CFP last date
22 April 2024
Reseach Article

Gabor Wavelet based Detection of Architectural Distortion and Mass in Mammographic Images and Classification using Adaptive Neuro Fuzzy Inference System

by U.s.ragupathy, T.saranya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 22
Year of Publication: 2012
Authors: U.s.ragupathy, T.saranya
10.5120/7100-9836

U.s.ragupathy, T.saranya . Gabor Wavelet based Detection of Architectural Distortion and Mass in Mammographic Images and Classification using Adaptive Neuro Fuzzy Inference System. International Journal of Computer Applications. 46, 22 ( May 2012), 37-40. DOI=10.5120/7100-9836

@article{ 10.5120/7100-9836,
author = { U.s.ragupathy, T.saranya },
title = { Gabor Wavelet based Detection of Architectural Distortion and Mass in Mammographic Images and Classification using Adaptive Neuro Fuzzy Inference System },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 22 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number22/7100-9836/ },
doi = { 10.5120/7100-9836 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:40:21.320320+05:30
%A U.s.ragupathy
%A T.saranya
%T Gabor Wavelet based Detection of Architectural Distortion and Mass in Mammographic Images and Classification using Adaptive Neuro Fuzzy Inference System
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 22
%P 37-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is a leading cause of cancer death among women. Many studies have shown that mammography is the most effective method for early detection of abnormalities such as microcalcification and mass. In the study on breast cancer, it is observed that architectural distortion is the most commonly missed abnormality in false-negative cases. Mass detection also poses a big challenge in detection because of its varying shape and density, as it is highly connected to the surrounding parenchymal tissue density. This paper proposes a new method for improving detection of architectural distortion and mass in mammographic images using Gabor wavelets and Adaptive Neuro-Fuzzy based classification. Segmentation of the abnormality is done using Otsu's thresholding. The segmented image is operated with Gabor filter. Feature extraction is done from the output images by forming Gray Level Co-occurrence Matrix (GLCM). Classification is done using Adaptive Neuro-Fuzzy Inference System (ANFIS). The Regions of Interest (RoI) of 40 images are used for training and testing using ANFIS. The sensitivity obtained is about 80% in case of images with architectural distortion and 50% in case of images with mass. The specificity obtained is about 83% for both the cases.

References
  1. JinshanTang, Rangaraj, M. Rangayyan, Jun Xu, Issam El Naga and Yongyi Yang, 2009, "Computer Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances", IEEE Trans on Information Technology in Biomedicine, vol. 13, no. 2.
  2. Fabio Jose Ayres and Rangaraj M. Rangayan, "Characterization of Architectural Distortion in Mammograms", Proceeding of the 25th Annual International Conference on IEEE EMBS
  3. Shantanu Banik, Rangaraj M. Rangayyan and J. E. Leo Desautels, 2011, "Detection of Architectural Distortion in Prior Mammograms", IEEE Trans . Med. Imag, vol. 30, no. 2.
  4. M. P. Sampat and A. C. Bovik, "Detection of Spiculated Lesions in Mammograms", 2003, in Proc. 25th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. , vol. 1, pp. 810–813.
  5. Fritz Albregsten, "Statistical Texture Measures Computed from Gray Level Co-occurrence Matrices", Image Processing Laboratory, Department of Informatics, University of Oslo.
  6. Jyh-Shing Roger Jang, 1993, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Trans on Systems, Man and Cybernetics, vol. 23, no. 3.
  7. M. P. Sampat, M. K. Markey, and A. C. Bovik, 2005, "Computer-aided detection and diagnosis in mammography", in Handbook of Image and Video Processing, A. C. Bovik, Ed. , 2nd Ed. New York: Academic,pp. 1195–1217.
  8. Robin N. Strickland, 2002, "Image Processing Techniques for Tumor Detection", Marcel Dekker Inc. New York.
  9. Joan Buciu A. Gacsadi, 2009, "Gabor Wavelet Based Features for Medical Image Analysis and Classification".
  10. Nobuyuki Otsu, 1979,"A Threshold Selection Method from Gray-Level Histograms", IEEE Trans. Systems, Man and Cybernetics, vol. 9, no. 1.
  11. Sickles E. A. , 1984, "Mammographic features of early breast cancer", Am. J. Roentgenol. , Vol 143, pp. 451-464.
  12. S. Liu, C. F. Babbs, and E. J. Delp, 2001, "Multiresolution detection of spiculated lesions in digital mammograms", IEEE Trans. Image Process. , vol. 10, no. 6, pp. 874–884.
  13. E. Catanzarti, G. Forni, A. Lauria, R. Prevete and M. Santoro, 2004, "A CAD System for the Detection of Mammographic Microcalcification Based on Gabor Transform".
  14. R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels A. F. Frere, 2004, "Analysis of Asymmetry in Mammograms via Directional Filtering with Gabor Wavelets", IEEE Trans. Med. Imag, vol. 20, no. 9.
  15. U. S. Ragupathy,A. Tamilarasi and S. Chenthur Pandian, 2012, "Improved Techniques for Mammographic Image Compression using Balanced Multiwavelet Block Tree Coding", IETE Journal of Research,vol. 58.
Index Terms

Computer Science
Information Sciences

Keywords

Anfis Architectural Distortion Breast Cancer Gabor Wavelet Mammography Mass