Real-Time Image Processing in Automated Cytology
Uterine cancer is detected in its early stages by screening cells scraped from the uterus and spread on a microscope slide for visual observation. Cells are roughly screened by a cytological technician called a “screener.” A physician then makes the final diagnosis. Cyto-screening has been automated by us using image processing and pattern recognition techniques. The authors have made significant improvements in the following factors: (1) screening accuracy, (2) screening speed, and (3) sample preparation. New cytologic feature parameters and a hierarchical classification algorithm have been developed. As feature parameters we use nuclear area, nuclear area/cytoplasm area, and nuclear area with higher density. The hierarchical classification algorithm incorporates the cell growth method of Suzuki (1978). Screening speed is expected to be twice that achieved in manual screening. The authors aimed at a speed of 5 min./sample and have succeeded in developing a system with a speed of 2 min./sample.
KeywordsFeature Extraction Image Input Image Sensor Uterine Cancer Nuclear Area
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- Suzuki, R., “Automated Classification of Uterine Cancer Cells Utilizing the Concept of Cellular Differentiation,” Proc. 4th Joint Conf. on Pattern Recog. (Kyoto, 1978).Google Scholar