Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

Authors:
A. N. Karahaliou;I. S. Boniatis;S. G. Skiadopoulos;F. N. Sakellaropoulos;N. S. Arikidis;E. A. Likaki;G. S. Panayiotakis;L. I. Costaridou
Affiliations:
Dept. of Med. Phys., Univ. of Patras, Patras;-;-;-;-;-;-;-
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2008

Citing 0
Cited 9

Introduction to the special section on biomedical informatics

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
A review of breast tissue classification in mammograms

Proceedings of the 2011 ACM Symposium on Research in Applied Computation
A CAD system for breast cancer diagnosis using modified genetic algorithm optimized artificial neural network

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images

Computer Methods and Programs in Biomedicine
An Improved Decision Support System for Detection of Lesions in Mammograms Using Differential Evolution Optimized Wavelet Neural Network

Journal of Medical Systems
A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms

Journal of Medical Systems
Mammographical mass detection and classification using Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT) hybrid scheme

Computers in Biology and Medicine
An automatic computer-aided diagnosis system for liver tumours on computed tomography images

Computers and Electrical Engineering
A supervised method for microcalcification cluster diagnosis

Integrated Computer-Aided Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

The current study investigates texture properties of the tissue surrounding microcalcification (MC) clusters on mammograms for breast cancer diagnosis. The case sample analyzed consists of 85 dense mammographic images, originating from the digital database for screening mammography. mammograms analyzed contain 100 subtle MC clusters (46 benign and 54 malignant). The tissue surrounding MCs is defined on original and wavelet decomposed images, based on a redundant discrete wavelet transform. Gray-level texture and wavelet coefficient texture features at three decomposition levels are extracted from surrounding tissue regions of interest (ST-ROIs). Specifically, gray-level first-order statistics, gray-level cooccurrence matrices features, and Lawspsila texture energy measures are extracted from original image ST-ROIs. Wavelet coefficient first-order statistics and wavelet coefficient cooccurrence matrices features are extracted from subimages ST-ROIs. The ability of each feature set in differentiating malignant from benign tissue is investigated using a probabilistic neural network. Classification outputs of most discriminating feature sets are combined using a majority voting rule. The proposed combined scheme achieved an area under receiver operating characteristic curve (Az) of 0.989. Results suggest that MCspsila ST texture analysis can contribute to computer-aided diagnosis of breast cancer.