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Detection of Brain Tumor using Modified Histo-Thresholding-Novel Approach

Chandrahas Sahu, Kiran Verma


In medical image processing brain tumor detection is one of the challenging tasks, since brain images are complicated and tumors can be analyzed only by expert physicians. The knowledge of volume of a tumor plays an important in the treatment of malignant tumors.Brain cancer can be counted among the most deadly and intractable diseases. Tumors may be embedded in regions of the brain that are critical to orchestrating the body‟s vital functions, while they shed cells to invade other parts of the brain, forming more tumors too small to detect using conventional imaging techniques. Brain cancer‟s location and ability to spread quickly makes treatment with surgery or radiation like fighting an enemy hiding out among minefields and caves. Manual segmentation of brain tumors from Magnetic Resonance images is a challenging and time consuming task so in this paper brain tumor is detected at various levels. First the pre-processing is reduce noise and then edge detection is done by using canny filter, then Segmentation is done by means of histogram clustering in which the tumor affected image is divided into two parts and threshold value is set , based on this value tumor is detected. Secondly the other technique involved is superimposing of the tumor affected with the healthy image. This method does not require any initialization while the others require an initialization inside the tumor. The third method in which, the histogram is calculated and the threshold is fixed. This work is carried in MRI image.


Histogram, MRI, Thresholding, Brain Tumor

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S.Xavierarockiarajand K.Nithya, “Brain Tumor Detection Using Modified Histogram Thresholding-Quadrant Approach”, Journal of Computer Applications (JCA), Volume V, Issue 1, 2012

Mohammad ShajibKhadem, “MRI Brain image segmentation using graph cuts”, Master of Science Thesis in Communication Engineering, Department of Signals and Systems, Chalmers University Of Technology, Goteborg, Sweden, 2010.

Yan Zhu and Hong Yan, “Computerized Tumor Boundary Detection Using a Hopfield Neural Network”, IEEE Trans. Medical Imaging, vol. 16, no. 1, pp.55-67 Feb.1997.

Orlando J. Tobias and RuiSeara,”Image Segmentation by Histogram Thresholding Using Fuzzy Sets,” IEEE transactions on Image Processing,Vol. 11,NO. 12,PP-1457-1465,DEC 2002.

Saif D. Salman and Ahmed A. Bahrani, “Segmentation of tumor tissue in gray medical images using watershed transformation method,” Intl. Journal of Advancements in Computing Technology,Vol. 2, No. 4,pp-123-127,OCT 2010.

Wenbing Tao, Hai Jin, and Yimin Zhang, “Color Image Segmentation Based on Mean Shift and Normalized Cuts,” IEEE Trans. on Systems, Man, and Cybernetic-Part B: Cybernetics, vol. 37, no. 5, pp.1382-1389, Oct. 2007.

F.kurugollu, “color image segmentation using histogram multithresholding and fusion,” Image and Vision Comuting,Vol. 19,pp-915-928,2001.

Mrs.MamataS.Kalas, “An Artificial Neural Network for Detection of Biological Early Brain Cancer,” Intl. Journal of Computer Applications, Vol. 1, No. 6,pp-17-23,2010.

S.Shen,W. A. Sandham and M. H. Granat, “PREPROCESSING AND SEG-MENTATION OF BRAIN MAGNETIC RESONANCE IMAGES,” Proc of the 4th Annual IEEE Conf on Information Technology Applications in Biomedicine, UK, pp. 149-152,2003.

Heath LM, Hall LO, Goldgof DB and Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonace images. Artificial Intelligence in Med. 21, 43-63.

Yang Y, Yan X, Zheng C and Lin P (2004) A novel statistical method for segmentation of brain MRI. IEEE, 946 949.

Salman YL, Assal MA, Badawi AM, Alian SM and MEI-EI Bayome (2005) Validation techniques for quantitative brain tumors measurements. IEEE Proc. Engg. Med. Biol. 7048- 7051.

Rajeev Ratan, Sanjay Sharma, S. K. Sharma, "Brain Tumor Detection Based onMulti-Parameter MRI Image Analysis", ICGST-GVIP Journal, Vol. 9, No. 3, PP-9-17,2009.

Tan E. T., Srinivasan R. and Robb R. A., "Intensity-Based Shape Propagation ForVolumetric Image Segmentation", IEEE International Symposium on BiomedicalImaging, PP-738-741,2006.

Jie Wu et. al., "Texture Feature based Automated Seeded Region Growing inAbdominal MRI Segmentation", Proc. of International. Conf. on BioMedicalEngineering and Informatics 2008, PP-263-267, 2008.

SriparnaSaha and Sanghamitra Bandy opadhyay, "MRI Brain ImageSegmentation by Fuzzy Symmetry Based Genetic Clustering Technique", IEEECongress on Evolutionary Computation (CEC 2007), PP-4418-4424, 2007.

Marijn E. Brummeret. al, "Automatic Detection of Brain Contours in MRI DataSets", IEEE Trans, on Medical Imaging, Vol. 12, No. 2, PP-153-166, 1993.

Jun Kong et. al, "A Novel Approach for Segmentation of MRI Brain Images",IEEE Melecon, PP-525-528, 2006.

H. P. Ng et. al, "Medical Image Segmentation Using Watershed Segmentationwith Texture-Based Region Merging", proc. of 30th Annual International IEEEEMBS Conf. Vancouver, British Columbia, Canada, PP-4039-4042, 2008.

K. J. Shanthi, M. Sasi Kumar and C. Kesavadas, "Neural Network Model forAutomatic Segmentation of Brain MRI", proc. of 2008 Asia Simulation Conference-?111 International Conf on Sys. Simulation and Scientific Computing, PP-1125-1128, 2008.

Arnaldo Mayer and Hayit Greenspan, "An Adaptive Mean-Shift Framework forMRI Brain Segmentation", IEEE Trans, on Medical Imaging, Vol. 28, No. 8, PP-1238-1250,2009.

Chunming Li et. al, "A Level Set Method for Image Segmentation in the Presenceof Intensity Inhomogeneities With Application to MRI", IEEE Trans, on MedicalImaging, Vol. 20, No. 7, PP-2007-2016, 2011.

DorinComaniciu and Peter Meer, "Mean Shift: A Robust Approach TowardsFeature Space", IEEE Trans, on Pattern analysis and Machine Intelligence, Vol.24, No. 8, PP-603-619, 2002.

AgusZainalArifin and Akira Asano, "Image Segmentation by HistogramThresholding Using Hierarchical Cluster Analysis", Pattern Recognition Letters, PP-1 -7, 2006.

R. B. Dubey, M.Hanmandlu, and S. K. Gupta, "An Advanced Technique forVolumetric Analysis", International Journal of Computer Applications, Vol. 1,No. l, PP-91-98, 2010.

K. Selvanayaki and Dr. M. Karnan, "CAD System for Automatic Detection ofBrain Tumor through Magnetic Resonance Image-A Review", InternationalJournal of Engineering Science and Technology, Vol. 2, No. 10, PP-5890-5901,2010.


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