Author : Venkatnaresh M 1
Date of Publication :7th March 2017
Abstract: The vital system of a human being is brain, such important part may be affected by unwanted tissue growth i.e tumor. Which is a critical problem in medical science for both diagnosis and treatment. In this paper we are concern about the diagnosis of tumor by analyzing MRI Images. The composite view of MRI images such as high intensive, divergent nature and Improper boundaries is difficult task for physician’s interpretation. So, an automated tumor segmentation methodology is demanded. To confer a solution to this issue, tumor segmentation method by K means clustering and Fuzzy C means clustering is implemented in this paper. After segmentation The features are extracted to classify as tumorous or non-tumorous. The feature extraction form MRI Image are implemented by using Intensity, Intensity Histogram and GLCM Methods. GLCM (Gray Level Co-Occurrence) feature extracting method yields better results when compared with other methods. The Accuracy of the segmented data is evaluated by confusion matrix that is created from the extracted features. All the implementation are performed against BRATS dataset.
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