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

Assessment of Diverse Quality Metrics for Medical Images Including Mammography

by T. Venkat Narayana Rao, A. Govardhan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 4
Year of Publication: 2013
Authors: T. Venkat Narayana Rao, A. Govardhan
10.5120/14440-2593

T. Venkat Narayana Rao, A. Govardhan . Assessment of Diverse Quality Metrics for Medical Images Including Mammography. International Journal of Computer Applications. 83, 4 ( December 2013), 42-47. DOI=10.5120/14440-2593

@article{ 10.5120/14440-2593,
author = { T. Venkat Narayana Rao, A. Govardhan },
title = { Assessment of Diverse Quality Metrics for Medical Images Including Mammography },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 4 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 42-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number4/14440-2593/ },
doi = { 10.5120/14440-2593 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:58:32.752319+05:30
%A T. Venkat Narayana Rao
%A A. Govardhan
%T Assessment of Diverse Quality Metrics for Medical Images Including Mammography
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 4
%P 42-47
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the comparative analysis of various quality metrics for medical image processing. Measurement of image quality is vital for numerous image-processing applications. Image quality measurement is closely related to image resemblance and assessment in which quality is based on the differences (or similarity) between a degraded image and that of the original image i. e. unmodified image chiefly in mammographic images. We have employed simple verifiable techniques for representing the image quality rather than difficult mathematical procedures, which are costly, time consuming and observer dependent. In this paper, the images have been subjected to various degrees of blur, noise, compression , contrast levels . Based on these factors the quality has been measured in terms of metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Maximum difference (MD) including new metric of image qualities such as Structural Similarity Index Metrics (SSIM)for low cost medical image analysis.

References
  1. Z. Wang, A. C Bovik, H. R Sheikh, and E. P Simoncelli, 2004. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions of Image Processing, (April 2004), vol. 13 1-12.
  2. Zhou Wang and Alan C. Bovik, 2002. A Universal Image Quality Index. IEEE Signal Processing Letters, vol. 9, No. 3, 81-84.
  3. Z. Wang, A. C. Bovik, and L. Lu, 2002. Why is image quality assessment so difficult. In Proceedings of IEEE Int. Conf. Acoustics, Speech, and Signal Processing, (May 2002), vol. 4. 3313–3316.
  4. Eric Silva, Karen A. Panetta, Sos S. Agaian, 2007. Quantify similarity with measurement of enhancement by entropy, Proceedings: Mobile Multimedia/Image Processing for Security Applications, SPIE Security Symposium (April 2007), Vol. 6579, l.
  5. Z. Wang, H. R. Sheikh, and A. C. Bovik, 2003. Objective video quality assessment. The Handbook of Video Databases: Design and Applications, B. Furht and O. Marques, Eds. Boca Raton, FL: CRC Press,.
  6. Alan C. Brooks, Xiaonan Zhao, and Thrasyvoulos N. Pappas, 2008 . Structural Similarity Quality Metric in a Coding Context: Exploring the Space of Realistic Distortions, IEEE Transactions on image processing, (August 2008)Vol. 17, No. 8.
  7. A. M. Eskicioglu and P. S. Fisher, 1995 . Image quality measures and their performance, IEEE Trans. Commun. ,( Dec 1995), vol. 43, 2959–2965.
  8. Sonja Grgic , Mislav Grgic,and Marta Mrak, 2004. Reliability Of Objective Picture Quality Measures, Journal of Electrical Engineering, (October 2004),Vol. 55, No. 1-2.
  9. A. M. Eskicioglu and P. S. Fisher, 1995 . Image quality measures and their performance, IEEE Trans. Commun. ,( Dec 1995), vol. 43, 2959–2965.
  10. Sonja Grgic , Mislav Grgic,and Marta Mrak, 2004. Reliability Of Objective Picture Quality Measures, Journal of Electrical Engineering, (October 2004),Vol. 55, No. 1-2.
  11. Oliver, A. , Lladó, X. , Pérez, E. , J. , P. , Denton, E. , Freixenet, J. , et al. (2010). A Statistical Approach for Breast Density Segmentation. J Digit Imaging , 23, No. 5.
  12. Sankar, D. , & Thomas, T. (2010, Oct). A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms. J Digit Imaging. 2010 Oct;23(5):538-46. Epub 2009 Jul 18. , 23, N. 5, pp. 538-546.
  13. Wang, X. , Lederman, D. , Tan, J. , & Zheng, B. (2010). Computer-aided Detection: The Impact of Machine Learning Classifier and Image Feature Selection on Scheme Performance. IJIIP: International Journal of Intelligent Information Processing , 1, No 1.
  14. Schulz-Wendtland, R. , Fuchsjäger, M. , Wackerc, T. , & Hermannd, K. (2009). Digital mammography: An update. European Journal of Radiology , 72, pp. 258-265.
  15. Sampat, M. , Markey, M. , & Bovik, A. (2005). Computer-Aided Detection and Diagnosis in Mammography. In A. Bovik, Handbook of Image and Video Processing (pp. 1195-1217). Elsevier.
  16. Masala, G. (2006). Computer Aided Detection on Mammography. World Academy of Science, Engineering and Technology , 15.
  17. Khoo, L. , Taylor, P. , & Given-Wilson, R. (2005). Computer-aided Detection in the United Kingdom National Breast Screening Programme: Prospective Study. Radiology , 237, pp. 444-449.
  18. American Cancer Society; "Breast Cancer Facts & Figures", Inc. 2009-2010.
  19. H. B. Kekre, T. K. Sarode, S. M. Gharge; "Tumor Detection in Mammography Images using Vector Quantization Technique", International Journal of Intelligent Information Technology Application, 332(5):237-242. 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Image quality analysis Mean Square Error (MSE) Structural Similarity Index Metric(SSIM) Peak Signal to Noise Ratio(PSNR) Mean Absolute Error(MAE)

Powered by PhDFocusTM