Computer-aided gleason grading of prostate cancer histopathological images using texton forests

Parmeshwar Khurd, Claus Bahlmann, Peter Maday, Ali Kamen, Summer Gibbs, Elizabeth M. Genega, John V. Frangioni

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

38 Citations (Scopus)

Abstract

The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.

Original languageEnglish (US)
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages636-639
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: Apr 14 2010Apr 17 2010

Other

Other7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
CountryNetherlands
CityRotterdam
Period4/14/104/17/10

Fingerprint

Neoplasm Grading
Prostatic Neoplasms
Textures
Cluster Analysis
Tumors
Prostate
Classifiers
Pixels
Personnel
Tissue
Imaging techniques
Planning
Forests
Neoplasms

Keywords

  • Gleason grading
  • Prostate cancer
  • Texture classification

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Khurd, P., Bahlmann, C., Maday, P., Kamen, A., Gibbs, S., Genega, E. M., & Frangioni, J. V. (2010). Computer-aided gleason grading of prostate cancer histopathological images using texton forests. In 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings (pp. 636-639). [5490096] https://doi.org/10.1109/ISBI.2010.5490096

Computer-aided gleason grading of prostate cancer histopathological images using texton forests. / Khurd, Parmeshwar; Bahlmann, Claus; Maday, Peter; Kamen, Ali; Gibbs, Summer; Genega, Elizabeth M.; Frangioni, John V.

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings. 2010. p. 636-639 5490096.

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

Khurd, P, Bahlmann, C, Maday, P, Kamen, A, Gibbs, S, Genega, EM & Frangioni, JV 2010, Computer-aided gleason grading of prostate cancer histopathological images using texton forests. in 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings., 5490096, pp. 636-639, 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010, Rotterdam, Netherlands, 4/14/10. https://doi.org/10.1109/ISBI.2010.5490096
Khurd P, Bahlmann C, Maday P, Kamen A, Gibbs S, Genega EM et al. Computer-aided gleason grading of prostate cancer histopathological images using texton forests. In 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings. 2010. p. 636-639. 5490096 https://doi.org/10.1109/ISBI.2010.5490096
Khurd, Parmeshwar ; Bahlmann, Claus ; Maday, Peter ; Kamen, Ali ; Gibbs, Summer ; Genega, Elizabeth M. ; Frangioni, John V. / Computer-aided gleason grading of prostate cancer histopathological images using texton forests. 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings. 2010. pp. 636-639
@inproceedings{a2590e79a18d4a85a8fce9dca6dd2851,
title = "Computer-aided gleason grading of prostate cancer histopathological images using texton forests",
abstract = "The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.",
keywords = "Gleason grading, Prostate cancer, Texture classification",
author = "Parmeshwar Khurd and Claus Bahlmann and Peter Maday and Ali Kamen and Summer Gibbs and Genega, {Elizabeth M.} and Frangioni, {John V.}",
year = "2010",
doi = "10.1109/ISBI.2010.5490096",
language = "English (US)",
isbn = "9781424441266",
pages = "636--639",
booktitle = "2010 7th IEEE International Symposium on Biomedical Imaging",

}

TY - GEN

T1 - Computer-aided gleason grading of prostate cancer histopathological images using texton forests

AU - Khurd, Parmeshwar

AU - Bahlmann, Claus

AU - Maday, Peter

AU - Kamen, Ali

AU - Gibbs, Summer

AU - Genega, Elizabeth M.

AU - Frangioni, John V.

PY - 2010

Y1 - 2010

N2 - The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.

AB - The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.

KW - Gleason grading

KW - Prostate cancer

KW - Texture classification

UR - http://www.scopus.com/inward/record.url?scp=77955217140&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77955217140&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2010.5490096

DO - 10.1109/ISBI.2010.5490096

M3 - Conference contribution

AN - SCOPUS:77955217140

SN - 9781424441266

SP - 636

EP - 639

BT - 2010 7th IEEE International Symposium on Biomedical Imaging

ER -