Efficient Tumor Detection in MRI Brain Images

Sri Lalitha Y., Manognya Katapally, Keerthana Pabba, Vineetha Mudunuri


Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain[15].    An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model.


Brain Tumor, MRI Image, Fuzzy Clustering

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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