OBJECTIVE: 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. STUDY DESIGN: 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. RESULTS: 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%. CONCLUSION: 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.
OBJECTIVE: 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. STUDY DESIGN: 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. RESULTS: 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%. CONCLUSION: 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.