A new set of wavelet-and fractals-based features for gleason grading of prostate cancer histopathology images

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

21 Scopus citations


Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationAlgorithms and Systems XI
StatePublished - 2013
Externally publishedYes
EventImage Processing: Algorithms and Systems XI - Burlingame, CA, United States
Duration: Feb 4 2013Feb 6 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceImage Processing: Algorithms and Systems XI
Country/TerritoryUnited States
CityBurlingame, CA


  • Gleason grading
  • Haar wavelet features
  • Prostate cancer
  • SVM classification
  • color fractal dimension

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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