Abstract
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a complementary diagnostic method for early detection of breast cancer. However, due to the large amount of information, DCEMRI data can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of segmented regions of interest according to their aggressiveness. While there is a certain amount of evidence that dynamic information can be suitably used for lesion classification, it still remains unclear whether other kinds of features (e.g. texture-based) can add useful information. This pushes the exploration of new features coming from different research fields such as Local Binary Pattern (LBP) and its variants. In particular, in this work we propose to use LBP-TOP (Three Orthogonal Projections) for the assessment of lesion malignancy in breast DCEMRI. Different classifiers as well as the influence of a motion correction technique have been considered. Our results indicate an improvement by using LPB-TOP in combination with a Random Forest classifier (84.6% accuracy) with respect to previous findings in literature.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 647-657 |
| Number of pages | 11 |
| Volume | 9279 |
| ISBN (Print) | 9783319232300 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy Duration: Sep 7 2015 → Sep 11 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9279 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 18th International Conference on Image Analysis and Processing, ICIAP 2015 |
|---|---|
| Country | Italy |
| City | Genoa |
| Period | 9/7/15 → 9/11/15 |
Fingerprint
Keywords
- 4D Volume
- DCE-MRI
- Dynamic features
- LBP-TOP
- Lesion classification
- Random forest
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
LBP-TOP for volume lesion classification in breast DCE-MRI. / Piantadosi, Gabriele; Fusco, Roberta; Petrillo, Antonella; Sansone, Mario; Sansone, Carlo.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9279 Springer Verlag, 2015. p. 647-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9279).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - LBP-TOP for volume lesion classification in breast DCE-MRI
AU - Piantadosi, Gabriele
AU - Fusco, Roberta
AU - Petrillo, Antonella
AU - Sansone, Mario
AU - Sansone, Carlo
PY - 2015
Y1 - 2015
N2 - Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a complementary diagnostic method for early detection of breast cancer. However, due to the large amount of information, DCEMRI data can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of segmented regions of interest according to their aggressiveness. While there is a certain amount of evidence that dynamic information can be suitably used for lesion classification, it still remains unclear whether other kinds of features (e.g. texture-based) can add useful information. This pushes the exploration of new features coming from different research fields such as Local Binary Pattern (LBP) and its variants. In particular, in this work we propose to use LBP-TOP (Three Orthogonal Projections) for the assessment of lesion malignancy in breast DCEMRI. Different classifiers as well as the influence of a motion correction technique have been considered. Our results indicate an improvement by using LPB-TOP in combination with a Random Forest classifier (84.6% accuracy) with respect to previous findings in literature.
AB - Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a complementary diagnostic method for early detection of breast cancer. However, due to the large amount of information, DCEMRI data can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of segmented regions of interest according to their aggressiveness. While there is a certain amount of evidence that dynamic information can be suitably used for lesion classification, it still remains unclear whether other kinds of features (e.g. texture-based) can add useful information. This pushes the exploration of new features coming from different research fields such as Local Binary Pattern (LBP) and its variants. In particular, in this work we propose to use LBP-TOP (Three Orthogonal Projections) for the assessment of lesion malignancy in breast DCEMRI. Different classifiers as well as the influence of a motion correction technique have been considered. Our results indicate an improvement by using LPB-TOP in combination with a Random Forest classifier (84.6% accuracy) with respect to previous findings in literature.
KW - 4D Volume
KW - DCE-MRI
KW - Dynamic features
KW - LBP-TOP
KW - Lesion classification
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=84944768884&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944768884&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23231-7_58
DO - 10.1007/978-3-319-23231-7_58
M3 - Conference contribution
AN - SCOPUS:84944768884
SN - 9783319232300
VL - 9279
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 647
EP - 657
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -