Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2012  |  Volume : 3  |  Issue : 1  |  Page : 33

The analysis of image feature robustness using cometcloud


1 Department of Pathology, Centre for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
2 Department of Electrical and Computer Engineering, NSF Cloud and Autonomic Computing Centre, Rutgers, University, Piscatway, New Jersey, USA
3 Department of Biostatistics, Division of Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA
4 Department of Pathology, Centre for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, New Brunswick, Radiology, UMDNJ-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA

Correspondence Address:
Xin Qi
Department of Pathology, Centre for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, New Brunswick, New Jersey
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.101782

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The robustness of image features is a very important consideration in quantitative image analysis. The objective of this paper is to investigate the robustness of a range of image texture features using hematoxylin stained breast tissue microarray slides which are assessed while simulating different imaging challenges including out of focus, changes in magnification and variations in illumination, noise, compression, distortion, and rotation. We employed five texture analysis methods and tested them while introducing all of the challenges listed above. The texture features that were evaluated include co-occurrence matrix, center-symmetric auto-correlation, texture feature coding method, local binary pattern, and texton. Due to the independence of each transformation and texture descriptor, a network structured combination was proposed and deployed on the Rutgers private cloud. The experiments utilized 20 randomly selected tissue microarray cores. All the combinations of the image transformations and deformations are calculated, and the whole feature extraction procedure was completed in 70 minutes using a cloud equipped with 20 nodes. Center-symmetric auto-correlation outperforms all the other four texture descriptors but also requires the longest computational time. It is roughly 10 times slower than local binary pattern and texton. From a speed perspective, both the local binary pattern and texton features provided excellent performance for classification and content-based image retrieval.


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