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Erschienen in:
2016 | OriginalPaper | Buchkapitel
BITSMSSC: Brain Image Tomography Using SOM with Multi SVM Sigmoid Classifier
verfasst von : B. Venkateswara Reddy, A. Sateesh Reddy, P. Bhaskara Reddy
Erschienen in: Computational Intelligence in Data Mining—Volume 2
Verlag: Springer India
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Abstract
Image segmentation is a process of elevating the objects by partitioning a digital image into multiple segments. To analyze an image, segmentation is the best process to follow. Especially, for detecting tumours from medical images such as brain, skin and breast in the field of medicine. To improve the results of brain images PSNR, ENTROPY image fusion technique is applied. Here the segmentation process is carried out by k-means clustered model algorithm. Then the entire data base is subjecting to classification mode under multi class svm sigmoid classifier. This generates a descent output of 96 % accurate results using various texture features they are contrast, energy, area of the tumour detecting by the cluster model and entropy. These parameters helps in identifying the tumour detection from brain MRI, CT scanned images.