CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs

by Richa Sharma, T. R. Gopalakrishnan Nair
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 2
Year of Publication: 2016
Authors: Richa Sharma, T. R. Gopalakrishnan Nair
10.5120/ijca2016910698

Richa Sharma, T. R. Gopalakrishnan Nair . Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs. International Journal of Computer Applications. 146, 2 ( Jul 2016), 35-40. DOI=10.5120/ijca2016910698

@article{ 10.5120/ijca2016910698,
author = { Richa Sharma, T. R. Gopalakrishnan Nair },
title = { Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 2 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number2/25374-2016910698/ },
doi = { 10.5120/ijca2016910698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:15.350411+05:30
%A Richa Sharma
%A T. R. Gopalakrishnan Nair
%T Studies of Suspicious Lesions through Local Texture Analysis in Spine Radiographs
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 2
%P 35-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a method to assess and classify the textural abnormalities created by the cellular changes made available through digital radiographs. In this process, digital X-ray images of the spine were subjected to investigations. A study has been made to choose the optimum set of features selectively to train the classifier. In most common applications, the feature vector is extracted from the image data using pixel intensity and magnitude of the frequency displayed as an image. In this case, the introduction of the phase angle of the frequency involved in generating the hue of the suspicious lesions considerably enhanced the success rate of the detection (approximately by 25%). Our experimental study based on 32 subjects indicates that the proposed system is successfully classifying the abnormal regions with 94.37% mean Correct Classification Ratio (CCR). Here, CCR is defined as correctly classified samples versus all the samples.

References
  1. Greef MD, Weve R, Kerkstra S, Tracking Objects using Gabor Filters, Profile Project 2006
  2. Gonzalez RC, Woods, Eddins, Digital image processing using MATLAB. Object Recognition. 2nd ed., Delhi, India, Pearson Education, 2nd ed., 498-522, 2009
  3. Collins RT, Liu Y, Leordeanu M, Online Selection of discriminating Tracking Features. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 27, No. 10, 1631-1643 (Oct.2005)
  4. Diniz PR, Murta-Junior LO, Brum DG, de Araújo DB, Santos AC, Brain tissue segmentation using q entropy, Brazilian Journal of Medical, 43(1),77-84 ( 2010), 77-84, http://dx.doi.org/10.1590/S0100-879X2009007500019
  5. Kim JK, Park HW, Statistical texture features for detection of microcalcifications in Digitized Mammograms. IEEE Transactions on medical imaging, Vol 18, No. 3, 231-238 (1999)
  6. Noor NM, Rejal OM, Yunus A, et al. A statistical interpretation of the chest radiograph for the detection of pulmonary tuberculosis. Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur,2010 IEEE EMBS, 47-51
  7. Patil SA, Udupi VR, Kane CD, Wasif AI, Geometrical and texture features estimation of lung cancer and TB images using chest X-ray database, International Conference on Biomedical and Pharmaceutical Engineering - ICBPE, 2009. doi: 10.1109/ICBPE.2009.5384113
  8. Steven W. Smith, The Scientist, and Engineer’s Guide to Digital Signal Processing, California Technical Publishing, ch. 13, 567-578, 2011.
  9. Z. Xiao, C. Guo, Y. Ming, and L. Qiang, Research on log Gabor wavelet and its application in image edge detection, International Conference on Signal Processing, Aug 2002, vol. 1, 592–595
  10. Daugman J, New methods in iris recognition. IEEE Trans. Systems, Man, Cybernetics B 37(5), 1167-1175 (2007).
  11. T.R.Gopalakrishnan Nair, Richa Sharma, Pre transplant visualization of combined images for predictive medical analysis, Journal of Medical Engineering and Technology, 38(4),220-226, (2014). doi:10.3109/03091902.2014.904015
  12. Michopoulou, S.; (2011) Image analysis for the diagnosis of MR images of the lumbar spine. Doctoral thesis, pp. 80, UCL (University College London)
  13. Jennifer S Gregory, Alison Stewart, Peter E Undrill,, David M Reid and Richard M Aspden, “Identification of hip fracture patients from radiographs using fourier analysis of the trabecular structure: a cross-sectional study”, BMC medical imaging, 2004
  14. Ojala T, Pietikäinen M, Mäenpää T, “Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns”. IEEE Transaction on pattern analysis 2002
Index Terms

Computer Science
Information Sciences

Keywords

Classification Segmentation Computer-aided detection (CAD) Spinal Tuberculosis (TB).

Powered by PhDFocusTM