Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classificationbased, region-based, and contour-based, and discuss the ...

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algor...

Tumor is an uncontrolled growth of tissue in any part of the body. The tumor is of different types and they have different characteristics and different treatment. Normally the anatomy of the brain can be viewed by the MRI scan or CT scan. MRI scanned image is used for the entire process. The MRI scan is more comfortable than any other scans for diagnosis. It will not affect the human body, bec...

Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the i...

Software quality and reliability have become the main concern during the software development. It is very difficult to develop software without any fault. The fault-proneness of a software module is the probability that the module contains faults and a software fault is a defect that causes software failures in an executable project. Early detection of fault prone software components enables ve...

The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology...

In this paper, we apply K-means and Fuzzy C-Means, two widely used clustering algorithms, to cluster a lymph node tissue section which had been diagnosed with metastatic infiltration (cancer spread from its original location). Each cluster algorithm is run 10 times as different initialisation states may lead to different clustering results. We compare the performance of the two algorithms by su...

Image segmentation and classification are two important steps in multicolor fluorescence in-situ hybridization (MFISH) images analysis. In this paper we first developed an improved adaptive fuzzy c-means (IAFCM) algorithm for the segmentation of the DAPI channel of M-FISH images to extract interested regions for the following classification task. Then we employed a sparse representation-based c...

It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Although preventative techniques such as access control and authentication attempt to prevent intruders, these can fail, and as a second line of defence, intrusion detection has been introduced. Rare events are events that occur very infrequently, detection of rare event...