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

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CISE.2009.5363285

Filename

5363285