Several computer-aided diagnostic (CAD) methods for solitary pulmonary nodules (SPNs) have been proposed, which can be divided into two major categories: (1) the morphometric CT method, and (2) the perfusion CT method. The first goal of this work is to introduce a neural network-based CAD method of lung nodule diagnosis by combining morphometry and perfusion characteristics by perfusion CT. The proposed approach has the following distinctive features. Firstly, this work develops a very efficient semi-automatic procedure to segment entire nodules. Secondly, reliable nodule classification can be achieved by using only two time-point perfusion CT feature measures (precontrast and 90 s). This greatly reduces the amount of radiation exposure to patients and the data processing time. As demonstrated in previous work, classification tuberculomas from malignancies has been considered to be a challenging task. However the diagnosis accuracy for tuberculomas reaches 92.9% by applying the proposed CAD method.
Another goal of this work is, by investigating the relative merits of 2D and 3D methods, to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. Experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method and thus alleviates the computational demand.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0715108-201821 |
| Date | 15 July 2008 |
| Creators | Yeh, Chinson |
| Contributors | Yaw-Terng Su, Ming-Huei Yu, Pei-Chung Chen, Ming-Ting Wu, Chi-Cheng Cheng, Chen-Wen Yen |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0715108-201821 |
| Rights | unrestricted, Copyright information available at source archive |
Page generated in 0.002 seconds