An Efficient HIM Technique for Tumour Detection from MRI Images

Data mining techniques are widely used for data processing from large data set such as data center and data warehouse. An Image mining technique is a new form of data mining technique in the processing of image data. In the medical field, day by day size of medical images data is increasing. MRI images are one of them. The medical images like as CT scan, MR images are widely used in brain tumor detection, cancer detection from the human body. It is quite challenging and complicated work to detect abnormal cells and tissue such as tumor from MR image data sets. Due to higher importance and demand of medical image data, it is necessary to process it correctly and efficiently. Image Segmentation has an important role in the field medical image processing. In that way, MRI has become a useful medical diagnostic tool for the diagnosis of brain and other medical images.In this paper, we are presenting a new hybrid image mining technique HIMT for MRI Image processing. The proposed HIMT uses combined strategy of clustering method Fuzzy C Mean with the Genetic algorithm and SVM classifier. The main key feature of proposed method is it can able assigns and processed two or more than two clusters as compared to K Means method where data point must exclusively belong to one cluster center and genetic algorithm is used as and optimization tool which helps to achieve results in less time. Proposed HIMT and existing method K Means clustering method with GA both are implemented over MATLAB tool and various performance measurement parameters such as detection rate, area or size and time are calculated. Simulation results are clearly influenced that proposed HIMT method performs outstanding over existing method. Deepak Kokate | Jijo Nair "An Efficient HIM Technique for Tumour Detection from MRI Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15627.pd