Automated Pulmonary Lung Nodule Detection Using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier


  • Ammi Reddy Pulagam Vasireddy Venkatadri Institute of Technology
  • Venkata Krıshna Rao Ede Lakireddy Bali Reddy College of Engineering, Mylavaram, Vijayawada, AP, India
  • Ramesh Babu Inampudi Acharya Nagrjuna University, Nagarjuna Nagar Guntur, AP, India



computed tomography, lung cancer, pulmonary nodules, statistical thresholding, SVM.


The pulmonary lung nodule is the most common indicator of lung cancer. An efficient automated pulmonary nodule detection system aids the radiologists to detect the lung abnormalities at an early stage. In this paper, an automated lung nodule detection system using a feature descriptor based on optimal manifold statistical thresholding to segment lung nodules in Computed Tomography (CT) scans is presented. The system comprises three processing stages. In the first stage, the lung region is extracted from thoracic CT scans using gray level thresholding and 3D connected component labeling. After that novel lung contour correction method is proposed using modified convex hull algorithm to correct the border of a diseased lung. In the second stage, optimal manifold statistical image thresholding is described to minimize the discrepancy between nodules and other tissues of the segmented lung region. Finally, a set of 2D and 3D features are extracted from the nodule candidates, and then the system is trained by employing support vector machines (SVM) to classify the nodules and non-nodules. The performance of the proposed system is assessed using Lung TIME database. The system is tested on 148 cases containing 36408 slices with total sensitivity of 94.3%, is achieved with only 2.6 false positives per scan.


(1) Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA: a cancer journal for clinicians. 2015 Jan 1;65(1):5-29.

(2) Ballangan C, Wang X, Fulham M, Eberl S, Feng DD. Lung tumor segmentation in PET images using graph cuts. Computer methods and programs in biomedicine. 2013 Mar 31;109(3):260-8.

(3) Thomsen LP, Weinreich UM, Karbing DS, Jensen VG, Vuust M, Frøkjær JB, Rees SE. Can computed tomography classifications of chronic obstructive pulmonary disease be identified using Bayesian networks and clinical data?. Computer methods and programs in biomedicine. 2013 Jun 30;110(3):361-8.

(4) Samuel CC, Saravanan V, Devi MV. Lung nodule diagnosis from CT images using fuzzy logic. InConference on Computational Intelligence and Multimedia Applications, 2007. International Conference on 2007 Dec 13 (Vol. 3, pp. 159-163). IEEE.

(5) Suárez-Cuenca JJ, Tahoces PG, Souto M, Lado MJ, Remy-Jardin M, Remy J, Vidal JJ. Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Computers in Biology and Medicine. 2009 Oct 31;39(10):921-33.

(6) Retico A, Delogu P, Fantacci ME, Gori I, Martinez AP. Lung nodule detection in low-dose and thin-slice computed tomography. Computers in biology and medicine. 2008 Apr 30;38(4):525-34.

(7) Messay T, Hardie RC, Rogers SK. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Medical image analysis. 2010 Jun 30;14(3):390-406.

(8) Sluimer I, Prokop M, Van Ginneken B. Toward automated segmentation of the pathological lung in CT. IEEE transactions on medical imaging. 2005 Aug;24(8):1025-38.

(9) van Rikxoort EM, de Hoop B, Viergever MA, Prokop M, van Ginneken B. Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. Medical physics. 2009 Jul 1;36(7):2934-47.

(10) De Nunzio G, Tommasi E, Agrusti A, Cataldo R, De Mitri I, Favetta M, Maglio S, Massafra A, Quarta M, Torsello M, Zecca I. Automatic lung segmentation in CT images with accurate handling of the hilar region. Journal of digital imaging. 2011 Feb 1;24(1):11-27.

(11) Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB, Yee J, Dey J, Napel S. Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE transactions on medical imaging. 2004 Jun;23(6):661-75.

(12) Armato SG, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H. Computerized detection of pulmonary nodules on CT scans 1. Radiographics. 1999 Sep;19(5):1303-11.

(13) Pulagam AR, Kande GB, Ede VK, Inampudi RB. Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases. Journal of digital imaging. 2016 Aug 1;29(4):507-19.

(14) Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, Cerello P, De Carlo F, Falaschi F, Fantacci ME, Gargano G. A novel multithreshold method for nodule detection in lung CT. Medical physics. 2009 Aug 1;36(8):3607-18.

(15) Dehmeshki J, Ye X, Lin X, Valdivieso M, Amin H. Automated detection of lung nodules in CT images using shape-based genetic algorithm. Computerized Medical Imaging and Graphics. 2007 Sep 30;31(6):408-17.

(16) Pu J, Paik DS, Meng X, Roos J, Rubin GD. Shape “break-and-repair” strategy and its application to automated medical image segmentation. IEEE transactions on visualization and computer graphics. 2011


(17) Osman O, Ozekes S, Ucan ON. Lung nodule diagnosis using 3D template matching. Computers in Biology and Medicine. 2007 Aug 31;37(8):1167-72.

(18) Suzuki K, Armato SG, Li F, Sone S. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low‐dose computed tomography. Medical physics. 2003 Jul 1;30(7):1602-17.

(19) Cascio D, Magro R, Fauci F, Iacomi M, Raso G. Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models. Computers in Biology and Medicine. 2012 Nov 30;42(11):1098-109.

(20) Choi WJ, Choi TS. Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Computer methods and programs in biomedicine. 2014 Jan 31;113(1):37-54.

(21) da Silva Sousa JR, Silva AC, de Paiva AC, Nunes RA. Methodology for automatic detection of lung nodules in computerized tomography images. Computer methods and programs in biomedicine. 2010 Apr 30;98(1):1-4.

(22) Dolejší M, Kybic J. Automatic two-step detection of pulmonary nodules. InMedical Imaging 2007 Mar 8 (pp. 65143J-65143J). International Society for Optics and Photonics.

(23) Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975 Jun;11(285-296):23-7.

(24) Hou Z, Hu Q, Nowinski WL. On minimum variance thresholding.

Pattern Recognition Letters. 2006 Oct 15;27(14):1732-43.

(25) Osuna E, Freund R, Girosi F. Support vector machines: Training and applications., 1997.




How to Cite

Pulagam, A. R., Ede, V. K. R., & Inampudi, R. B. (2017). Automated Pulmonary Lung Nodule Detection Using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier. British Journal of Healthcare and Medical Research, 4(4), 20.