Combination of low level processing and active contour techniques for semi-automated volumetric lung lesion segmentation from thoracic CT images

Segmentation is one of the most important steps in automated medical diagnosis applications, which remains to be a difficult task. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic Computed Tomography (CT) images by combining low level processing and active contour techniques. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the image of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values between 0.837 to 0.956, especially when considering the variability of the alternative segmentations.