Computer-Aided Prostate Cancer Diagnosis from Digitized Histopathology: A Review on Texture-Based Systems

Clara Mosquera-Lopez, Sos Agaian, Alejandro Velez-Hoyos, Ian Thompson

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


Prostate cancer (PCa) is currently diagnosed by microscopic evaluation of biopsy samples. Since tissue assessment heavily relies on the pathologists level of expertise and interpretation criteria, it is still a subjective process with high intra-and interobserver variabilities. Computer-aided diagnosis (CAD) may have a major impact on detection and grading of PCa by reducing the pathologists reading time, and increasing the accuracy and reproducibility of diagnosis outcomes. However, the complexity of the prostatic tissue and the large volumes of data generated by biopsy procedures make the development of CAD systems for PCa a challenging task. The problem of automated diagnosis of prostatic carcinoma from histopathology has received a lot of attention. As a result, a number of CAD systems, have been proposed for quantitative image analysis and classification. This review aims at providing a detailed description of selected literature in the field of CAD of PCa, emphasizing the role of texture analysis methods in tissue description. It includes a review of image analysis tools for image preprocessing, feature extraction, classification, and validation techniques used in PCa detection and grading, as well as future directions in pursuit of better texture-based CAD systems.

Original languageEnglish (US)
Article number6857992
Pages (from-to)98-113
Number of pages16
JournalIEEE Reviews in Biomedical Engineering
StatePublished - 2015
Externally publishedYes


  • Computer-aided diagnosis (CAD)
  • gleason grading
  • histopathology image analysis
  • pattern recognition
  • prostate cancer
  • texture-based CAD systems

ASJC Scopus subject areas

  • Biomedical Engineering


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