Title :
Classification of Urinary Sediments Image Based on Bayesian Classifier
Author :
Dong, Liyan ; Yuan, Senmiao ; Liu, Guangyuan ; Zhou, Lingyan ; Li, Yongli
Author_Institution :
Jilin Univ., Changchun
Abstract :
In this paper a new method of classification of image in medical domain was introduced. Since the traditional way of diagnosis is slow, time-consuming and heavy-workload, the result is largely influenced by the experience of doctor. The new way of classification can largely improve the efficiency and accuracy in diagnosis. The method is based on Naive Bayesian classification. After distinguishing the visible compositions, feature extraction and feature selection, we can get a naive Bayesian classifier which can be used in classifying of object entities. Experiments show that the new method is suitable for image classification.
Keywords :
belief networks; feature extraction; image classification; sediments; Bayesian classifier; feature extraction; feature selection; image classification; naive Bayesian classification; object entities; urinary sediments; Automation; Bayesian methods; Biomedical imaging; Data mining; Educational institutions; Feature extraction; Image classification; Mechatronics; Medical diagnostic imaging; Sediments; Bayesian classifier; feature extraction; feature selection; image classification;
Conference_Titel :
Mechatronics and Automation, 2007. ICMA 2007. International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0828-3
Electronic_ISBN :
978-1-4244-0828-3
DOI :
10.1109/ICMA.2007.4303603