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Published on March 17, 2014

Author: divaybhardwaj

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Automated Segmentation and Classification of Magnetic Resonance Imaging X-ray Images for Brain Tumor and Fracture Detection: Automated Segmentation and Classification of Magnetic Resonance Imaging X-ray Images for Brain Tumor and Fracture Detection OBJECTIVE: OBJECTIVE The Main Objective of the project is to develop a automated system which can detect brain tumors using MRI image of a patient as a input. The system makes use of the varying intensity levels of tumor and healthy tissue. The objective of this work is to present an automated unsupervised method for finding tumor. INTRODUCTION: INTRODUCTION The extra cells form a mass of tissue called a tumor. Tumors are benign or malignant. Specifically the aim for this work is to segment a tumor in a brain. This will make the surgeon able to see the tumor and then ease the treatment. The critical problem is finding the tumor location automatically and later finding its boundary precisely MODULES: MODULES Module1-Preprocessing-in which filters have been implemented in it like gaussian , arithmetic ,high pass using MRI Images etc Module-2-Segmentation using watershed , threshold and edge. Module-3 Classification in which we classify the tumor. Module-4 Binarization and texture MRI Image Module-5 Use Morphological Operators like dilation and erosion in MRI Image Module-6 Detect the tumor and Save Images. APPLICATIONS: APPLICATIONS This project used in detecting Brain Tumor among patients. Improved image after using above process. Doctors have a better accuracy of identifying the brain tumor at its accurate location in the brain. METHODOLOGY : METHODOLOGY Preprocessing Image: means improvement of image that we have original as well as modified image. Segmentation of a Image: the process of partitioning a  digital image  into multiple segments (sets  of pixels, also known as superpixels ). Multiparameter Calculations: Recent advances in medical image analysis often include processes for an image to be segmented in terms of a few parameters and into smaller sizes or regions PROBLEM DEFINATION: PROBLEM DEFINATION Brain Tumor is one of the most deadly and dangerous disease which accounts for a lot of death in children as well as adults. A major task while treating the brain tumor is detecting the tumor accurately so that the treatment can be done very effectively. As Doctors have to manually see the location of the tumor using CT( Computerized Tomography ) scan images, they can sometimes miscalculate its location and hence the treatment of the patient can be adversely affected. The proposed system helps to solve this problem by locating the tumor at its exact location using various image processing techniques. The Doctor after using the proposed system can find the pin point location of the tumor and directly proceed to the treatment of the patient. The proposed system would eliminate the hit and trial techniques that doctors use while the treatment of the patient. REQUIREMENTS: REQUIREMENTS SOFTWARE REQUIREMENTS Turbo C++ compiler Matlab 2009a .2013a Web camera software Operating System- Windows 8 HARDWARE REQUIREMENTS 20 GB Hard Disk 1 GB RAM Pentium Processor Web cam SCREENSHOTS-PREPROCESSING: SCREENSHOTS-PREPROCESSING ALGOTHRIM USED k-MEANS: ALGOTHRIM USED k-MEANS FUZZY CLUSTERING: FUZZY CLUSTERING OTSU : OTSU SEGMENTATIONS METHODS-THRESHOLD: SEGMENTATIONS METHODS-THRESHOLD EDGE SEGMENTATION: EDGE SEGMENTATION WATERSHED SEGMENTATION: WATERSHED SEGMENTATION REFERENCES: REFERENCES Rafael C. Gonzalez, Richard E. Woods- Digital Image Processing 3 rd  Edition, Pearson Publication N. Kwak , and C. H. Choi , "Input Feature Selection for Classification Problems", IEEE Transactions on Neural Networks, vol.13(1), pp 143-159, 2002.   Georgiadis . Et all, "Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features", Computer Methods and program in biomedicine, vol 89, pp24-32, 2008.   L. Lemieux, G. Hagemann , K. Krakow, and F. G. Woermann ,  "Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data",   Magn . Reson . Med. ,  vol. 42,  pp.127 -135, 1999.   W. M. Wells III, W. E. L. Grimson , R. Kikinis , and F. A. Jolesz ,  "Adaptive segmentation of MRI data",   IEEE Trans. Med. Imag . ,  vol. 15,  no. 4,  pp.429 -442 ,1996.   R. K. S. Kwan, A. C. Evans, and G. B. Pike,  "MRI simulation-based evaluation of image-processing and classification methods",   IEEE Trans. Med. Imag . ,  vol. 18,  no. 11,  pp.1085 -1097 ,1999 . http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.121.6183&rep=rep1&type=pdf REFERENCES: REFERENCES http://seminarprojects.net/t-brain-tumor-detection-based-on-multiparameter-mri-image-analysis http://www.mediabistro.com/portfolios/samples_files/1809558_vkRJSgG6YNXYheolDuv5em1pV.pdf http://en.wikipedia.org/wiki/Brain_tumor N. Kwak , and C. H. Choi , "Input Feature Selection for Classification Problems", IEEE Transactions on Neural Networks, vol.13(1), pp 143-159, 2002.   Georgiadis . Et all, "Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features", Computer Methods and program in biomedicine, vol 89, pp24-32, 2008.   L. Lemieux, G. Hagemann , K. Krakow, and F. G. Woermann ,  "Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data",   Magn . Reson . Med. ,  vol. 42,  pp.127 -135, 1999.   W. M. Wells III, W. E. L. Grimson , R. Kikinis , and F. A. Jolesz ,  "Adaptive segmentation of MRI data",   IEEE Trans. Med. Imag . ,  vol. 15,  no. 4,  pp.429 -442 ,1996.   R. K. S. Kwan, A. C. Evans, and G. B. Pike,  "MRI simulation-based evaluation of image-processing and classification methods",   IEEE Trans. Med. Imag . ,  vol. 18,  no. 11,  pp.1085 -1097 ,1999 . PowerPoint Presentation: THANK YOU

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