Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images.
OBJECTIVE
An example-based assisting approach that supports decision making in classifying pulmonary nodules in 3-dimensional (3D) thoracic computed tomography images has been developed.


METHODS
The example-based assisting approach retrieves and displays nodules that exhibit morphologic and internal profiles consistent to the nodule in question. It uses a 3D computed tomography image database containing 143 pulmonary nodules for which diagnosis is known. The central module makes possible analysis of the query nodule image and extraction of the features of interest: shape, surrounding structure, and internal structure of the nodules. The principal axes and the compactness characterize the nodule shape. The surrounding and internal structures are represented by the distribution pattern of computed tomography density value and 3D curvature indexes. The nodule representation is then used for computing a similarity measure such as a correlation coefficient and a malignant likelihood of the query nodule. The malignant likelihood is estimated by the difference between the representation patterns of the query case and the retrieved lesions. The Mahalanobis distance was adopted as the difference measure. The approach performance was assessed through leave-one-out method by the false-positive rate.


RESULTS
Given a query nodule image, the proposed method retrieved benign and malignant images similar to the query case and provided its malignant likelihood. The number of cases that obtained enough number of the retrieved cases for estimating the malignant likelihood was 107 cases (malignant, 70; benign, 37) in our database. Sensitivity was 91.4% (64 of 70 malignant nodules), specificity was 51.4% (19 of 37 benign nodules), and accuracy values were 77.6% (83 of 107 nodules).


CONCLUSIONS
Preliminary assessment of this approach showed that an example-based assisting approach is an effective tool for making the diagnostic decision in the classification of pulmonary nodules using the nodule image database.
Kawata
Yoshiki
Y
Department of Optical Science and Technology, University of Tokushima, Minamijosanjima-cho, 2-1, Tokushima 770-8506, Japan.
Niki
Noboru
N
Ohmatsu
Hironobu
H
Moriyama
Noriyuki
N
D016208:Databases, Factual; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000473:pathology*; D008297:Male; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic; D011873:Radiology Information Systems; D014057:Tomography, X-Ray Computed