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A R T I C L E I N F O A B S T R A C T Article history: Received: 01 January, 2020 Accepted: 11 April, 2020 Online: 03 May, 2020 It is difficult to accurately segment brain MRI in the complex structures of brain tumors, blurred borders, and external variables such as noise. Much research in developing as well as developed countries show that the number of individuals suffering tumor of the brain has died as a result of the inaccurate diagnosis. The proposed article, a novel hybrid method improves segmentation accuracy. The proposed research includes three basic steps. In the first step, the adaptive filter based on mean and local variance is utilized for noise removal in the input images. It helps in de-noising to a different orientation and scale, creates numerous responses for all components in the medical images while preserving the edges. In the second step, the development of a hybrid method takes place. It is the combination of extended K-mean clustering and fuzzy C-mean clustering. The purpose of the research is to develop a hybrid segmentation structure of single-channel T1 MR Images for multiform benign and malignant tumors. It removes the limitation of prefixed cluster size which helps in improving the segmentation accuracy by reducing the sensitivity of the clustering parameters. In the third step, the morphological non-linear operation performed for the removal of the non-tumor part. The proposed approach is evaluated against various statistical parameters such as mean, standard deviation, entropy, correlation, homogeneity, smoothness and variance. The parameters result predicts a greater balance between the automated tumor areas extracted by radiologists with the tumor areas extracted by the proposed method. The findings show that the proposed hybrid method achieves a 98% level of segmentation accuracy.

A Novel Hybrid Method for Segmentation and Analysis of Brain MRI for Tumor Diagnosis