APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION

Abstract

Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To measure accuracy, the Jaccard Index (JI) was used. Jaccard Index (JI) was calculated by comparing the segmented lesion to alternative segmentations obtained from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results showed that the semi-automated technique (combination techniques between an active contour and low-level processing) in lung lesion segmentation has moderate accuracy with an average JI value of 0.76±0.12.