Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2011  |  Volume : 2  |  Issue : 1  |  Page : 37

Automated vector selection of SIVQ and parallel computing integration MATLAB TM : Innovations supporting large-scale and high-throughput image analysis studies

1 Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, Michigan 48109-0602, USA
2 Department of Biomedical Engineering, Rutgers The State University of New Jersey, 599 Taylor Road, Piscataway, NJ, USA

Correspondence Address:
Ulysses J Balis
Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, Michigan 48109-0602
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.83752

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Introduction: Spatially invariant vector quantization (SIVQ) is a texture and color-based image matching algorithm that queries the image space through the use of ring vectors. In prior studies, the selection of one or more optimal vectors for a particular feature of interest required a manual process, with the user initially stochastically selecting candidate vectors and subsequently testing them upon other regions of the image to verify the vector's sensitivity and specificity properties (typically by reviewing a resultant heat map). In carrying out the prior efforts, the SIVQ algorithm was noted to exhibit highly scalable computational properties, where each region of analysis can take place independently of others, making a compelling case for the exploration of its deployment on high-throughput computing platforms, with the hypothesis that such an exercise will result in performance gains that scale linearly with increasing processor count. Methods: An automated process was developed for the selection of optimal ring vectors to serve as the predicate matching operator in defining histopathological features of interest. Briefly, candidate vectors were generated from every possible coordinate origin within a user-defined vector selection area (VSA) and subsequently compared against user-identified positive and negative "ground truth" regions on the same image. Each vector from the VSA was assessed for its goodness-of-fit to both the positive and negative areas via the use of the receiver operating characteristic (ROC) transfer function, with each assessment resulting in an associated area-under-the-curve (AUC) figure of merit. Results: Use of the above-mentioned automated vector selection process was demonstrated in two cases of use: First, to identify malignant colonic epithelium, and second, to identify soft tissue sarcoma. For both examples, a very satisfactory optimized vector was identified, as defined by the AUC metric. Finally, as an additional effort directed towards attaining high-throughput capability for the SIVQ algorithm, we demonstrated the successful incorporation of it with the MATrix LABoratory (MATLAB TM ) application interface. Conclusion: The SIVQ algorithm is suitable for automated vector selection settings and high throughput computation.

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