Intuitionistic Fuzzy Domain Level Set Method for Automatic Delineation of Juxta-pleural Pulmonary Nodules in Thoracic CT Images

Background: Automated delineation of exact boundaries of nodules in Thoracic Computed Tomography (CT) images is one of the difficult tasks owing to the weak edges and fuzzy boundaries of nodules. Generally, the juxta-pleura nodules get missed due to an inaccurate extraction of lung field region. Methods: In this work, an attempt has been made to address such issues by developing an Intuitionistic Fuzzy domain Region-based Level Set Method named as IFRbLSM to detect pleura attached lung nodules in Thoracic CT images. In the proposed method, Intuitionistic fuzzy energy is incorporated into length regularization term of Region-based Level Set Method (RbLSM) to solve the problem of boundary leakage and a simple Lung field extraction algorithm is used as a preprocessing step to extract the lung field region accurately. Result: Due to inherent capability of Intuitionistic fuzzy sets in handling fuzzy boundaries, the proposed method improves the detection of juxta-pleural nodules both in terms of accuracy and time. The method has been tested and evaluated on the standardized Thoracic CT image dataset provided by Lung Image Database Consortium (LIDC). Performance has been evaluated in terms of True Positive Ratio, False Positive Ratio, False Negative Ratio, Tannimoto Coefficient, Dice Similarity Coefficient and time complexity. From experimental results, it has been observed that proposed IFRbLSM outperforms the Fuzzy domain Region-based Level Set Method (FRbLSM) in terms of all quantitative metrics. Conclusion: Moreover, IFRbLSM is computationally more efficient than FRbLSM.