The Contribution of Morphological Features in the Classification of Prostate Carcinoma in Digital Pathology Images
|Title:||The Contribution of Morphological Features in the Classification of Prostate Carcinoma in Digital Pathology Images||Authors:||McCarthy, Nicholas
|Permanent link:||http://hdl.handle.net/10197/8531||Date:||28-Aug-2014||Online since:||2017-05-22T11:18:52Z||Abstract:||In this paper we present work on the development of a system for automated classification of digitized H&E histopathology images of prostate carcinoma (PCa). In our system, images are transformed into a tiled grid from which various texture and morphological features are extracted. We evaluate the contribution of high-level morphological features such as those derived from tissue segmentation algorithms as they relate to the accuracy of our classifier models. We also present work on an algorithm for tissue segmentation in image tiles, and introduce a novel feature vector representation of tissue classes in same. Finally, we present the classification accuracy, sensitivity and specificity results of our system when performing three tasks: distinguishing between cancer and non-cancer tiles, between low and high-grade cancer and between Gleason grades 3, 4 and 5. Our results show that the novel tissue representation outperforms the morphological features derived from tissue segmentation by a significant margin, but that neither feature sets improve on the accuracy gained by features from low-level texture methods.||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2014 IEEE||Keywords:||Machine learning; Statistics; Prostate cancer; Image color analysis||DOI:||10.1109/ICPR.2014.563||Language:||en||Status of Item:||Peer reviewed||Conference Details:||2014 22nd International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, 24-28 August 2014|
|Appears in Collections:||Conway Institute Research Collection|
Computer Science Research Collection
Insight Research Collection
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