Advance on curvelet application to prostate cancer tissue image classification

Wen Chyi Lin; Ching Chung Li; Jonathan Ira Epstein; Robert W. Veltri

doi:10.1109/ICCABS.2017.8114306

Advance on curvelet application to prostate cancer tissue image classification

Wen Chyi Lin, Ching Chung Li, Jonathan Ira Epstein, Robert W. Veltri

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

The approach of multi-resolution curvelet transform has been applied in our study of computer-aided classification of four critical Gleason patterns in prostate histological images. In the current study, we consider the maximum curvelet coefficients for the texture feature extraction to obtain more discriminative capability. A two-level classifier is re-designed, its excellent performance has been demonstrated.

Original language	English (US)
Title of host publication	2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Volume	2017-October
ISBN (Print)	9781538625941
DOIs	https://doi.org/10.1109/ICCABS.2017.8114306
State	Published - Nov 16 2017
Event	7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017 - Orlando, United States Duration: Oct 19 2017 → Oct 21 2017

Other

Other	7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
Country/Territory	United States
City	Orlando
Period	10/19/17 → 10/21/17

Keywords

Curvelets
Gleason grading
Gleason scores
maximum curvelet coefficient
prostate cancer
tissue texture classification

ASJC Scopus subject areas

Biomedical Engineering
Computational Theory and Mathematics

Access to Document

10.1109/ICCABS.2017.8114306

Cite this

Lin, W. C., Li, C. C., Epstein, J. I., & Veltri, R. W. (2017). Advance on curvelet application to prostate cancer tissue image classification. In 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017 (Vol. 2017-October). Article 8114306 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCABS.2017.8114306

Advance on curvelet application to prostate cancer tissue image classification. / Lin, Wen Chyi; Li, Ching Chung; Epstein, Jonathan Ira et al.
2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017. 8114306.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, WC, Li, CC, Epstein, JI & Veltri, RW 2017, Advance on curvelet application to prostate cancer tissue image classification. in 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017. vol. 2017-October, 8114306, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017, Orlando, United States, 10/19/17. https://doi.org/10.1109/ICCABS.2017.8114306

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abstract = "The approach of multi-resolution curvelet transform has been applied in our study of computer-aided classification of four critical Gleason patterns in prostate histological images. In the current study, we consider the maximum curvelet coefficients for the texture feature extraction to obtain more discriminative capability. A two-level classifier is re-designed, its excellent performance has been demonstrated.",

keywords = "Curvelets, Gleason grading, Gleason scores, maximum curvelet coefficient, prostate cancer, tissue texture classification",

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AU - Epstein, Jonathan Ira

AU - Veltri, Robert W.

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AB - The approach of multi-resolution curvelet transform has been applied in our study of computer-aided classification of four critical Gleason patterns in prostate histological images. In the current study, we consider the maximum curvelet coefficients for the texture feature extraction to obtain more discriminative capability. A two-level classifier is re-designed, its excellent performance has been demonstrated.

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KW - Gleason grading

KW - Gleason scores

KW - maximum curvelet coefficient

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KW - tissue texture classification

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VL - 2017-October

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Y2 - 19 October 2017 through 21 October 2017

ER -

Advance on curvelet application to prostate cancer tissue image classification

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Keywords

ASJC Scopus subject areas

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