• Elaheh Aghabalaei Khordehchi Iran University of Science and Technology
  • Ahmad Ayatollahi Iran University of Science and Technology
  • Mohammad Reza Daliri Iran University of Science and Technology



CT X-ray images, morphological filtering, nodule detection, nonlinear partial differential quations, statistical region merging, support vector machine


Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

Author Biographies

Elaheh Aghabalaei Khordehchi, Iran University of Science and Technology

Electrical Engineering Department

Ahmad Ayatollahi, Iran University of Science and Technology

Electrical Engineering Department

Mohammad Reza Daliri, Iran University of Science and Technology

Electrical Engineering Department


Akram S, Javed MY, Akram MU, Qamar U, Hassan A (2016). Pulmonary Nodules Detection and Classification Using Hybrid Features from Computerized Tomographic Images. J Med Imag Health Info 6: 252-9.

Alpert JB, Lowry CM, Ko JP (2015). Imaging the solitary pulmonary nodule. Clini Chest Med 36: 161-78.

Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Kazerooni EA (2011). The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phy 38(2): 915-31.

Bergtholdt M, Wiemker R, Klinder T (2016). Pulmonary nodule detection using a cascaded SVM classifier. In SPIE Medical Imaging. Int Soc Opt Phot 978513.

Boroczky L, Zhao L, Lee KP (2006). Feature subset selection for improving the performance of false positive reduction in lung nodule CAD. IEEE Trans Info Tech Biomed, 10: 504-11.

Boser BE, Guyon IM, Vapnik VN (1992). A training algorithm for optimal margin classifiers. In Proceed fifth annual workshop Comp learning theory :144-52.

Campadelli P, Casiraghi E, Artioli D (2006). A fully automated method for lung nodule detection from postero-anterior chest radiographs. IEEE trans Med Imag 25: 1588-603.

Crespo J, Serra J, Schafer RW (1995). Theoretical aspects of morphological filters by reconstruction. Sig Process 47: 201-25.

De Carvalho Filho AO, de Sampaio WB, Silva AC, de Paiva AC, Nunes RA, Gattass M (2014). Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Art Intel Med 60: 165-77.

Devan L, Santosham R, Hariharan R (2014). Automated texture‐based characterization of fibrosis and carcinoma using low‐dose lung CT images. Int J Imag Sys Tech 24: 39-44.

El-Baz A, Beache GM, Gimel'farb G, Suzuki K, Okada K, Elnakib A, Abdollahi B (2013). Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Int J Biomed Imag.

Fielding AH, Bell JF (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24: 38-49.

Froz BR, de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M (2017). Lung nodule classification using artificial crawlers, directional texture and support vector machine. Exp Sys App 69: 176-88.

Gao Q, Wang S, Zhao D, Liu J (2007). Accurate lung segmentation for X-ray CT images. In Third Int Conf Natural Comp (IEEE - ICNC 2007) :275-9.

Gould MK, Fletcher J, Iannettoni MD, Lynch WR, Midthun DE, Naidich DP, Ost DE (2007). Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines. Chest J 132: 108-30.

Harikumar R (2015). Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imag Sys Tech 25: 33-40.

Hasanabadi H, Zabihi M, Mirsharif Q (2014). Detection of pulmonary nodules in CT images using template matching and neural classifier. J Adv Comp Research 5: 19-28.

Hassanpour H, Zehtabian A, Yousefian H (2011). Pixon-based image segmentation. INTECH Open Access Publisher.

Herman GT (2009). Fundamentals of computerized tomography: image reconstruction from projections. Springer Science & Business Media.

Hoffman EA, Clough AV, Christensen GE, Lin CL, McLennan G, Reinhardt JM, Wang G (2004). The comprehensive imaging-based analysis of the lung: A forum for team science1. Acad Radio 11: 1370-80.

Hu S, Hoffman EA, Reinhardt JM (2001). Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imag 20: 490-8.

John J, Mini MG (2016). Multilevel Thresholding Based Segmentation and Feature Extraction for Pulmonary Nodule Detection. Pro Tech 24: 957-63.

Kim DY, Kim JH, Noh SM, Park JW (2003). Pulmonary nodule detection using chest CT images. Acta Radio, 44: 252-7.

Kishore VV, Satyanarayana RVS (2013). Performance evaluation of edge detectors-morphology based ROI segmentation and nodule detection from DICOM lung images in the noisy environment. In Adv Comp Conf (IEEE- IACC) : 1131-7.

Korfiatis P, Skiadopoulos S, Sakellaropoulos P, Kalogeropoulou C, Costaridou L (2014). Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. British J Radio.

Lo SC, Lou SL, Lin JS, Freedman MT, Chien MV, Mun SK (1995). Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imag 14:711-8.

Mansoor A, Bagci U, Xu Z, Foster B, Olivier KN, Elinoff JM, Mollura DJ (2014). A generic approach to pathological lung segmentation. IEEE Trans Med Imag 33: 2293-310.

Mendonça AM, da Silva JA, Campilho A (2004). Automatic delimitation of lung fields on chest radiographs. In Biomedical Imaging: Nano to Macro, 2004. IEEE Int Symp : 1287-90.

Mousa WA, Khan MA (2002). Lung nodule classification utilizing support vector machines. In Imag Process Proceedings. Int Conf : 150-3.

Nagarajan MB, Huber MB, Schlossbauer T, Leinsinger G, Krol A, Wismüller A (2013). Classification of small lesions in breast MRI: evaluating the role of dynamically extracted texture features through feature selection. J Med Bio Eng, 33: 102-10.

Nock R, Nielsen F (2004). Statistical region merging. IEEE Trans Patt Analy Machine Intell, 26: 1452-8.

Padma A, Giridharan N (2016). Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images. Int J Imag Sys Tech 26: 97-105.

Penedo MG, Carreira MJ, Mosquera A, Cabello D (1998). Computer-aided diagnosis: a neural-network-based approach to lung nodule detection. IEEE Trans Med Imag, 17: 872-80.

Perona P, Malik J (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Trans Patt Analy Machine Intell 12: 629-39.

Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, van Riel SJ, van Ginneken B (2016). Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imag 35: 1160-9.

Silveira M, Nascimento J , Marques J (2007). Automatic segmentation of the lungs using robust level sets. In 2007 29th Annual Int Conf IEEE Eng Med Bio Soc : 4414-7.

Soille P (2013). Morphological image analysis: principles and applications. Springer Science & Business Media.

Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMR (2016). Automatic 3D pulmonary nodule detection in CT images: a survey. Comp Meth Prog Biomed 124: 91-107.

Wei J, Li G (2014). Automated Lung Segmentation and Image Quality Assessment for Clinical 3-D/4-D-Computed Tomography. IEEE J Trans Eng Health Med 2: 1-10.

Witkin AP, Filtering SS (1984). A new approach to multi-scale description. In IEEE ICASSP.

Yim Y, Hong H, Shin YG (2005). Hybrid lung segmentation in chest CT images for computer-aided diagnosis. In Proceedings of 7th Int Workshop Enterprise Net Comp Healthcare Indust, 2005. HEALTHCOM 2005: 378-83.

Zehtabian A, Nazari A, Ghassemian H, Gribaudo M (2015). Adaptive Restoration of Multispectral Datasets used for SVM classification. Europ J Remote Sens 48: 183-200.

Zehtabian A, Ghassemian H (2016). Automatic Object-Based Hyperspectral Image Classification Using Complex Diffusions and a New Distance Metric. IEEE Trans Geosc Remote Sens 54: 4106-14.

Zehtabian A, Ghassemian H (2016). Combining Genetic Algorithm with PDEs for Improving the Performance of Statistical Region Merging Based Object Extraction. J Ind Soc Remote Sens 1-13.

Zhang F, Song Y, Cai W, Zhou Y, Shan S, Feng D (2013). Context curves for classification of lung nodule images. In Digital Imag Comp: Tech App (DICTA), 2013 Int Conf : 1-7.

Zhou Z, Wu S, Chang KJ, Chen WR, Chen YS, Kuo WH, Tsui PH (2015). Classification of benign and malignant breast tumors in ultrasound images with posterior acoustic shadowing using half-contour features. J Med Bio Eng 35: 178-87.

Zhou T, Lu H, Zhang, J, Shi H (2016). Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets. BioMed Research Int, 2016.




How to Cite

Aghabalaei Khordehchi, E., Ayatollahi, A., & Daliri, M. R. (2017). AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES. Image Analysis and Stereology, 36(2), 65–78.



Original Research Paper