We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis

by Bikesh Kr. Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 1
Year of Publication: 2011
Authors: Bikesh Kr. Singh
10.5120/3268-4430

Bikesh Kr. Singh . Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis. International Journal of Computer Applications. 27, 1 ( August 2011), 18-23. DOI=10.5120/3268-4430

@article{ 10.5120/3268-4430,
author = { Bikesh Kr. Singh },
title = { Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 1 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number1/3268-4430/ },
doi = { 10.5120/3268-4430 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:39.846651+05:30
%A Bikesh Kr. Singh
%T Mammographic Image Enhancement, Classification and Retrieval using Color, Statistical and Spectral Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 1
%P 18-23
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the major causes of cancer death among middle aged women in developed countries is breast cancer. Mammography is one method used by radiologists for detection and interpretation of cancer in breast images. Over the past few years Content-based Image Retrieval (CBIR) approaches has received significant attention for medical images analysis. In this paper content based retrieval techniques were tested for tissue classification and analysis of breast images. The proposed method employs image enhancement, analysis and classification of mammograms using histogram, statistical, wavelet coefficients and spectral features. The implementation of proposed method was carried out using MATLAB software and hence can work on simple personal computer. Analysis was carried out on 56 images collected from open source mini-MIAS database. Euclidean distance was used to compare the features of query image with stored images in database. Results show that the suggested features can be used for both classification and retrieval of mammographic images. The retrieval efficiency was obtained to be 85.7%.

References
  1. J. Ferlay, F. Bray, P. Pisani and D.M. Parkin. GLOBOCAN 2000: Cancer Incidence, Mortality and Prevalence Worldwide. Version 1.0. IARC Cancer Base No. 5. Lyon, IARC Press, 2001.
  2. V.D Dzung, D.T Thuan Nguyen, T.D Nguyen, T.T Nguyen and D.H Tran, “A Program For Locating Possible Breast Masses On Mammograms” Proceedings of 3rd International Conferences on the Development of BME in Vietnam, pp 109-112, 11-14 Jan 2010.
  3. Y. Ireaneus Anna Rejani, “ Early Detection of Breast Cancer using SVM Classifier Technique”, International Journal of Computer Science and Engineering Vol.1(3), pp. 127-130, 2009.
  4. B. K. Singh, G. R. Sinha, A. Wany and B. Mazumdar, Content based retrieval from biomedical images using color histograms, proceedings of International Conference on Nanotechnology and Biosensors, ICBN , Raghu College of Engg., Visakhapatnam 2010.
  5. P.L. Stanchev, D. Green Jr. and B. Dimitrov, “ High level color similarity retrieval,” Int. J. Inf. Theories Appl., 2004, pp 363–369.
  6. Y. Liu, D.S. Zhang, G. Lu and W.-Y. Ma, “Region-based image retrieval with perceptual colors, Proceedings of the Pacific-Rim Multimedia Conference (PCM),” December 2004, pp. 931–938.
  7. R. Shi, H. Feng, T.-S. Chua and C.-H. Lee, “An adaptive image content representation and segmentation approach to automatic image annotation,” International Conference on Image and Video Retrieval (CIVR), 2004, pp. 545–554.
  8. V. Mezaris, I. Kompatsiaris and M.G. Strintzis, “ontology approach to object-based image retrieval,” Proceedings of the ICIP, vol. II, 2003, pp. 511–514.
  9. B.S. Manjunath, “Color and texture descriptors,” IEEETrans. CSVT 11 (6) (2001) pp. 703–715.
  10. G. S Linda and C. S George,"Computer Vision" Prentice Hall, 2003.
  11. M. Nilsson, J. S. Bartunek, J. Nordberg and I. Claesson, “On Histograms and Spatiograms - Introduction of the Mapogram,” ICIP, 2008, page(s): 973-976.
  12. S.T. Birchfield, S, Rangarajan, “Spatiograms versus Histograms for Region-Based Tracking,” 2005, In CVPR’05.
  13. G. S Linda and C. S George,"Computer Vision" Prentice Hall, 2001.
  14. M. Stricker and M. Swain, “The Capacity of Color Histogram Indexing,” Computer Vision and Pattern Recognition, Proceedings CVPR IEEE Computer Society Conference, 1994, pp. 704 – 708.
  15. R. C Gonzalez, R. E Woods and S. L. Eddins, Digital Image Processing using Matlab, Pearson Education, 2006.
  16. J Suckling et al (1994): The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 pp375-378.
Index Terms

Computer Science
Information Sciences

Keywords

Mammograms image processing spectral and statistical features wavelet coefficients

Powered by PhDFocusTM