The week's pick
Reseach Article
Characterization of Normal Tissues in Computed Tomography using Textural Information
| International Conference and Workshop on Emerging Trends in Technology 2013 |
| Foundation of Computer Science USA |
| ICWET2013 - Number 1 |
| April 2013 |
| Authors: Savita N. Kulkarni, Niranjan T. Kulkarni |
| 1d639b40-e24e-4643-949c-89a256b74d18 |
Savita N. Kulkarni, Niranjan T. Kulkarni . Characterization of Normal Tissues in Computed Tomography using Textural Information. International Conference and Workshop on Emerging Trends in Technology 2013. ICWET2013, 1 (April 2013), 5-7.
Abstract
This paper discusses the process of developing an automated imaging system for classification of tissues in medical images obtained from typical Digital Imaging and Communication in Medicine (DICOM) format of a Computed Tomography (CT) scans. It focuses on using wavelet based multi-resolution texture analysis. The approach consist of two steps: automatic extraction of most discriminative texture features of regions of interest in CT medical images and segmentation is performed that automatically identifies the various tissues. A wavelet-based texture descriptors coupled with the implementation of a minimum distance classifier approach is carried out. Preliminary results for a 3D data set from abdomen CT scans are presented.
References
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- D. H. Xu, A. Kurani, J. D. Furst, & D. S. Raicu, "Run-length encoding for volumetric texture", The 4th IASTED International Conference on Visualization, Imaging, and Image Processing -VIIP 2004, Marbella, Spain, September 6-8, 2004.
- Kara, Bayram, and NurdalWatsuji. Using Wavelets For Texture Classification. IJCI Proceedings of International Conference on Signal Processing. ISSN 1304-2386, Columne:1, Number: 2. September 2003.
- Van de Wouwer, G. , P. Scheunders, and D. Van Dyck. Statistical Texture Characterization from Discrete Wavelet Representations. University of Antwerp: Antwerpen, Belgium Yinpeng Jin, Elsa Angelini, Andrew Laine,"Wavelets in Medical Image Processing: DE-noising, Segmentation, and Registration", Department of Biomedical Engineering, Columbia University, New York, NY, USA.
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