Automated Lung Nodules and Ground Glass Opacity Nodules Detection and Classification from Computed Tomography Images
Lung cancer health care community depends on lung cancer Computer Aided Detection system to draw useful lung cancer details from Computed Tomography lung images. Nodules growth rate indicates the severity of the disease, which can be periodically radiologist analyzed by nodule segmentation and classification. Main challenges in analyzing nodules growth rate are lung nodules of different type requires special methods for segmentation, their irregular shape, and boundary. In this paper, automatic three-phase framework for lung nodules and nodules of ground glass opacity detection followed by classification is proposed. In this work, nodule segmentation framework uses proposed automatic region growing algorithm that selects set of black pixels as seed points automatically from output binary image for lung parenchyma segmentation followed by artifacts removal to reduce disease search space. Nodules are segmented based on nodule candidates center pixels identification and intensity feature of lung nodule candidates. Segmented nodules are classified using SVM classifier and classification results are compared with other considered classifiers KNN, boosting and decision tree. In the evaluation step, it was found that SVM classifier’s performance is outstanding compared to other considered classifiers in this work. Complete automation in nodule detection within very less time is the key feature of the proposed method. CT images are taken from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database to evaluate the performance of proposed work. An accuracy of 98% (45/46) with less computational time is achieved. The experimental results demonstrated that the proposed method achieve efficient and accurate segmentation of lung nodules and ground glass opacity nodules with less computation time.
KeywordsLung parenchyma Computer Aided Detection Benign and malignant Nodules segmentation Computed tomography
I thank my research guide Dr. Girijamma H A, Professor, Department of CSE, RNSIT, Bangalore, India. For supporting to complete this research article. I would like to thank public LIDC database from which images are taken to carry a reach work.
- 1.Non communicable Diseases Progress Monitor (2017) Geneva: World Health Organization; 2017. Licence: CC BY-NC-SA 3.0 IGOGoogle Scholar
- 3.Hillary Wasserman Becky Bunn.: Lung Cancer Facts and Statistics, International Association for the Study of Lung Cancer(IACLS), WCLC, 20173999999 (2017)Google Scholar
- 4.Habib MSL (2009) A computer aided diagnosis system (CAD) for the detection of pulmonary nodules on CT scans. Systems and biomedical engineering Department, Faculty of Engineering, Cairo University, Giza, EgyptGoogle Scholar
- 6.Ye X, Lin X, Dehmeshki J, Slabaugh G (2009) Shape-based computer aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 56(7)Google Scholar
- 8.Liu Y, Xing Z, Deng C, Li P, Guo M (2010) Automatically detecting lung nodules based on shape descriptor and semi-supervised learning. In: International conference on computer application and system modeling. IEEE, https://doi.org/10.1109/iccasm.20https://doi.org/10.5619447, Taiyuan, ChinaGoogle Scholar
- 10.Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406. https://doi.org/10.1016/j.media
- 11.Shao H, Cao L, Liu Y (2012) A detection approach for solitary pulmonary nodules based on CT images. In: IEEE, 2nd international conference on computer science and network technology (ICCSNT). Changchun, pp 1253–1257Google Scholar
- 14.Elsayed O, Mahar K, Kholief M, Khater HA (2015) Automatic detection of the pulmonary nodules from CT images. IEEE. doi: https://doi.org/10.1109/intellisys.2015.7361223. SAI Intelligent Systems Conference (IntelliSys)