Title :
Application of Multi-Classification Support Vector Machine in the B-Placenta Image Classification
Author :
Liu, Zhi ; Zheng, Herong ; Lin, Shengliang
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
In this paper, B-placenta image is classified automatically using support vector machine based on feature extraction. Firstly, artificial selected region of interest (ROI) is regarded as the object of feature extraction. Then traditional gray-scale statistical analysis is used to extract the characteristic parameters of B-placenta image as the basis data for the placenta classification. The binary tree multi-classification SVM is used to automatically classify the B-placenta image. The binary tree generation algorithm is optimized based on the ultra-radius. The experiment shows that this classifying method using binary tree multi-classification SVM in the B-placenta image classification has very high value.
Keywords :
biomedical ultrasonics; feature extraction; image classification; medical image processing; statistical analysis; support vector machines; trees (mathematics); B-placenta image classification; binary tree generation algorithm; feature extraction; gray-scale statistical analysis; multi classification support vector machine; region of interest; ultrasound placenta image classification; Application software; Binary trees; Classification tree analysis; Feature extraction; Fetus; Image classification; Statistical analysis; Support vector machine classification; Support vector machines; Ultrasonic imaging;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5363285
