which aims to separate different objects in the image scene. This is achieved by mainly dividing the image into two parts, the region of interest (ROI) and the background. Segmentation of acute lymphoblastic leukemia blood cell (ALL) based on microscope color image is one of the important step in the recognition process. This paper proposed a technique which aims to segment the color image of acute leukemia by transforming the RGB color space to C-Y color space .in the C-Y color space, the luminance component is used to segment (ALL) .The proposed algorithm runs on 100 microscopic ALL images and the experimental result shows that the proposed system can provide a good segmentation of ALL from its complicated background and shows that the segmentation accuracy of the proposed technique is 98.38% compared to the result of the manual segmentation method by expert.

Sewer overflows may cause communities to be vulnerable to various health problems and other monetary losses. This puts a lot of burden on responsible to minimize end user complaints. Therefore, crack prediction would be helpful to facilitate decision makers to control sewer overflow problems and prioritize inspection and rehabilitation needs .The accurate prediction of current underground sewer pipe cracks must be done before any pipe crashing with enough period of time to enable rehabilitation and replacement intervals, appropriate remedial methods and preventing sewer pipes crashing. Unfortunately, traditional technologies and models approaches have been limited to predict the development of sewer pipe cracks. In this paper, we address the problem of crack prediction of such cracks. This paper provides a proposed model which predict crack and cracks developments in any period of time that may occur in weak areas of a network of pipes.. We evaluate our results by comparing them with those obtained by many other models. The accuracy percentage of this model exceeds 90% and outperforms other approaches.

This research paper presents a system for the acute leukemia blast cells segmentation and classification. The research objective is to generate the features characterizing normal and infected cells. The proposed system consists of one segmentation method and one classification method of acute leukemia. The features extracted from the cell and adopted features are used as the input signals to the Multi Layer Perception (MLP) neural network classifier. The experimental results show that our proposed system is robust and effective in identifying acute leukemia blast cells.