Discriminating dysplasia: optical tomographic texture analysis of colorectal polyps

Wenqi Li (Lead / Corresponding author), Maria Coats, Jianguo Zhang (Lead / Corresponding author), Stephen McKenna (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
455 Downloads (Pure)


Optical projection tomography enables 3-D imaging of colorectal polyps at resolutions of 5-10 μm. This paper investigates the ability of image analysis based on 3-D texture features to discriminate diagnostic levels of dysplastic change from such images, specifically, low-grade dysplasia, high-grade dysplasia and invasive cancer. We build a patch-based recognition system and evaluate both multi-class classification and ordinal regression formulations on a 90 polyp dataset. 3-D texture representations computed with a handcrafted feature extractor, random projection, and unsupervised image filter learning are compared using a bag-of-words framework. We measure performance in terms of error rates, F-measures, and ROC surfaces. Results demonstrate that randomly projected features are effective. Discrimination was improved by carefully manipulating various important aspects of the system, including class balancing, output calibration and approximation of non-linear kernels.
Original languageEnglish
Pages (from-to)57-69
Number of pages13
JournalMedical Image Analysis
Issue number1
Early online date20 Aug 2015
Publication statusPublished - Dec 2015


  • Volumetric texture
  • Optical projection tomography
  • 3-D histopathology
  • colorectal polypoid cancer


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