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Reseach Article

Characterization of Normal Tissues in Computed Tomography using Textural Information

Published on April 2013 by Savita N. Kulkarni, Niranjan T. Kulkarni
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.

@article{
author = { Savita N. Kulkarni, Niranjan T. Kulkarni },
title = { Characterization of Normal Tissues in Computed Tomography using Textural Information },
journal = { International Conference and Workshop on Emerging Trends in Technology 2013 },
issue_date = { April 2013 },
volume = { ICWET2013 },
number = { 1 },
month = { April },
year = { 2013 },
issn = 0975-8887,
pages = { 5-7 },
numpages = 3,
url = { /proceedings/icwet2013/number1/11327-1342/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology 2013
%A Savita N. Kulkarni
%A Niranjan T. Kulkarni
%T Characterization of Normal Tissues in Computed Tomography using Textural Information
%J International Conference and Workshop on Emerging Trends in Technology 2013
%@ 0975-8887
%V ICWET2013
%N 1
%P 5-7
%D 2013
%I International Journal of Computer Applications
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
  1. D. Xu, J. Lee, D. S. Raicu, J. D. Furst, D. Channin. "Texture Classification of Normal Tissues in Computed Tomography", The 2005 Annual Meeting of the Society for Computer Applications in Radiology, Orlando, Florida, June 2-5, 2005.
  2. L. Semler, L. Dettori, J. Furst, "Wavelet-Based Texture Classification of Tissues in Computed Tomography", The 18th IEEE International Segmentation on Computer-Based Medical Systems (CBMS05), Dubin, Ireland, 2005, 265-270.
  3. Kurani, A, Xu, D. H. , Furst, J. D. , Raicu D. S. . "Co-occurrence matrices for volumetric data", The 7th IASTED International Conference on Computer Graphics and Imaging - CGIM 2004, Kauai, Hawaii, USA, in August 16- 18, 2004
  4. 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.
  5. 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.
  6. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Texture Analysis Dicom (digital Imaging And Communication In Medicine) Multi-resolution Analysis Wavelet Transform Computed Tomography