An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image

•An improved lung segmentation algorithm is presented employing morphological techniques.•Considering the morphological and deformable properties of the lung nodules, adaptive structuring elements have been introduced for the development of morphological filters.•Gray scale adaptive morphological fi...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 197; p. 105720
Main Authors:	Halder, Amitava, Chatterjee, Saptarshi, Dey, Debangshu, Kole, Surajit, Munshi, Sugata
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01-12-2020
Subjects:	Adaptive morphology Computer-aided detection Diagnosis, Computer-Assisted Humans Lung - diagnostic imaging Lung cancer Lung Neoplasms - diagnostic imaging Nodule detection Radiographic Image Interpretation, Computer-Assisted Segmentation Sensitivity and Specificity Solitary Pulmonary Nodule - diagnostic imaging Tomography, X-Ray Computed Nodule detection Segmentation Adaptive morphology Computer-aided detection Lung cancer
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Summary:	•An improved lung segmentation algorithm is presented employing morphological techniques.•Considering the morphological and deformable properties of the lung nodules, adaptive structuring elements have been introduced for the development of morphological filters.•Gray scale adaptive morphological filters are developed for the segmentation and detection of large varieties of candidate nodules. Lung cancer is one of the most life-threatening cancers mostly indicated by the presence of nodules in the lung. Doctors and radiological experts use High-Resolution Computed Tomography (HRCT) images for nodule detection and further decision making from visual inspection. Manual detection of lung nodules is a time-consuming process. Therefore, Computer-aided detection (CADe) systems have been developed for accurate nodule detection and segmentation. CADe-based systems assist radiologists to detect lung nodules with greater confidence and a lesser amount of time and have a significant impact on the accurate, uniform, and early-stage diagnosis of lung cancer. In this research work, an adaptive morphology-based segmentation technique (AMST) has been introduced by designing an adaptive morphological filter for improved segmentation of the lung nodule region. The adaptive morphological filter detects candidate nodule regions by employing adaptive structuring element (ASE) and at the same time improves nodule detection accuracy by reducing false positives (FPs) from the Computed Tomography (CT) slices. The detected nodule candidate regions are then processed for feature extraction. In this study, morphological, texture and intensity-based features have been used with support vector machine (SVM) classifier for lung nodule detection. The performance of the proposed framework has been evaluated by incorporating a 10-fold cross-validation technique on Lung Image Database Consortium-Image Database Resource Initiative (LIDC/IDRI) dataset and on a private dataset, collected from a consultant radiologist. It has been observed that the proposed automated computer-aided detection system has achieved overall classification performance indices with 94.88% sensitivity, 93.45% specificity and 94.27% detection accuracy with 1.8 FPs/scan on LIDC/IDRI dataset and 91.43% sensitivity, 90.45% specificity, 92.83% accuracy with 3.2 FPs/scan on a private dataset. The results show that the proposed CADe system presented in this paper outperforms the other state-of-the-art methods for automatic nodule detection from the HRCT image.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0169-2607 1872-7565
DOI:	10.1016/j.cmpb.2020.105720