Title:
Histological image classification using biologically interpretable shape-based features
Histological image classification using biologically interpretable shape-based features
| dc.contributor.author | Kothari, Sonal | en_US |
| dc.contributor.author | Phan, John H. | en_US |
| dc.contributor.author | Young, Andrew N. | en_US |
| dc.contributor.author | Wang, May Dongmei | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | en_US |
| dc.contributor.corporatename | Emory University. School of Medicine | en_US |
| dc.contributor.corporatename | Grady Health System | en_US |
| dc.contributor.corporatename | Emory University. Dept. of Pathology and Laboratory Medicine | en_US |
| dc.date.accessioned | 2013-06-12T20:22:58Z | |
| dc.date.available | 2013-06-12T20:22:58Z | |
| dc.date.issued | 2013 | |
| dc.description | © 2013 Kothari et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.description | DOI: 10.1186/1471-2342-13-9 | en_US |
| dc.description.abstract | Background: Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. Methods: We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. Results: The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Conclusions: Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions. | en_US |
| dc.identifier.citation | Sonal Kothari, John H. Phan, Andrew N. Young, and May D. Wang, "Histological image classification using biologically interpretable shape-based features," BMC Medical Imaging, 13:9 (2013) | en_US |
| dc.identifier.doi | 10.1186/1471-2342-13-9 | |
| dc.identifier.issn | 1471-2342 | |
| dc.identifier.uri | http://hdl.handle.net/1853/47411 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | BioMed Central | en_US |
| dc.subject | Automatic cancer diagnostic systems | en_US |
| dc.subject | Histological image classification | en_US |
| dc.subject | Shape-based analysis | en_US |
| dc.subject | Biological interpretation | en_US |
| dc.subject | Clinical diagnosis | en_US |
| dc.title | Histological image classification using biologically interpretable shape-based features | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Wang, May Dongmei | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAuthorOfPublication | e8cb038f-ed3c-41d4-9159-0e51e2e069f1 | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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