Bounding box based automatic segmentation of brain tumors using random walker and active contours from brain MRI

This paper reveals a computer aided system aimed at automatically segmenting brain tumors from MRI images is proposed, using bounding box-Active contour and Random walker algorithm. This method performs fast and accurate segmentation results by segmenting the most dissimilar regions of a tumor image. This paper overviews the benefits of automatic segmentation than the manual segmentation algorithms and we are proving that this algorithm is better than most of the segmentation.

[1]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[2]  Wiro J Niessen,et al.  Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm. , 2004, Academic radiology.

[3]  Rüdiger Westermann,et al.  Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation , 2005, MICCAI.

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

[5]  Qingmao Hu,et al.  Rapid and automatic detection of brain tumors in MR images , 2004, SPIE Medical Imaging.

[6]  R. Sukanesh A Padma,et al.  Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features , 2011 .

[7]  E. Schwartz,et al.  Faster graph-theoretic image processing via small-world and quadtree topologies , 2004, CVPR 2004.

[8]  Christos Davatzikos,et al.  Deformable Registration of Glioma Images Using EM Algorithm and Diffusion Reaction Modeling , 2011, IEEE Transactions on Medical Imaging.

[9]  Li-Hong Juang,et al.  MRI brain lesion image detection based on color-converted K-means clustering segmentation , 2010 .

[10]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[11]  R. Dhanasekaran,et al.  Fuzzy Clustering and Deformable Model for Tumor Segmentation on MRI Brain Image: A Combined Approach , 2012 .

[12]  Gözde B. Ünal,et al.  Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications , 2012, IEEE Transactions on Medical Imaging.

[13]  Guillermo Sapiro,et al.  Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting , 2009, International Journal of Computer Vision.

[14]  Vinod Kumar,et al.  A novel content-based active contour model for brain tumor segmentation. , 2012, Magnetic resonance imaging.

[15]  Hamid Soltanian-Zadeh,et al.  Atlas-based fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients , 2011, NeuroImage.

[16]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[17]  L. Vese,et al.  A Variational Method in Image Recovery , 1997 .