Automated Labeling of Cancer Textures in Larynx Histopathology Slides Using Quasi-supervised Learning

作者：Onder Devrim^*; Sarioglu Sulen; Karacali Bilge

来源：Analytical and Quantitative Cytology and Histology, 2014, 36(6): 314-+.

• classification
• histopathology
• quasi-supervised learning
• scatter matrices
• statistical learning
• texture classification

OBJECTIVE: To evaluate the performance of a quasisupervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. %26lt;br%26gt;STUDY DESIGN: Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasisupervised learning algorithm. %26lt;br%26gt;RESULTS: Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. %26lt;br%26gt;CONCLUSION: Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.

• 出版日期2014-12