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Brain Tumor Detection Using Hard and Soft Computing Techniques


 

Medical image segmentation has great significance in the research in the research field of image segmentation. It is the one way to take out important information from medical images. There are many soft computing and hard computing techniques are there which are used for segment the interested area in medical images. Both has its own advantages and disadvantages. In this paper we provide a new approach to medical image segmentation. we use three approaches with the combination of each other. That approaches are K-mean, Fuzzy C-mean and genetic algorithm. So we divide this paper in three phases. In first phase we apply the K-mean algorithm on the image which segment the interested are but this algorithm not gave the satisfactory result. So on the result of this algorithm we apply FCM after this optimization is done with the help of GA. The experimental result is calculated on the basis of classification accuracy. The result is 90.3%.


Keywords

Medical Image Segmentation, K-means, FCM, MRI, GA
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  • Brain Tumor Detection Using Hard and Soft Computing Techniques

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Abstract


Medical image segmentation has great significance in the research in the research field of image segmentation. It is the one way to take out important information from medical images. There are many soft computing and hard computing techniques are there which are used for segment the interested area in medical images. Both has its own advantages and disadvantages. In this paper we provide a new approach to medical image segmentation. we use three approaches with the combination of each other. That approaches are K-mean, Fuzzy C-mean and genetic algorithm. So we divide this paper in three phases. In first phase we apply the K-mean algorithm on the image which segment the interested are but this algorithm not gave the satisfactory result. So on the result of this algorithm we apply FCM after this optimization is done with the help of GA. The experimental result is calculated on the basis of classification accuracy. The result is 90.3%.


Keywords


Medical Image Segmentation, K-means, FCM, MRI, GA