Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading

Hong Jun Yoon; Ching Chung Li; Christhunesa Christudass; Robert Veltri; Jonathan I. Epstein; Zhen Zhang

doi:10.1109/BIBM.2011.59

Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading

Hong Jun Yoon, Ching Chung Li, Christhunesa Christudass, Robert Veltri, Jonathan I. Epstein, Zhen Zhang

School of Medicine

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

15 Scopus citations

Abstract

Computer-aided Gleason grading of prostate cancer tissue images has been in rapid development during the past decade. Automated classifiers using features derived from multi wavelet transform, fractal dimension and other measurements, and using text on forests have shown considerable successes. This paper presents our study on application of cardinal multiridgelet transform (CMRT) to prostate cancer images to extract texture features in the transform domain. CMRT can provide cardinality, approximate translation invariance and rotation invariance simultaneously. With 32 images of Gleason grade 3 and grade4 as a training set and using texture features extracted there from, a support vector machine with Gaussian kernel has been trained to classify grade 3 and grade 4. The leave-one-outcross-validation showed its accuracy of 93.75% and AUC of0.9651. 10 test images of grade 4 showed 100% accuracy.

Original language	English (US)
Title of host publication	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Pages	315-320
Number of pages	6
DOIs	https://doi.org/10.1109/BIBM.2011.59
State	Published - 2011
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 - Atlanta, GA, United States Duration: Nov 12 2011 → Nov 15 2011

Publication series

Name	Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011

Other

Other	2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011
Country/Territory	United States
City	Atlanta, GA
Period	11/12/11 → 11/15/11

Keywords

Complex Ridgelets
Gleason Grading
Multiridgelets
Multiwavelets
Prostate Cancer
Ridgelets
Tissue Texture Classification
Wavelets

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.1109/BIBM.2011.59

Cite this

Yoon, H. J., Li, C. C., Christudass, C., Veltri, R., Epstein, J. I., & Zhang, Z. (2011). Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading. In Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011 (pp. 315-320). Article 6120459 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011). https://doi.org/10.1109/BIBM.2011.59

Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading. / Yoon, Hong Jun; Li, Ching Chung; Christudass, Christhunesa et al.
Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. p. 315-320 6120459 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011).

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

Yoon, HJ, Li, CC, Christudass, C, Veltri, R, Epstein, JI & Zhang, Z 2011, Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading. in Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011., 6120459, Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, pp. 315-320, 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBM.2011.59

Yoon HJ, Li CC, Christudass C, Veltri R, Epstein JI, Zhang Z. Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading. In Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. p. 315-320. 6120459. (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011). doi: 10.1109/BIBM.2011.59

Yoon, Hong Jun ; Li, Ching Chung ; Christudass, Christhunesa et al. / Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading. Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011. 2011. pp. 315-320 (Proceedings - 2011 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011).

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abstract = "Computer-aided Gleason grading of prostate cancer tissue images has been in rapid development during the past decade. Automated classifiers using features derived from multi wavelet transform, fractal dimension and other measurements, and using text on forests have shown considerable successes. This paper presents our study on application of cardinal multiridgelet transform (CMRT) to prostate cancer images to extract texture features in the transform domain. CMRT can provide cardinality, approximate translation invariance and rotation invariance simultaneously. With 32 images of Gleason grade 3 and grade4 as a training set and using texture features extracted there from, a support vector machine with Gaussian kernel has been trained to classify grade 3 and grade 4. The leave-one-outcross-validation showed its accuracy of 93.75% and AUC of0.9651. 10 test images of grade 4 showed 100% accuracy.",

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Cardinal multiridgelet-based prostate cancer histological image classification for Gleason grading

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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