08-06-2013, 02:35 PM
MEDICAL IMAGE SEGMENTATION BY USING MOUNTAIN CLUSTERING
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ABSTRACT
This paper presents Improved Mountain Clustering (IMC) based medical image segmentation. Proposed technique is a more powerful approach for X-Ray image based diagnosing diseases like lung cancer and tuberculosis. The IMC based segmentation approach was applied on lung X-Ray images and compared with some existing techniques such as K-Means and FCM based segmentation approaches. The performance of all these segmentation approaches is compared in terms of cluster entropy as a measure of information. The segments obtained from the methods have been verified visually.
INTRODUCTION
Image segmentation is widely used in exploratory pattern-analysis, grouping, decision making, and machine-learning situations for medical images. However, in many such problems, there is little a priori information (e.g., statistical models) available about the data, and the decision-maker must make as few assumptions about the data as possible. It is under these restrictions that clustering methodology is particularly appropriate for the exploration of interrelationships among the data points to make an assessment (perhaps preliminary) of their structure.
The image segmentation can be accomplished using certain properties of an image such as color and texture for medical images. Clustering is the natural grouping of similar objects such that:
• Each group or cluster is homogeneous; examples that belong to the same group are similar to each other.
• Each group or cluster should be different from other clusters, that is, examples that belong to one cluster should be different from the examples of other clusters.