The brain tumor detection is the approach which can detect the tumor portion from the MRI image. To detect tumor from the image various techniques has been proposed in the previous times. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. The technique which is proposed in this research paper is based on morphological scanning and naïve bayes classification. The morphological scanning will scan the input image and naïve bayes classifier mark the tumor portion from the MRI image. The proposed algorithm is implemented in MATLAB and results are analyzed in terms of qualitatively and quantitatively in various parameters like false positive rate, false negative rate, execution time, PSNR, MSE, Accuracy and Fault Detection and also calculate overlapping area with dice coef. The proposed method has been tested on data set with more than 25 slide scanned images. This proposed method achieved accuracy with 86% best cell detection.

MRI, Naïve Bayes, Morphological Scanning, Brain Tumor, Clustering

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