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B. Srikanth
Dr. E. Sreenivasa Reddy


Tumor detection and segmentation is an important task in medical image processing. Detection of the presence of tumor on time is important for treatment planning. The main objective of this research is the automatic analysis, detection and segmentation of multiple tumors from Magnetic Resonance Image (MRI). Different approaches exploiting anatomical and spatial preceding information have been projected. The paper presents the construction of an Adaptive Advanced Segmentation Image Enhanced Technique (AASIET) and detailed probabilistic chart describing the multi tumors’ preferential locations in the brain. The proposed constitutes an outstanding mat lab tool for the study of the mechanisms behind the genesis of the multi tumors and provides strong spatial on where they are expected to appear. The proposed characteristic is exploited in a watershed segmentation based segmentation method where the plan guides the different segmentation process as well as characterizes the multi tumor’s preferential analysis. Second, we introduce an Adaptive Feature Fuzzy C-means (AAFFCM) simultaneous multi tumor SVM classifier and register with absent correspondences method. The anatomical knowledge introduced by the advance development increases the segmentation quality; while increasingly acknowledge the attendance of the multi tumor ensures that the registration is not despoiled by the missing correspondences without the introduction of a bias. The third method is designed as a Morphological Operation Symmetric Analysis (MOSA) hierarchical grid-based representation where the segmentation and register parameter are estimated simultaneously from database image threshold and segmentation is to remove an assortment of features of the image on the grid’s control point. The potentials of all methods have been demonstrated on a large data-set of heterogeneous -in appearance, size and shape. The proposed methods go away from the scope of the presented scientific context due to their strong modularity and could easily be adapted to other clinical or computer vision problems. In this paper we compare the different approaches of multi tumor detection algorithms.


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