Automatic classification of lung nodules on MDCT images with the temporal subtraction technique

Abstract

Purpose: A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives.

Method: This paper presents a novel automatic segmentation of lung nodules using the Watershed method, multiscale gradient vector flow snakes and a detection method using the extracted features and classifiers for small lung nodules (20 mm or less).

Result: Using the proposed method, we conduct an experiment on 30 thoracic multiple-detector computed tomography cases including 31 small lung nodules.

Conclusion: The experimental results indicate the efficiency of our segmentation method.

Keywords: CAD; Lung nodule; MDCT; Machine learning; Temporal subtraction.