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Sushma Laxman Wakchaure, Anil Khandekar

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose a Visualization of 3d View of Detected Brain Tumor and Calculation of its Volume that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations.it also include tumor detection with comparisons and finally 3D model is obtain of tumor detected portion with its volume. Proposed method developed a tumor detection method using three parameters; edge (E), gray (G), and threshold value (T) values. The method proposed here studied the EGT parameters in a supervised block of input images. These feature blocks were compared with standardized parameters (derived from normal template block) to detect abnormal occurrences, e.g. image block which contain lesions or tumor cells. The proposed method shows more precision among the others. Processing time is less also proposed system implement more than one edge detection system i.e. sobel edge detection and canny edge detection method. Result also compared of implemented both methods. This will help the physicians in analyzing the brain tumors accurately and efficiently. It is used to segment the brain tumor from 2D images and then converting it into 3D for further model analysis and volume calculation