JBiSE  Vol.2 No.1 , February 2009
Texture feature based automated seeded region growing in abdominal MRI segmentation
Abstract: A new texture feature-based seeded region growing algorithm is proposed for automated segmentation of organs in abdominal MR images. 2D Co-occurrence texture feature, Gabor texture feature, and both 2D and 3D Semi- variogram texture features are extracted from the image and a seeded region growing algorithm is run on these feature spaces. With a given Region of Interest (ROI), a seed point is automatically se-lected based on three homogeneity criteria. A threshold is then obtained by taking a lower value just before the one causing ‘explosion’. This algorithm is tested on 12 series of 3D ab-dominal MR images.
Cite this paper: nullWu, J. , Poehlman, S. , Noseworthy, M. and V. Kamath, M. (2009) Texture feature based automated seeded region growing in abdominal MRI segmentation. Journal of Biomedical Science and Engineering, 2, 1-8. doi: 10.4236/jbise.2009.21001.

[1]   P. Maillard, (2001) "Developing methods of texture analysis in high resolution images of the Earth? X Simpósio Brasileiro de Sensoriamento Remoto, São Paulo-SP: Fábrica da Imagem. 1-11.

[2]   T.-Y. Law and P. A. Heng, (2000) “Automated extraction of bronchus from 3-D CT images of lung based on genetic algo-rithm and 3-D region growing”, Proc. SPIE 3979, Medical Im-aging 2000: Image Processing, 906-916.

[3]   R. Susomboon, D. S. Raicu, and J. D. Furst, (2006)“Pixel-Based Texture Classification of Tissues in Computed Tomography”, CTI Research Symposium, Chicago, April 2006.

[4]   J. E. Koss, F. D. Newman, T. K. Johnson, D. L. Kirch, (1999) “Abdominal organ segmentation using texture transform and a Hopfield neural network”, IEEE Trans. Medical Imaging, Vol.18, 640-648.

[5]   R. M. Haralick, K. Shanmugam, and I. Dinstein, (1973) “Texture Features for Image Classification”, IEEE Trans. On Systems, Man, and Cybernetics, Vol. Smc-3, No.6, 610-621.

[6]   R. Adams, L Bischof, (1994) “Seeded region growing”. IEEE Trans. Pattern Anal. Machine Intell. 16, 641-647.

[7]   N. A. Mat-Isa, M. Y. Mashor & N. H. Othman, (2005) “Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer”. International Journal of the Computer, the Internet and Management. (ISSN No: 0858-7027). Vol. 13. No. 1. January-April.

[8]   Y. Tuduki, K. Murase, M. Izumida, H. Miki, K. Kikuchi, K. Murakami & J. Ikezoe (2000). “Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data”. Proceedings of The 22nd Annual International. Conference of the IEEE Engineering in Medicine and Biology Society. 3. 1756-1759.

[9]   P. A. Venkatachalam, U. K.Ngah, A. F. M. Hani& A. Y. M. Shakaff, (2002). “Seed Based Region Growing Technique in Breast Cancer Detection and Embedded Expert System”. Pro-ceedings of International Conference on Artificial Intelligence in En-gineering and Technology. 464-469.

[10]   V. A. Kovalev, F. Kruggel, H.-J Gertz, and D.Y. von Cramon. (2001) “Three-dimensional texture analysis of MRI brain data-sets.” IEEE Trans. on Medical Imaging, 20(5): 424-433.

[11]   S. A. Karkanis, et al., (1999) “Detecting abnormalities in colono-scopic images by texture descriptors and neural networks,” Proc. of the Workshop Machine Learning in Med. App., 59-62.

[12]   A. Madabhushi, M. Feldman, D. Metaxas, D. Chute, and J. Tomaszewski. (2003) “A novel stochastic combination of 3D tex-ture features for automated segmentation of prostatic adenocarci-noma from high resolution MRI.” Medical Image Computing and Computer-Assisted Intervention, volume 2878 of Lecture Notes in Computer Science, pp. 581-591. Springer-Verlag.

[13]   M. Kalinin, D. S. Raicu, J. Furst, and D. S. Channin, “A classification Approach for anatomical regions segmentation”, IEEE Int. Conf. on Image Processing, 2005.

[14]   B. W. Whitney, N. J. Backman, J. D. Furst, D. S. Raicu, (2006) “Single click volumetric segmentation of abdominal organs in Computed Tomography images”, Proceedings of SPIE Medical Imaging Conference, San Diego, CA, Februar.

[15]   D. Zhang, A. Wong, M. Indrawan, G. Lu, (1990) Content-Based Image Retrieval Using Gabor Texture Features, IEEE Transac-tions PAMI, 12:7, 629-639.

[16]   J. Wu, S. Poehlman, M. D. Noseworthy, M. Kamath, (2008) Texture Feature based Automated Seeded Region Growing in Abdominal MRI Segmentation, 2008 International Conference on Biomedical Engineering and Informatics, Sanya, China, May 27-30.