Yamamoto Megumi, Ishida Takayuki, Kawashita Ikuo, Kagemoto Masayuki, Fujikawa Koichi, Mitogawa Yoshimi, Ubagai Tsutomu, Ishine Masahiro, Ito Katsuhide, Akiyama Mitoshi
Hiroshima International University, Department of Clinical Radiology.
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2006 Apr 20;62(4):555-64. doi: 10.6009/jjrt.62.555.
We have developed an automated computerized method for the detection of lung nodules in three-dimensional (3D) computed tomography (CT) images obtained by helical CT. In this scheme, a lung segmentation technique for the determination of the nodule search area is performed based on a gray-level thresholding technique. To enhance lung nodules, we employed the 3D cross-correlation method by using a 3D Gaussian template with zero-surrounding as a model of lung nodule. False positives are then eliminated by using a rule-base with 53 features. For further reduction of false positives, we performed linear discriminant analysis using these 53 features. The average number of false positives was 6.7 per case at a percent sensitivity of 85.0%. This computerized scheme will be useful to radiologists by providing a "second opinion" in case of possible early lung cancer.
