Small-Size Lung Nodule Modeling and Detection with Clinical Evaluation

被引：0
|
作者：
Farag, Amal [1 ]
Graham, James [1 ]
Abdelmunim, Hossam [1 ]
Elshazly, Salwa [1 ]
El-Mogy, Mohamed
El-Mogy, Sabry
Falk, Robert
Farag, Aly A. [1 ]
机构：
[1] Univ Louisville, Comp Vis & Image Proc Lab CVIP Lab, Louisville, KY 40292 USA
来源：
2012 CAIRO INTERNATIONAL BIOMEDICAL ENGINEERING CONFERENCE (CIBEC) | 2012年
关键词：
Lung nodule modeling; Data-driven; nodule detection; PULMONARY NODULES;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In this paper examination of the template modeling process using the Active Appearance Modeling (AAM) approach for automatic detection of lung nodules is investigated. A template matching approach is formulated to compute a similarity score between the AAM templates and the input lung CT slice, where the goal is to maximize the similarity measure at different image pixels to increase nodule detection. The template matching approach is implemented using nine similarity measures. Performance validation for the robustness of the generated models is tested on three clinical databases.
引用
收藏
页码：44 / 47
页数：4
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