Informatics Publishing Limited

S. Arumugadevi ¹, V. Seenivasagam ²

Affiliations
1 Department of Information Technology, Sri Krishna Engineering College, Chennai, IN
2 Department of Computer Science and Engg, National Engineering College (Autonomous), Kovilpatti, IN

Abstract

Background/Objectives: Image segmentation is the first step for any image processing based applications. The Conventional methods are unable to produce good segmentation results for color images. Methods/Statistical analysis: We present two soft computing approaches namely Fuzzy C-Means (FCM) clustering and Self Organizing Map (SOM) network are used to segment the color images. The segmentation results of FCM and SOM compared to the results of K-Means clustering. Results/Findings: Our experimental results shown that the Fuzzy C-Means and SOM produced the better results than K-means for segmenting complex color images. The time required for the training of SOM is higher. Conclusion/Application: The trained SOM network reduced the execution time for segmenting color images. The performance of FCM and SOM is higher than the K-means for segmenting color images. Applications of color image segmentation are video surveillance, face recognition, fingerprint recognition, object detection, medical image analysis, and Automatic target detection.

Keywords

Clustering, FCM, Image Segmentation, K-Means, SOM, Subtractive Clustering.

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R. Chitra ¹, V. Seenivasagam ²

Affiliations
1 Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, IN
2 Department of Information Technology, National Engineering College, IN

Abstract

The Healthcare industry generally clinical diagnosis is done mostly by doctor's expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

Keywords

Neural Network, Hybrid Intelligent Algorithm, Heart Disease Prediction, Computer Aided Decision Support System.

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