EFFECTIVE ALGORITHM FOR BENIGN BRAIN TUMOR DETECTION USING FRACTIONAL CALCULUS

Brain tumor detection has gained research interest due to complexity involved in tumor detection and mortality ratio over the few decades. The present manuscript primarily addresses issues regarding benign brain tumor detection. Brain tumor detection involves segmentation in primary stage. Segmentation using existing techniques have some limitation like unable to handle noisy data and unable to detect small intensity variation in the image. In this manuscript, a new fractional mask design for benign brain tumor detection is proposed. Qualitative and quantitative analysis have been performed to prove superiority over existing boundary based methods available .

[1]  K. B. Oldham,et al.  The Fractional Calculus: Theory and Applications of Differentiation and Integration to Arbitrary Order , 1974 .

[2]  Ryosuke Shibasaki,et al.  An approach to image segmentation using multiresolution analysis of wavelets , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[3]  Manish Kumar Bajpai,et al.  A NOVEL APPROACH FOR EDGE DETECTION OF LOW CONTRAST SATELLITE IMAGES , 2015 .

[4]  Inan Güler,et al.  Segmentation of Tumor and Edema Along With Healthy Tissues of Brain Using Wavelets and Neural Networks , 2015, IEEE Journal of Biomedical and Health Informatics.

[5]  Nelly Gordillo,et al.  State of the art survey on MRI brain tumor segmentation. , 2013, Magnetic resonance imaging.

[6]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[7]  Jun Kong,et al.  A novel approach for segmentation of MRI brain images , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[8]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[10]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[11]  Demetri Terzopoulos,et al.  Deformable models , 2000, The Visual Computer.

[12]  Shohreh Kasaei,et al.  Automatic Brain Tissue Detection in Mri Images Using Seeded Region Growing Segmentation and Neural Network Classification , 2011 .

[13]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[14]  Manish Kumar Bajpai,et al.  A Novel Non-Invasive Method for Extraction of Geometrical and Texture Features of Wood , 2017 .

[15]  A. Jemal,et al.  Cancer statistics, 2015 , 2015, CA: a cancer journal for clinicians.

[16]  Jun Cheng,et al.  brain tumor dataset , 2016 .

[17]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[18]  B. F. Logan,et al.  The Fourier reconstruction of a head section , 1974 .

[19]  Koushlendra Kumar Singh,et al.  A novel approach for enhancement of geometric and contrast resolution properties of low contrast images , 2018, IEEE/CAA Journal of Automatica Sinica.

[20]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[21]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Pierre Lanchantin,et al.  Statistical image segmentation using triplet Markov fields , 2003, SPIE Remote Sensing.