Research Article
Dual Tree M-Band Wavelet Transform Model based Classification of Mammogram Images
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 152 - Issue 10 |
| Published: Oct 2016 |
| Authors: C. Suba, S. Niranjanan |
10.5120/ijca2016911777
|
C. Suba, S. Niranjanan . Dual Tree M-Band Wavelet Transform Model based Classification of Mammogram Images. International Journal of Computer Applications. 152, 10 (Oct 2016), 27-32. DOI=10.5120/ijca2016911777
@article{ 10.5120/ijca2016911777,
author = { C. Suba,S. Niranjanan },
title = { Dual Tree M-Band Wavelet Transform Model based Classification of Mammogram Images },
journal = { International Journal of Computer Applications },
year = { 2016 },
volume = { 152 },
number = { 10 },
pages = { 27-32 },
doi = { 10.5120/ijca2016911777 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2016
%A C. Suba
%A S. Niranjanan
%T Dual Tree M-Band Wavelet Transform Model based Classification of Mammogram Images%T
%J International Journal of Computer Applications
%V 152
%N 10
%P 27-32
%R 10.5120/ijca2016911777
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Breast Cancer is the one of the leading causes of cancer mortality among women and second leading cause of cancer deaths worldwide after lung cancer. In the US, 1 in 8 women will be diagnosed with breast cancer in their lifetime. The proposed CAD system is implemented in MATLAB and the performance is analyzed in terms of classification accuracy. Experimental Results indicate that DTMBWT has emerged as a potentially dominant feature extraction technique for breast cancer diagnosis. The risk for breast cancer increases with age; most breast cancer are diagnosed after age 50 and about 95% of all breast cancers in the US occur in women 40 and older.
References
- www.breastcancer.org
- Brijesh Verma and Ping Zhang.”A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms”, Applied Soft Computing, no.7, pp.513-525, 2007.
- Marrocco, C., M. Molinara, C. D’Elia and F. Tortorella,2010. “A Computer Aided Detection System for Clustered microcalcifications. Artif. Intell. Med., 50:23-32. DOI: 10.1016/j.artmed.2010.04.007.
- R. Nithya and B. Santhi.” Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer, International Journal of Computer Applications (0975 – 8887), Volume 28– No.6, August 2011.
- Aidarus M. Ibrahim, Baharum Baharudin, Abas Md Said and P.N. Hashimah,” Classification of Breast Tumor in Mammogram Images Using Unsupervised Feature Learning”, Science Publications, Original Research Paper,2016.
- Mini MIAS Database Information available at: http://mammoimage.org/database..:
- Caroline Chaux , Laurent Duva and Jean-Christophe Pesquet. ”2D Dual-Tree M-Band Wavelet Decomposition”.
- Selesnick.I.W.”The Double-Density Dual-Tree DWT”, IEEE Transaction on Signal Processing, 52(5), 1304-1314, 2004.
- Cheng. H. D. & Cui. M. (2004). “Mass Lesion Detection with a Fuzzy Neural Network’s”, 37(6), 1189-1200.
- Verma. B., McLeod. P, & Klevansky. A. (2010). “Classification of Benign and Malignant Patterns in Digital Mammograms for the Diagnosis of Breast Cancer”.Expert Systems with Applications, 37(4), 3344-3351.
Index Terms
Keywords