[Development of computer-aided diagnostic system for detection of lung nodules in three-dimensional computed tomography images].
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.
Yamamoto
Megumi
M
Hiroshima International University, Department of Clinical Radiology.
Ishida
Takayuki
T
Kawashita
Ikuo
I
Kagemoto
Masayuki
M
Fujikawa
Koichi
K
Mitogawa
Yoshimi
Y
Ubagai
Tsutomu
T
Ishine
Masahiro
M
Ito
Katsuhide
K
Akiyama
Mitoshi
M
D003936:Diagnosis, Computer-Assisted; D005189:False Positive Reactions; D006801:Humans; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*