A machine learning approach to identify prostate cancer areas in complex histological images

Sadri Salman, Zhaoxuan Ma, Sambit Mohanty, Sanica Bhele, Yung Tien Chu, Beatrice Knudsen, Arkadiusz Gertych

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Separating benign glands, and cancer areas from stroma is one of the vital steps towards automated grading of prostate cancer in digital images of H&E preparations. In this work we present a novel tool that utilizes a supervised classification of histograms of staining components in hematoxylin and eosin images to delineate areas of benign and cancer glands. Using high resolution images of whole slide prostatectomies we compared several image classification schemes which included intensity histograms, histograms of oriented gradients, and their concatenations to the manual annotations of tissues by a pathologist, and showed that joint intensity histograms of hematoxylin and eosin components performed with the highest accuracy.

Original languageEnglish (US)
Pages (from-to)295-306
Number of pages12
JournalAdvances in Intelligent Systems and Computing
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science

Cite this