Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Classification of benign and malignant pulmonary nodules can provide useful indicators for estimating the risk of lung cancer. In this study, an improved random forest (RF) algorithm is proposed for classification of benign and malignant pulmonary nodules in thoracic computed tomography images. First, an improved random walk algorithm is proposed to automatically segment pulmonary nodules. Then, intensity, geometric and texture features based on the grey-level co-occurrence matrix, rotation invariant uniform local binary pattern and Gabor filter methods are combined to generate an effective and discriminative feature vector. Mutual information is employed to reduce the dimensionality. Finally, an improved RF classifier is trained to classify benign and malignant nodules. An appropriate feature subset is selected by the bootstrap method and an effective combination method is introduced to predict a class label. The proposed classification method on the lung images dataset consortium dataset achieves a sensitivity of 0.92 and the area under the receiver-operating-characteristic curve of 0.95. An additional evaluation is performed on another dataset coming from General Hospital of Guangzhou Military Command. A mean sensitivity and a mean specificity of the proposed method are 0.85 and 0.82, respectively. Experimental results demonstrate that the proposed method achieves the satisfactory classification performance.


    1. 1)
      • 1. Siegel, R.L., Miller, K.D., Jemal, A.: ‘Cancer statistics, 2015’, CA Cancer J. Clin., 2015, 65, pp. 529.
    2. 2)
      • 2. Girvin, F., Ko, J.P.: ‘Pulmonary nodules: detection, assessment, and CAD’, Am. J. Roentgenol., 2008, 191, pp. 10571069.
    3. 3)
      • 3. Iwano, S., Nakamura, T., Kamioka, Y., et al: ‘Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT’, Comput. Med. Imaging Graph., 2008, 32, pp. 416422.
    4. 4)
      • 4. Chen, H., Xu, Y., Ma, Y.J., et al: ‘Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images clinical evaluation’, Acad. Radiol., 2010, 17, pp. 595602.
    5. 5)
      • 5. Chen, H., Zhang, J., Xu, Y., et al: ‘Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans’, Expert Syst. Appl., 2012, 39, pp. 1150311509.
    6. 6)
      • 6. Lin, P.-L., Huang, P.-W., Lee, C.-H., et al: ‘Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model’, Pattern Recognit., 2013, 46, pp. 32793287.
    7. 7)
      • 7. Han, F., Wang, H., Zhang, G., et al: ‘Texture feature analysis for computer-aided diagnosis on pulmonary nodules’, J. Digit. Imaging, 2015, 28, pp. 99115.
    8. 8)
      • 8. Cheng, J.-Z., Ni, D., Chou, Y.-H., et al: ‘Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans’, Sci. Rep., 2016, 6, pp. 2445424466.
    9. 9)
      • 9. Dhara, A.K., Mukhopadhyay, S., Dutta, A., et al: ‘A combination of shape and texture features for classification of pulmonary nodules in lung CT images’, J. Digit. Imaging, 2016, 29, pp. 466475.
    10. 10)
      • 10. Liu, Y., Balagurunathan, Y., Atwater, T., et al: ‘Radiological image traits predictive of cancer status in pulmonary nodules’, Clin. Cancer Res., 2017, 23, pp. 14421449.
    11. 11)
      • 11. Tajbakhsh, N., Suzuki, K.: ‘Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs’, Pattern Recognit., 2017, 63, pp. 476486.
    12. 12)
      • 12. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, pp. 17681783.
    13. 13)
      • 13. Eslami, A., Karamalis, A., Katouzian, A., et al: ‘Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI’, Med. Image Anal., 2013, 17, pp. 236253.
    14. 14)
      • 14. Xu, Z., Bagci, U., Foster, B., et al: ‘A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT’, Med. Image Anal., 2015, 24, pp. 117.
    15. 15)
      • 15. Tan, J.H., Acharya, U.R., Lim, C.M., et al: ‘An interactive lung field segmentation scheme with automated capability’, Digit. Signal Process., 2013, 23, pp. 10221031.
    16. 16)
      • 16. Wang, Q., Lu, L., Wu, D., et al: ‘Automatic segmentation of spinal canals in CT images via iterative topology refinement’, IEEE Trans. Med. Imaging, 2015, 34, pp. 16941704.
    17. 17)
      • 17. Mi, H., Petitjean, C., Vera, P., et al: ‘Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images’, Med. Image Anal., 2015, 23, pp. 8491.
    18. 18)
      • 18. Ju, W., Xiang, D., Zhang, B., et al: ‘Random walk and graph cut for co-segmentation of lung tumor on PET-CT images’, IEEE Trans. Image Process., 2015, 24, pp. 58545867.
    19. 19)
      • 19. Patz, T., Preusser, T.: ‘Segmentation of stochastic images with a stochastic random walker method’, IEEE Trans. Image Process., 2012, 21, pp. 24242433.
    20. 20)
      • 20. Park, S.H., Lee, S., Yun, I.D., et al: ‘Structured patch model for a unified automatic and interactive segmentation framework’, Med. Image Anal., 2015, 24, pp. 297312.
    21. 21)
      • 21. Dong, X.P., Shen, J.B., Shao, L., et al: ‘Sub-Markov random walk for image segmentation’, IEEE Trans. Image Process., 2016, 25, pp. 516527.
    22. 22)
      • 22. Breiman, L.: ‘Random forests’, Mach. Learn., 2001, 45, pp. 532.
    23. 23)
      • 23. Mursalin, M., Zhang, Y., Chen, Y.H., et al: ‘Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier’, Neurocomputing, 2017, 241, pp. 204214.
    24. 24)
      • 24. Kostoglou, K., Michmizos, K.P., Stathis, P., et al: ‘Classification and prediction of clinical improvement in deep brain stimulation from intraoperative microelectrode recordings’, IEEE Trans. Biomed. Eng., 2017, 64, pp. 11231130.
    25. 25)
      • 25. Kostis, W.J., Reeves, A.P., Yankelevitz, D.F., et al: ‘Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images’, IEEE Trans. Med. Imaging, 2003, 22, pp. 12591274.
    26. 26)
      • 26. Kuhnigk, J.M., Dicken, V., Bornemann, L., et al: ‘Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans’, IEEE Trans. Med. Imaging, 2006, 25, pp. 417434.
    27. 27)
      • 27. Diciotti, S., Picozzi, G., Falchini, M., et al: ‘3-D segmentation algorithm of small lung nodules in spiral CT images’, IEEE Trans. Inf. Technol. Biomed., 2008, 12, pp. 719.
    28. 28)
      • 28. Dehmeshki, J., Amin, H., Valdivieso, M., et al: ‘Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach’, IEEE Trans. Med. Imaging, 2008, 27, pp. 467480.
    29. 29)
      • 29. Kubota, T., Jerebko, A.K., Dewan, M., et al: ‘Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models’, Med. Image Anal., 2011, 15, pp. 133154.
    30. 30)
      • 30. Farag, A.A., Abd El Munim, H.E., Graham, J.H., et al: ‘A novel approach for lung nodules segmentation in chest CT using level sets’, IEEE Trans. Image Process., 2013, 22, pp. 52025213.
    31. 31)
      • 31. Netto, S.M.B., Silva, A.C., Nunes, R.A., et al: ‘Automatic segmentation of lung nodules with growing neural gas and support vector machine’, Comput. Biol. Med., 2012, 42, pp. 11101121.
    32. 32)
      • 32. Chen, K., Li, B., Tian, L.F., et al: ‘Vessel attachment nodule segmentation using integrated active contour model based on fuzzy speed function and shape-intensity joint Bhattacharya distance’, Signal Process., 2014, 103, pp. 273284.
    33. 33)
      • 33. Sun, S.S., Guo, Y., Guan, Y.B., et al: ‘Juxta-vascular nodule segmentation based on flow entropy and geodesic distance’, IEEE J. Biomed. Health Inf., 2014, 18, pp. 13551362.
    34. 34)
      • 34. Messay, T., Hardie, R.C., Tuinstra, T.R.: ‘Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset’, Med. Image Anal., 2015, 22, pp. 4862.
    35. 35)
      • 35. Li, B., Chen, Q.L., Peng, G.M., et al: ‘Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering’, Biomed. Eng. Online, 2016, 15, pp. 4976.
    36. 36)
      • 36. Diciotti, S., Lombardo, S., Falchini, M., et al: ‘Automated segmentation refinement of small lung nodules in CT scans by local shape analysis’, IEEE Trans. Biomed. Eng., 2011, 58, pp. 34183428.
    37. 37)
      • 37. Zhang, F., Song, Y., Cai, W., et al: ‘Lung nodule classification with multilevel patch-based context analysis’, IEEE Trans. Biomed. Eng., 2014, 61, pp. 11551166.
    38. 38)
      • 38. Ye, X.J., Lin, X.Y., Dehmeshki, J., et al: ‘Shape-based computer-aided detection of lung nodules in thoracic CT images’, IEEE Trans. Biomed. Eng., 2009, 56, pp. 18101820.
    39. 39)
      • 39. Muramatsu, C., Hara, T., Endo, T., et al: ‘Breast mass classification on mammograms using radial local ternary patterns’, Comput. Biol. Med., 2016, 72, pp. 4353.
    40. 40)
      • 40. Beura, S., Majhi, B., Dash, R.: ‘Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer’, Neurocomputing, 2015, 154, pp. 114.
    41. 41)
      • 41. Sethi, G., Saini, B.S.: ‘Computer aided diagnosis system for abdomen diseases in computed tomography images’, Biocybern. Biomed. Eng., 2015, 36, pp. 4255.
    42. 42)
      • 42. Torheim, T., Malinen, E., Kvaal, K., et al: ‘Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines’, IEEE Trans. Med. Imaging, 2014, 33, pp. 16481656.
    43. 43)
      • 43. Gomez, W., Pereira, W.C.A., Infantosi, A.F.C.: ‘Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound’, IEEE Trans. Med. Imaging, 2012, 31, pp. 18891899.
    44. 44)
      • 44. Liu, L., Lao, S., Fieguth, P.W., et al: ‘Median robust extended local binary pattern for texture classification’, IEEE Trans. Image Process., 2016, 25, pp. 13681381.
    45. 45)
      • 45. Rastghalam, R., Pourghassem, H.: ‘Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images’, Pattern Recognit., 2016, 51, pp. 176186.
    46. 46)
      • 46. Guo, Z., Wang, X., Zhou, J., et al: ‘Robust texture image representation by scale selective local binary patterns’, IEEE Trans. Image Process., 2016, 25, pp. 687699.
    47. 47)
      • 47. Bianconi, F., Fernandez, A.: ‘Evaluation of the effects of Gabor filter parameters on texture classification’, Pattern Recognit., 2007, 40, pp. 33253335.
    48. 48)
      • 48. Ivanovici, M., Richard, N.: ‘Fractal dimension of color fractal images’, IEEE Trans. Image Process., 2011, 20, pp. 227235.
    49. 49)
      • 49. Gangeh, M.J., Tadayyon, H., Sannachi, L., et al: ‘Computer aided theragnosis using quantitative ultrasound spectroscopy and maximum mean discrepancy in locally advanced breast cancer’, IEEE Trans. Med. Imaging, 2016, 35, pp. 778790.
    50. 50)
      • 50. Jacobs, C., van Rikxoort, E.M., Twellmann, T., et al: ‘Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images’, Med. Image Anal., 2014, 18, pp. 374384.
    51. 51)
      • 51. Liu, J., Wang, S., Linguraru, M.G., et al: ‘Computer-aided detection of exophytic renal lesions on non-contrast CT images’, Med. Image Anal., 2015, 19, pp. 1529.
    52. 52)
      • 52. Guo, Z.H., Zhang, L., Zhang, D.: ‘Rotation invariant texture classification using LBP variance (LBPV) with global matching’, Pattern Recognit., 2010, 43, pp. 706719.
    53. 53)
      • 53. Jaeger, S., Karargyris, A., Candemir, S., et al: ‘Automatic tuberculosis screening using chest radiographs’, IEEE Trans. Med. Imaging, 2014, 33, pp. 233245.
    54. 54)
      • 54. Xu, D., Li, H.: ‘Geometric moment invariants’, Pattern Recognit., 2008, 41, pp. 240249.
    55. 55)
      • 55. Wee, C.-Y., Paramesran, R., Mukundan, R.: ‘Fast computation of geometric moments using a symmetric kernel’, Pattern Recognit., 2008, 41, pp. 23692380.
    56. 56)
      • 56. Dhara, A.K., Mukhopadhyay, S., Saha, P., et al: ‘Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images’, Int. J. Comput. Assist. Radiol. Surg., 2016, 11, pp. 337349.
    57. 57)
      • 57. Armato, S.G., McLennan, G., Bidaut, L., et al: ‘The lung image database consortium, (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans’, Med. Phys., 2011, 38, pp. 915931.
    58. 58)
      • 58. Aminikhanghahi, S., Shin, S., Wang, W., et al: ‘A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification’, Multimedia Tools Appl., 2017, 76, pp. 1019110205.
    59. 59)
      • 59. Dora, L., Agrawal, S., Panda, R., et al: ‘Optimal breast cancer classification using Gauss–newton representation based algorithm’, Expert Syst. Appl., 2017, 85, pp. 134145.
    60. 60)
      • 60. Shen, W., Zhou, M., Yang, F., et al: ‘Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification’, Pattern Recognit., 2017, 61, pp. 663673.

Related content

This is a required field
Please enter a valid email address