Abstract
Accurate segmentation of lung nodules with low contrast boundaries in CT images is a challenging task since the intensity of nodules and non-nodules overlap with each other. This work proposes a lung nodule segmentation scheme based on least weight navigation (LWN) that segments the lung nodule accurately with such low contrast boundaries. The complete lung nodule segmentation is categorized intothree stages namely, (i) Lung segmentation, (ii) Coarse segmentation of nodule, and (iii) Fine segmentation of nodule. The lung segmentation aims to eliminate the background other than the lung, whereas the coarse segmentation eliminates the lung leaving the nodules. The lung segmentation and coarse segmentation can be achieved using the traditional algorithms namely, dilation, erosion, and Otsu’s thresholding. The proposed work focused on fine segmentation where the boundaries are accurately detected by the LWN algorithm. The LWN algorithm estimates the edge points and then navigation is performed based on the least weight. This navigation is done till the final termination is reached, which results in accurate segmentation results. The experimental validation was done on LIDC and Cancer Imaging dataset with three different nodules such as Juxta vascular, Juxta pleura, and Solitary. The evaluation was done using the metrics such as dice similarity coefficient (DSC), sensitivity (SEN), positive prediction value (PPV). Hausdorff distance (HD) andProbability rand index(PRI). The proposed approach provides a DSC, SEN, and PPV of 84.27%, 89.92%, and 80.12% respectively. The result reveals that the proposed work outperforms the traditional lung nodule segmentation algorithms.
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Beula, R.J., Wesley, A.B. Accurate segmentation of lung nodule with low contrast boundaries by least weight navigation. Multimed Tools Appl 82, 27635–27657 (2023). https://doi.org/10.1007/s11042-023-14620-w
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DOI: https://doi.org/10.1007/s11042-023-14620-w