LBP-TOP for volume lesion classification in breast DCE-MRI

Gabriele Piantadosi, Roberta Fusco, Antonella Petrillo, Mario Sansone, Carlo Sansone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages647-657
Number of pages11
Volume9279
ISBN (Print)9783319232300
DOIs
Publication statusPublished - 2015
Event18th International Conference on Image Analysis and Processing, ICIAP 2015 - Genoa, Italy
Duration: Sep 7 2015Sep 11 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9279
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Image Analysis and Processing, ICIAP 2015
CountryItaly
CityGenoa
Period9/7/159/11/15

Fingerprint

Magnetic Resonance Imaging
Computer aided diagnosis
Computer-aided Diagnosis
Binary
Classifiers
Classifier
Texture Feature
Random Forest
Orthogonal Projection
Region of Interest
Breast Cancer
Diagnostics
Computer systems
Textures
Motion

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

Piantadosi, G., Fusco, R., Petrillo, A., Sansone, M., & Sansone, C. (2015). LBP-TOP for volume lesion classification in breast DCE-MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9279, pp. 647-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9279). Springer Verlag. https://doi.org/10.1007/978-3-319-23231-7_58

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 proceedingConference contribution

Piantadosi, G, Fusco, R, Petrillo, A, Sansone, M & Sansone, C 2015, LBP-TOP for volume lesion classification in breast DCE-MRI. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9279, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9279, Springer Verlag, pp. 647-657, 18th International Conference on Image Analysis and Processing, ICIAP 2015, Genoa, Italy, 9/7/15. https://doi.org/10.1007/978-3-319-23231-7_58
Piantadosi G, Fusco R, Petrillo A, Sansone M, Sansone C. LBP-TOP for volume lesion classification in breast DCE-MRI. In 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)). https://doi.org/10.1007/978-3-319-23231-7_58
Piantadosi, Gabriele ; Fusco, Roberta ; Petrillo, Antonella ; Sansone, Mario ; Sansone, Carlo. / LBP-TOP for volume lesion classification in breast DCE-MRI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9279 Springer Verlag, 2015. pp. 647-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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