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Jyotsana Tripathi
Apoorva Chaudhary


Brain tumour is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Curing cancer has been a major goal of medical researchers for decades. The early detection of cancer can be helpful in curing the disease completely.An Artificial neural network-based approach to identify brain tumour from MRI images can help in quicker more efficient detection. The main objective of the project is to help identify and classify brain tumours by training an Artificial Neural Network on MRI scans of various tumour free brains as well as brains with tumours to allow the system to learn how to classify unseen MRI This paper describes the strategy to detect & extraction of brain tumour from patient’s MRI scan images of the brain. This method uses some noise removal functions, segmentation and morphological operations from the basic concepts of image processing. The process of tumour detection and elicitation of the tumour from the image is done using MATLAB.




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S. Murugavalli, V. Rajamani, “A high speed parallel fuzzy c-mean algorithm for brain tumour segmentationâ€,†BIME Journalâ€, Vol. no: 06, Issue (1), Dec., 2006

H.R. Tizhoosh, G. Krell, and B. Michaelis, (1997) "On fuzzy enhancement of megavoltage images in radiation therapy," in: Proceedings of the 6th IEEE International Conference on Fuzzy Systems 3, pp. 1398-1404.

Nicolaos B. Karayiannis and Pin-I Pai, "Segmentation of Magnetic Resonance Images Using Fuzzy Algorithms for Learning Vector Quantization", IEEE Transactions on Medical Imaging, Vol. IS, No. 2, pp. 172-ISO, February 1999

Information with Fuzzy C-Means Clustering," European Journal of Scientific Research, ISSN I450-2I6X Vol. 41 No.3 pp.437-45I

Fitsum Admasua, Stephan AI-Zubia, Klaus Toenniesa, Nils Bodammerb and Hermann Hinrichsb, "Segmentation of Multiple Sclerosis Lesions from MR Brain Images Using the Principles of Fuzzy-Connectedness and Artificial Neuron Networks", International Conference on Image Processing, Barcelona, Spain, Vol. 3, 2003.

Priyanka Kamboj, Versha Rani," A Brief Study of Various Noise Model and Filtering Techniques " Journal of Global Research in ComputerScience,Vol.4,pp.166-171,2013

D.Selvaraj, R.Dhanasekaran," Novel approach for segmentation of brain magnetic resonance imaging using intensity based thresholding", IEEEInternational Conference on Communication Control and Computing Technologies, pp 502-507,2010

Rajeev Ratan, Sanjay Sharma,S. K. Sharma," Brain Tumor Detection based on Multi-parameter MRI Image Analysis" InternationalConference of Graphics and Statistical Technology,Vol.9,pp 9-17,2009

Anil Z Chitade(2010j , " Colour based image segmentation using k­ means clustering," International Journal of Engineering Science and Technology Vol. 2(10), 5319-5325

Anupurba Nandi, 2015 IEEE International Conference on Computer Graphics and Information Security.

Natarajan, Krishnan, Natasha Sandeep Kenkre," Tumor Detection using threshold operation in MRI Brain Images" International Conference onComputer intelligence and Computing Research, pp 1-4, 2012

Kaiqiong Sun, Shaofeng Jiang," Segmentation of Coronary Artery on Angiogram by Combined Morphological Operations and Watershed",

IEEE International Conference on Biomedical Engineering and Informatics,pp1-4,2009

W. Gonzalez, “Digital Image Processingâ€, 2nd ed. Prentice Hall, Year of Publication 2008, Page no 378.

Nassir Salman," Image Segmentation Based on Watershed and Edge Detection Techniques",The International Arab Journal of InformationTechnology, vol 3,pp 104-110,2006

S. Zulaikha BeeviM, Mohamed Sathik(20IO). "An Effective Approach for Segmentation of MRI Images: Combining Spatial

M.Masroor Ahmed. Dzulkifli Bin Mohammad, " Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model", IEEE International Journal of Image Processing,pp27-34,2011