Abstract
In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 684-692 |
| Number of pages | 9 |
| Volume | 7626 LNCS |
| DOIs | |
| Publication status | Published - 2012 |
| Event | Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012 - Hiroshima, Japan Duration: Nov 7 2012 → Nov 9 2012 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7626 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012 |
|---|---|
| Country | Japan |
| City | Hiroshima |
| Period | 11/7/12 → 11/9/12 |
Fingerprint
Keywords
- breast DCE-MRI
- morphological and dynamic features
- multiple classification system
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
A multiple classifier system for classification of breast lesions using dynamic and morphological features in DCE-MRI. / Fusco, Roberta; Sansone, Mario; Petrillo, Antonella; Sansone, Carlo.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7626 LNCS 2012. p. 684-692 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7626 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A multiple classifier system for classification of breast lesions using dynamic and morphological features in DCE-MRI
AU - Fusco, Roberta
AU - Sansone, Mario
AU - Petrillo, Antonella
AU - Sansone, Carlo
PY - 2012
Y1 - 2012
N2 - In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.
AB - In this paper we propose a Multiple Classifier System (MCS) for classifying breast lesions in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). The proposed MCS combines the results of two classifiers trained with dynamic and morphological features respectively. Twenty-one malignant and seventeen benign breast lesions, histologically proven, were analyzed. Volumes of Interest (VOIs) have been automatically extracted via a segmentation procedure assessed in a previous study. The performance of the MCS have been compared with histological classification. Results indicated that with automatic segmented VOIs 90% of test-set lesions were correctly classified.
KW - breast DCE-MRI
KW - morphological and dynamic features
KW - multiple classification system
UR - http://www.scopus.com/inward/record.url?scp=84868146605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868146605&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34166-3_75
DO - 10.1007/978-3-642-34166-3_75
M3 - Conference contribution
AN - SCOPUS:84868146605
SN - 9783642341656
VL - 7626 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 684
EP - 692
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -