Ninos Konstantinos, Kostopoulos Spiros, Sidiropoulos Konstantinos, Kalatzis Ioannis, Glotsos Dimitris, Athanasiadis Emmanouil, Ravazoula Panagiota, Panayiotakis George, Economou George, Cavouras Dionisis
Department of Physics, School of Natural Sciences, University of Patras, Rio, Patras, Greece.
Anal Quant Cytopathol Histpathol. 2013 Oct;35(5):261-72.
To design a pattern recognition (PR) system for discriminating between low- and high-grade laryngeal cancer cases, employing immunohistochemically stained, for p63 expression, histopathology images.
The PR system was designed to assist in the physician's diagnosis for improving patient survival. The material comprised 55 verified cases of laryngeal cancer, 21 of low-grade and 34 of high-grade malignancy. Histopathology images were first processed for automatically segmenting p63 expressed nuclei. Fifty-two features were next extracted from the segmented nuclei, concerning nuclei texture, shape, and physical topology in the image. Those features and the Probabilistic Neural Network classifier were used to design the PR system on the multiprocessors of the Nvidia 580 GTX graphics processing unit (GPU) card using the Compute Unified Device Architecture parallel programming model and C++ programming language.
PR system performance in classifying laryngeal cancer cases as low grade and high grade was 85.7% and 94.1%, respectively. The system's overall accuracy was 90.9%, using 7 features, and its estimated accuracy to "unseen" by the system cases was 80%.
Optimum system design was feasible after employing parallel processing techniques and GPU technology. The proposed system was structured so as to function in a clinical environment, as a research tool, and with the capability of being redesigned on site when new verified cases are added to its repository.
设计一种模式识别（PR）系统，用于鉴别低级别和高级别喉癌病例，该系统采用经免疫组织化学染色检测p63表达的组织病理学图像。
该PR系统旨在辅助医生诊断以提高患者生存率。材料包括55例经证实的喉癌病例，其中21例为低级别恶性肿瘤，34例为高级别恶性肿瘤。首先对组织病理学图像进行处理，以自动分割表达p63的细胞核。接下来从分割后的细胞核中提取52个特征，涉及细胞核的纹理、形状以及图像中的物理拓扑结构。利用这些特征和概率神经网络分类器，采用计算统一设备架构并行编程模型和C++编程语言，在英伟达580 GTX图形处理单元（GPU）卡的多处理器上设计PR系统。
PR系统将喉癌病例分类为低级别和高级别的性能分别为85.7%和94.1%。该系统使用7个特征时的总体准确率为90.9%，其对系统未“见过”的病例的估计准确率为80%。
采用并行处理技术和GPU技术后，实现最佳系统设计是可行的。所提出的系统结构设计使其能够在临床环境中作为研究工具发挥作用，并且当新的经证实病例添加到其数据库时能够在现场重新设计。
