Computer-Aided Evaluation of Screening Mammograms Based on Local Texture Models

2009 ◽  
Vol 18 (4) ◽  
pp. 765-773 ◽  
Author(s):  
JirÍ Grim ◽  
Petr Somol ◽  
Michal Haindl ◽  
Jan Danes
Keyword(s):  
Local Texture ◽  
Computer Aided ◽  
Screening Mammograms

Screening Mammograms: Interpretation with Computer-aided Detection—Prospective Evaluation

Radiology ◽  
2006 ◽  
Vol 239 (2) ◽  
pp. 375-383 ◽  
Author(s):  
Marilyn J. Morton ◽  
Dana H. Whaley ◽  
Kathleen R. Brandt ◽  
Kimberly K. Amrami
Keyword(s):  
Computer Aided Detection ◽  
Prospective Evaluation ◽  
Computer Aided ◽  
Screening Mammograms

Can Computer-aided Detection with Double Reading of Screening Mammograms Help Decrease the False-Negative Rate? Initial Experience

Radiology ◽  
2004 ◽  
Vol 232 (2) ◽  
pp. 578-584 ◽  
Author(s):  
Stamatia V. Destounis ◽  
Patricia DiNitto ◽  
Wende Logan-Young ◽  
Ermelinda Bonaccio ◽  
Margarita L. Zuley ◽  
...  
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Initial Experience ◽  
Computer Aided Detection ◽  
Double Reading ◽  
Negative Rate ◽  
Computer Aided ◽  
Screening Mammograms

scholarly journals A Computer-Aided Diagnosis System for Breast Cancer Using Independent Component Analysis and Fuzzy Classifier

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Ikhlas Abdel-Qader ◽  
Fadi Abu-Amara
Keyword(s):  
Breast Cancer ◽  
Independent Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Computer Aided Detection ◽  
Fuzzy Classifier ◽  
Novel Approach ◽  
Computer Aided ◽  
Screening Mammograms

Screening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3–4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.

Effect of Computer-Aided Detection on Independent Double Reading of Paired Screen-Film and Full-Field Digital Screening Mammograms

2007 ◽  
Vol 188 (2) ◽  
pp. 377-384 ◽  
Author(s):  
Per Skaane ◽  
Ashwini Kshirsagar ◽  
Sandra Stapleton ◽  
Kari Young ◽  
Ronald A. Castellino
Keyword(s):  
Computer Aided Detection ◽  
Double Reading ◽  
Full Field ◽  
Computer Aided ◽  
Screening Mammograms
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