



ORIGINAL ARTICLE J Pathol Inform 2011, 2:13

Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology



Jason D Hipp1, Jerome Y Cheng1, Mehmet Toner2, Ronald G Tompkins2, Ulysses J Balis1

1 Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, MI 48109-0602, USA

2 Massachusetts General Hospital, Harvard Medical School, 16th Street Building 114, Charlestown, MA 02129, USA



Date of Submission 13-Dec-2010 Date of Acceptance 15-Jan-2011 Date of Web Publication 26-Feb-2011

Correspondence Address:

Ulysses J Balis

Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, MI 48109-0602

USA

Source of Support: None, Conflict of Interest: None Check 11

DOI: 10.4103/2153-3539.77175



Abstract

Introduction: Historically, effective clinical utilization of image analysis and pattern recognition algorithms in pathology has been hampered by two critical limitations: 1) the availability of digital whole slide imagery data sets and 2) a relative domain knowledge deficit in terms of application of such algorithms, on the part of practicing pathologists. With the advent of the recent and rapid adoption of whole slide imaging solutions, the former limitation has been largely resolved. However, with the expectation that it is unlikely for the general cohort of contemporary pathologists to gain advanced image analysis skills in the short term, the latter problem remains, thus underscoring the need for a class of algorithm that has the concurrent properties of image domain (or organ system) independence and extreme ease of use, without the need for specialized training or expertise. Results: In this report, we present a novel, general case pattern recognition algorithm, Spatially Invariant Vector Quantization (SIVQ), that overcomes the aforementioned knowledge deficit. Fundamentally based on conventional Vector Quantization (VQ) pattern recognition approaches, SIVQ gains its superior performance and essentially zero-training workflow model from its use of ring vectors, which exhibit continuous symmetry, as opposed to square or rectangular vectors, which do not. By use of the stochastic matching properties inherent in continuous symmetry, a single ring vector can exhibit as much as a millionfold improvement in matching possibilities, as opposed to conventional VQ vectors. SIVQ was utilized to demonstrate rapid and highly precise pattern recognition capability in a broad range of gross and microscopic use-case settings. Conclusion: With the performance of SIVQ observed thus far, we find evidence that indeed there exist classes of image analysis/pattern recognition algorithms suitable for deployment in settings where pathologists alone can effectively incorporate their use into clinical workflow, as a turnkey solution. We anticipate that SIVQ, and other related class-independent pattern recognition algorithms, will become part of the overall armamentarium of digital image analysis approaches that are immediately available to practicing pathologists, without the need for the immediate availability of an image analysis expert.

Keywords: Bicubic interpolation, content-based image retrieval, continuous symmetry, digital whole slide imaging, image analysis, image vector, Nyquist sampling theory, pathology, pattern recognition, Spatially Invariant Vector Quantization, vector quantization, remote sensing

How to cite this article:

Hipp JD, Cheng JY, Toner M, Tompkins RG, Balis UJ. Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology. J Pathol Inform 2011;2:13

How to cite this URL:

Hipp JD, Cheng JY, Toner M, Tompkins RG, Balis UJ. Spatially Invariant Vector Quantization: A pattern matching algorithm for multiple classes of image subject matter including pathology. J Pathol Inform [serial online] 2011 [cited 2020 Sep 30];2:13. Available from: http://www.jpathinformatics.org/text.asp?2011/2/1/13/77175

Introduction

Pap smear

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Materials and Methods

Figure 1: SIVQ graphical user interface. A screen capture of the SIVQ graphical user interface is depicted. The pre-processing viewport in the upper left demonstrates the source predicate image with this window also being utilized for image navigation. The ring vector preview window, depicted slightly to the right of this viewport, allows for visual examination of the selected search predicate. Further to the right are a number of SIVQ algorithm parameter settings (e.g. vector size, quantity of sub-rings, heat map paint size feature, etc.) that allow for optimization of the algorithm's overall selectivity and sensitivity. Finally, a post-rendering window is depicted below, with it demonstrating resultant heat maps, where the quality of SIVQ-based pattern matching can be assessed



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Results

Figure 2: Rotational sampling - character recognition. This series of sub-panels depicts the rotationally invariant recognition performance of SIVQ in the predicate task of character recognition with the capital letter "A" as the exemplar



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Figure 3: Mirror symmetries - specific pattern matching. This series of sub-panels depicts the rotationally invariant and mirror-symmetry invariant recognition performance of SIVQ in the predicate task of identifying a specific unique feature (a bee), in eight possible configurations



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Figure 4: Feature extraction - satellite image/remote sensing applications. To underscore the class-independence of the SIVQ algorithm, a satellite image of Baghdad, Iraq (from Google Maps), depicted in the top panel was utilized as the image domain space. From this field of view, a single ring vector was selected, with it being specific for the central unique radial symmetry of helicopter rotor blades, as seen from above (inset, upper right). Subsequent full-field search with this predicate correctly identified the original helicopter and all three of the adjacent helicopter rotor blades, as depicted by red heat map matching events, yielding 100% sensitivity and specificity



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Figure 5: Applications to hematology. A digital slide of a bone marrow aspirate is shown. In the left panel, a single ring vector was selected such that it exhibited high specificity for immature polymorphonuclear lymphocytes (bands), with this vector being depicted in the inset at the bottom left. The far majority of bands were correctly identified (circled in red). In the right panel, a single vector was selected such that it exhibited high specificity for normoblasts (also circled in red)



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Figure 6: Breast calcifications. From a breast tissue image with micro-calcifications (blue arrows already present) (http://www.breastpathology.info/calcs_benign-3.html ), a single ring vector was selected such that it exhibited high specificity and sensitivity toward the calcifications (as depicted in the processed heat map image on the right)



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Figure 7: Identification of colon cancer. In a digital H&E stained tissue section of colonic adenocarcinoma (panel a), a single ring vector was selected such that it exhibited high specificity and sensitivity toward malignant glands (panel b). An additional single ring vector was selected such that it exhibited high specificity and sensitivity toward intervening intestinal stroma (panel c). Using combinatorial Boolean predicate calculus, the gated stromal area was subtracted from the gated malignant epithelium in panel B, yielding a hybrid vector-selection construct, in which greater sensitivity and specificity for the foreground feature was realized (panel d) than would be possible with a single vector alone



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Figure 8: Identification of microorganisms on special stained tissue sections. GMS stained tissue section in the left panel, with black structures representing hyphael fungal forms. A single ring vector was selected such that it exhibited high specificity and sensitivity toward these hyphael forms (panel at right)



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Figure 9: Stromal texture analysis. H&E stained tissue of human breast tissue with a single vector selected from an area involving the darker-stained pink stroma, as depicted in the upper right corner. A probability heat map resulting from this vector was generated, allowing for review of its general pattern matching characteristics across the overall field of view (with the rendered heat map color representing the overall quality of feature match; red being the best and blue being the worst)



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Reed-Sternberg cells

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Figure 10: SIVQ analysis of Hodgkin's disease. H&E stained tissue of a human lymph node (http://www.webpathology.com) depicting the usual morphology for Reed-Sternberg (RS) cells, with prominent cherry nucleoli ("owl's eye"). Two ring vectors were selected such that they exhibited high specificity and sensitivity toward the RS cells (as depicted at bottom left) to recognize these nuclear features (with the resultant heat map at right)



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Figure 11: Gross photo analysis of lymph nodes involved by Classical Hodgkin's Lymphoma, Nodular Sclerosis type. Gross photograph of enlarged human lymph nodes involved by Classical Hodgkin's Lymphoma, Nodular Sclerosis (left panel) with a single ring vector selected such that it exhibited high specificity and sensitivity toward fibrotic bands (bottom left inset of left panel). The resultant probability heat map (at right) demonstrated excellent spatial co-localization to uniquely fibrotic regions



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Discussion

Competing Interests

Authors' Contributions

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