Semi-Automatic Segmentation and Classification of Pap Smear Cellsстатья из журнала
Аннотация: Cytologic screening has been widely used for detecting the cervical cancers. In this study, a semiautomatic PC-based cellular image analysis system was developed for segmenting nuclear and cytoplasmic contours and for computing morphometric and textual features to train support vector machine (SVM) classifiers to classify four different types of cells and to discriminate dysplastic from normal cells. A software program incorporating function, including image reviewing and standardized denomination of file names, was also designed to facilitate and standardize the workflow of cell analyses. Two experiments were conducted to verify the classification performance. The cross-validation results of the first experiment showed that average accuracies of 97.16% and 98.83%, respectively, for differentiating four different types of cells and in discriminating dysplastic from normal cells have been achieved using salient features (8 for four-cluster and 7 for two-cluster classifiers) selected with SVM recursive feature addition. In the second experiment, 70% (837) of the cell images were used for training and 30% (361) for testing, achieving an accuracy of 96.12% and 98.61% for four-cluster and two-cluster classifiers, respectively. The proposed system provides a feasible and effective tool in evaluating cytologic specimens.
Год издания: 2013
Авторы: Yung-Fu Chen, Po-Chi Huang, Ker-Cheng Lin, Hsuan‐Hung Lin, Li-En Wang, Chung-Chuan Cheng, Tsung‐Po Chen, Yung‐Kuan Chan, John Y. Chiang
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Journal of Biomedical and Health Informatics
Ключевые слова: AI in cancer detection, Digital Imaging for Blood Diseases, Cell Image Analysis Techniques
Другие ссылки: IEEE Journal of Biomedical and Health Informatics (HTML)
PubMed (HTML)
PubMed (HTML)
Открытый доступ: closed
Том: 18
Выпуск: 1
Страницы: 94–108