A hybrid segmentation approach for brain tumor extraction and detection

Nowadays, medical image processing and particularly MRI images is the one of the most challenging field and emerging to help specialists in their diagnostics. In this context and to detect automatically suspicious regions or tumors, this paper presents a new approach called hybrid segmentation inspired by both mathematical morphology operators and morphological watershed segmentation. Our approach's advantage comes from the complementarity between these two approaches. The morphological operators extract roughly the tumor region and eventually can affect healthy structures while the watershed method provides details of various brain's structures and therefore the fusion of these two approaches improves significantly the segmentation and the extraction of the tumor zone.

[1]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[2]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[3]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[4]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[5]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[6]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[7]  Isaac N. Bankman,et al.  Handbook of medical imaging , 2000 .

[8]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[9]  Anil K. Jain,et al.  A wrapper-based approach to image segmentation and classification , 2004, IEEE Transactions on Image Processing.

[10]  Guido Gerig,et al.  Level-set evolution with region competition: automatic 3-D segmentation of brain tumors , 2002, Object recognition supported by user interaction for service robots.

[11]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[12]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[13]  Bernd Jähne,et al.  Practical handbook on image processing for scientific applications , 1997 .

[14]  Guillaume Dugas-Phocion,et al.  Segmentation d'IRM cérébrales multi-séquences et application à la sclérose en plaques. (Segmentation of multi-sequence brain mri and application to multiple slerosis) , 2006 .

[15]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[16]  T. Logeswari,et al.  An improved implementation of brain tumor detection using segmentation based on soft computing , 2010 .

[17]  L. Lukas,et al.  Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours? , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[19]  P. Sivakumar,et al.  A REVIEW ON IMAGE SEGMENTATION TECHNIQUES , 2016 .

[20]  Serge Beucher,et al.  THE WATERSHED TRANSFORMATION APPLIED TO IMAGE SEGMENTATION , 2009 .

[21]  Serge Beucher,et al.  Use of watersheds in contour detection , 1979 .

[22]  Anam Mustaqeem,et al.  An Efficient Brain Tumor Detection Algorithm Using Watershed & Thresholding Based Segmentation , 2012 .

[23]  Noel C. F. Codella,et al.  Image Segmentation Techniques , 1984 .

[24]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[25]  Ross T. Whitaker,et al.  Volumetric deformable models: active blobs , 1994, Other Conferences.

[26]  Christine Fernandez-Maloigne,et al.  Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information , 2004, Inf. Fusion.

[27]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[28]  Anne-Sophie Capelle-Laizé,et al.  Segmentation des images IRM multi-échos tridimensionnelles pour la détection des tumeurs cérébrales par la théorie de l'évidence. (Three dimensional multi-echoes MR segmentation for the detection of brain tumours by evidence theory) , 2003 .

[29]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..

[30]  Christine Fernandez-Maloigne,et al.  Segmentation of brain tumors by evidence theory: on the use of the conflict information , 2004 .

[31]  Bernd Jähne,et al.  Practical handbook on image processing for scientific and technical applications , 2004 .

[32]  Kaleem Siddiqi,et al.  Flux Maximizing Geometric Flows , 2001, ICCV.

[33]  Demetri Terzopoulos,et al.  A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[34]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[35]  Sarbani Datta,et al.  Brain Tumor Detection from Pre-Processed MR Images using Segmentation Techniques , 2011 .

[36]  Sebastien Ourselin,et al.  A New Deformable Model Using Dynamic Gradient Vector Flow and Adaptive Balloon Forces , 2003 .

[37]  Javad Alirezaie,et al.  Neural network based segmentation of magnetic resonance images of the brain , 1995, 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference Record.

[38]  Jasjit S. Suri,et al.  Segmentation techniques in the quantification of multiple sclerosis lesions in MRI , 2001 .

[39]  Thomas S. Huang,et al.  Image processing , 1971 .

[40]  Xavier Cufí,et al.  Yet Another Survey on Image Segmentation: Region and Boundary Information Integration , 2002, ECCV.

[41]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

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

[43]  Jérémy Lecoeur,et al.  Segmentation d'images cérébrales : État de l'art , 2007 .

[44]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[45]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[46]  Pierre Machart.  Morphological Segmentation , 2009 .

[47]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[48]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[49]  Elsevier Sdol,et al.  Journal of Visual Communication and Image Representation , 2009 .

[50]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[51]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[52]  Olivier Alata,et al.  Unsupervised segmentation for automatic detection of brain tumors in MRI , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[53]  Yu Jin Zhang,et al.  Evaluation and comparison of different segmentation algorithms , 1997, Pattern Recognit. Lett..

[54]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[55]  Richard L. Van Metter,et al.  Handbook of Medical Imaging , 2009 .