| Autor: |
Karahaliou AN; Department of Medical Physics, School of Medicine, University of Patras, Patras 265 00, Greece. karahaliou.a@med.upatras.gr, Boniatis IS, Skiadopoulos SG, Sakellaropoulos FN, Arikidis NS, Likaki EA, Panayiotakis GS, Costaridou LI |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Inf Technol Biomed] 2008 Nov; Vol. 12 (6), pp. 731-8. |
| DOI: |
10.1109/TITB.2008.920634 |
| Abstrakt: |
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 Laws' 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 ( A(z)) of 0.989. Results suggest that MCs' ST texture analysis can contribute to computer-aided diagnosis of breast cancer. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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