Cheng Jerome, Hipp Jason, Monaco James, Lucas David R, Madabhushi Anant, Balis Ulysses J
Department of Pathology, University of Michigan Health System, M4233A Medical Science I, 1301 Catherine, Ann Arbor, Michigan 48109-0602.
J Pathol Inform. 2011;2:37. doi: 10.4103/2153-3539.83752. Epub 2011 Aug 13.
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.
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.
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™) application interface.
The SIVQ algorithm is suitable for automated vector selection settings and high throughput computation.
空间不变矢量量化（SIVQ）是一种基于纹理和颜色的图像匹配算法，它通过使用环形矢量来查询图像空间。在先前的研究中，为特定感兴趣特征选择一个或多个最优矢量需要手动过程，用户最初随机选择候选矢量，随后在图像的其他区域对其进行测试，以验证矢量的敏感性和特异性属性（通常通过查看生成的热图）。在进行先前的工作时，人们注意到SIVQ算法具有高度可扩展的计算属性，其中每个分析区域都可以独立于其他区域进行，这使得探索其在高通量计算平台上的部署成为一个有吸引力的案例，假设这样的操作将带来与处理器数量增加成线性比例的性能提升。
开发了一种自动过程，用于选择最优环形矢量，以作为定义感兴趣的组织病理学特征的谓词匹配算子。简而言之，候选矢量从用户定义的矢量选择区域（VSA）内的每个可能坐标原点生成，随后与同一图像上用户确定的正、负“真值”区域进行比较。通过使用接收器操作特征（ROC）传递函数，对VSA中的每个矢量与正、负区域的拟合优度进行评估，每次评估都会产生一个相关的曲线下面积（AUC）品质因数。
在两个应用案例中展示了上述自动矢量选择过程的使用：第一，识别恶性结肠上皮；第二，识别软组织肉瘤。对于这两个例子，如AUC指标所定义的，都确定了一个非常令人满意的优化矢量。最后，作为为SIVQ算法实现高通量能力的额外努力，我们展示了它与MATrix LABoratory（MATLAB™）应用接口的成功整合。
SIVQ算法适用于自动矢量选择设置和高通量计算。
