计算机工程与应用2013，Vol.49Issue(2)：211-218,8.DOI:10.3778/j.issn.1002-8331.1205-0397
分维和孔隙度在肝癌超声纹理识别中协同作用
Synergy between fractal dimension and lacunarity discriminating liver cancer form normal liver from ultrasonic images
季桂树 1禹智夫2
作者信息
- 1. 中南大学地球科学与信息物理学院,长沙410083
- 2. 中南大学信息科学与工程学院,长沙410083
- 折叠
摘要
Abstract
The performance of describing ultrasonic liver cancer image texture feature with fractal and lacunarity and their combined factors is studied. The fractal dimension and lacunarity values are estimated with 4 fractal dimension and a lacunarity methods based on the samples of 14 ultrasonic images each for normal liver and liver cancer. ROC (Receiver Operating Characteristic) analysis shows that the single factors for FPS and LBCM hold higher AUCs(area under ROC curve). The train and test results for the single factors and the combined factors with SVM( Support Vector Machine)exhibit that FPS( Fourier Power Spectrum) + LBCM (Lacunarity of Box Column Mean) (4 kemals) and DBC (Differential Box Counting) + LBCM (except SIG-MOID) get higher classification accuracy rate than the single factors.关键词
肝癌超声图像/纹理分析/分维/孔隙度/协同作用/支持向量机Key words
ultrasonic liver cancer image/ texture analysis/ fractal dimension/ lacunarity/ synergy/ Support Vector Machine( SVM)分类
信息技术与安全科学引用本文复制引用
季桂树,禹智夫..分维和孔隙度在肝癌超声纹理识别中协同作用[J].计算机工程与应用,2013,49(2):211-218,8.
