Skip to main content
SpringerLink
Log in

A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Lungs cancer is a fatal disease. However, its early detection increases the chances of survival among patients. An automated nodule detection system provides the second opinion to radiologists in early diagnosis. In this paper, an automated technique for nodule detection and classification is presented. Firstly, the lung region is extracted on the basis of the optimal gray level threshold. In the next phase, a novel hybrid 3D nodule candidate detection method is presented, comprises of Active Contour Model (ACM), 3D neighborhood connectivity and geometric properties based rules. A hybrid feature vector is created, by combining geometric texture and Histogram of Oriented Gradient reduced by Principle Component Analysis (HOG-PCA) features, for each nodule candidate. After feature extraction, classification is performed by applying four different classifiers including k-Nearest Neighborhood (k-NN), Naive Bayesian, Support Vector Machine (SVM) and AdaBoost. The evaluation is performed over Lung Image Database Consortium (LIDC) database. It is evident that AdaBoost has outperformed all other classifiers regarding accuracy, sensitivity, specificity and FPs/scan. Moreover, the proposed technique has shown significantly better results as compared to other existing methods reported in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

Acc:

Accuracy

ACM:

Active contour models

AUC:

Area under curve

CT:

Computed tomography

FN:

False negative

FP:

False positive

HOG:

Histograms of oriented gradients

HU:

Hounsfield unit

k-NN:

k-Nearest neighbor

LIDC:

Lung image database consortium

PCA:

Principle component analysis

ROI:

Region of interest

Sen:

Sensitivity

Spc:

Specificity

SVM:

Support vector machine

TN:

True negative

TP:

True positive

w :

Width of the polygon surrounding an object

l :

Length of the polygon

τ :

Threshold

ø :

Empty set

S :

Set of objects in ROI

σ :

Standard deviation

μ 3 :

Skewness

E :

Energy

H :

Entropy

μ 4 :

Kurtosis

G :

Maximum gray level in an image

μ i :

Mean of class i

V :

Set of voxels in the segmented object

v :

Voxel

v(s):

Position of snake

R τ :

Ratio threshold

\( {T}_l^A \) :

Lower threshold of the area

\( {T}_h^A \) :

Higher threshold of the area

C τ :

Circularity threshold

G l :

Lowest gray level in an image

G h :

Highest gray level in an image

F G :

Geometric features

F T :

Texture features

F H :

HOG features

X :

Attributes in the feature vector

C i :

Target class

D t :

Probability distribution factor in AdaBoost

T :

Total number of weak classifiers in AdaBoost

U :

Union

p i :

Probability distribution

∝:

The scalar to control elastic energy in ACM

∝t :

Trust factor

β :

The scalar to control bending energy in ACM

ω :

The penalty associated with the probability of a category

C i :

Target class

\( {d}_{\tau}^n \) :

Maximum threshold for the diameter of the nodule

P:

Probability

d:

Degree of the polynomial

D :

Diameter

r :

Radius

A :

Area

References

  1. Ali I, Hart G, Gunabushanam G, Liang Y, Muhammad W, Nartowt B, Kane M, Ma X, Deng J (2018) lung nodule Detection via Deep reinforcement learning. Front Oncol 8:108

    Article  Google Scholar 

  2. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman 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 Phys 38(2):915–931

    Article  Google Scholar 

  3. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H (2004) Lung image database consortium: Developing a resource for the medical imaging research community 1. Radiology 232(3):739–748

    Article  Google Scholar 

  4. Armato S, MacMahon H (2003) Automated lung segmentation and computer-aided diagnosis for thoracic CT scans. In: International Congress Series. Elsevier, pp 977–982

  5. Badura P, Pietka E (2014) Soft computing approach to 3D lung nodule segmentation in CT. Comput Biol Med 53:230–243

    Article  Google Scholar 

  6. Cao P, Yang J, Li W, Zhao D, Zaiane O (2014) Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD. Comput Med Imaging Graph 38(3):137–150

    Article  Google Scholar 

  7. Choi W-J, Choi T-S (2012) Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Inf Sci 212:57–78

    Article  Google Scholar 

  8. Choi W-J, Choi T-S (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Prog Biomed 113(1):37–54

    Article  Google Scholar 

  9. da Silva GL, da Silva Neto OP, Silva AC, de Paiva AC, Gattass M (2017) Lung nodules diagnosis based on evolutionary convolutional neural network. Multimed Tools Appl 76(18):19039–19055

    Article  Google Scholar 

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, pp 886–893

  11. 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. Artif Intell Med 60(3):165–177

    Article  Google Scholar 

  12. Dehmeshki J, Ye X, Lin X, Valdivieso M, Amin H (2007) Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput Med Imaging Graph 31(6):408–417

    Article  Google Scholar 

  13. Dou Q, Chen H, Yu L, Qin J, Heng P-A (2017) Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558–1567

    Article  Google Scholar 

  14. Farahani FV, Ahmadi A, Zarandi MHF (2018) Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math Comput Simul 149:48–68

    Article  MathSciNet  Google Scholar 

  15. Foody GM, Mathur A (2004) A relative evaluation of multiclass image classification by support vector machines. IEEE Trans Geosci Remote Sens 42(6):1335–1343

    Article  Google Scholar 

  16. Han H, Li L, Han F, Song B, Moore W, Liang Z (2015) Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE Journal of Biomedical and Health Informatics 19(2):648–659

    Article  Google Scholar 

  17. Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics (6):610–621

  18. Huidrom R, Chanu YJ, Singh KM (2019) Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme. SIViP 13(1):53–60

    Article  Google Scholar 

  19. Jaffar MA, Zia MS, Hussain M, Siddiqui AB, Akram S, Jamil U (2018) An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance. Multimed Tools Appl:1–27

  20. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vis 1(4):321–331

    Article  MATH  Google Scholar 

  21. Khan MA, Akram T, Sharif M, Javed MY, Muhammad N, Yasmin M (2018) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pattern Anal Applic:1–21

  22. Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Prog Biomed 113(1):202–209

    Article  Google Scholar 

  23. Li G-Z, Bu H-L, Yang MQ, Zeng X-Q, Yang JY (2008) Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis. BMC Genomics 9(2):S24

    Article  Google Scholar 

  24. Li H, Wang Y, Liu KR, Lo S-C, Freedman MT (2001) Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation. IEEE Trans Med Imaging 20(4):289–301

    Article  Google Scholar 

  25. Lu L, Tan Y, Schwartz LH, Zhao B (2015) Hybrid detection of lung nodules on CT scan images. Med Phys 42(9):5042–5054

    Article  Google Scholar 

  26. Magalhães Barros Netto S, Corrêa Silva A, Acatauassú Nunes R, Gattass M (2012) Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput Biol Med 42(11):1110–1121

    Article  Google Scholar 

  27. Mattoccia S, Tombari F, Di Stefano L (2011) Efficient template matching for multi-channel images. Pattern Recogn Lett 32(5):694–700

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. Mukherjee I, Rudin C, Schapire RE (2013) The rate of convergence of AdaBoost. The Journal of Machine Learning Research 14(1):2315–2347

    MathSciNet  MATH  Google Scholar 

  30. Mukhopadhyay S (2016) A Segmentation Framework of Pulmonary Nodules in Lung CT Images. J Digit Imaging 29(1):86–103

    Article  MathSciNet  Google Scholar 

  31. Naqi SM, Sharif M (2017) Recent Developments in Computer Aided Diagnosis for Lung Nodule Detection from CT images: A Review. Current Medical Imaging Reviews 13(1):3–19

    Article  Google Scholar 

  32. Naqi SM, Sharif M, Jaffar A (2018) Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput & Applic:1–19

  33. Naqi SM, Sharif M, Yasmin M (2018) multi stage segmentation model and SVM-ensemble for precise lung nodule detection. Int J Comput Assist Radiol Surg. https://doi.org/10.1007/s11548-018-1715-9

  34. Naqi S, Sharif M, Yasmin M, Fernandes SL (2018) Lung Nodule Detection Using Polygon Approximation and Hybrid Features from CT Images. Current Medical Imaging Reviews 14(1):108–117

    Article  Google Scholar 

  35. Netto SMB, Silva AC, de Paiva AC, Nunes RA, Gattass M (2017) Unsupervised detection of density changes through principal component analysis for lung lesion classification. Multimed Tools Appl 76(18):18929–18954

    Article  Google Scholar 

  36. Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg:1–10

  37. Organization WH (2019) Cancer fact sheet http://www.who.int/mediacentre/factsheets/fs297/en/. Accessed 14/01/2019 2019

  38. Rätsch G, Onoda T, Müller K-R (2001) Soft margins for AdaBoost. Mach Learn 42(3):287–320

    Article  MATH  Google Scholar 

  39. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJ, Kazerooni EA, Yankelevitz D, McNitt-Gray MF, McLennan G (2007) The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Acad Radiol 14(12):1475–1485

    Article  Google Scholar 

  40. Reeves AP, Kostis WJ (2000) Computer-aided diagnosis of small pulmonary nodules. In: Seminars in Ultrasound, CT and MRI. vol 2. Elsevier, pp 116–128

  41. Samanthula BK, Elmehdwi Y, Jiang W (2015) K-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans Knowl Data Eng 27(5):1261–1273

    Article  Google Scholar 

  42. Sezgin M (2004) Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1):146–168

    Article  Google Scholar 

  43. Shaukat F, Raja G, Ashraf R, Khalid S, Ahmad M, Ali A (2019) Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features. J Ambient Intell Humaniz Comput:1–15

  44. Shen S, Bui AA, Cong J, Hsu W (2015) An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 57:139–149

    Article  Google Scholar 

  45. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69(1):7–34

    Article  Google Scholar 

  46. Sluimer I, Schilham A, Prokop M, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25(4):385–405

    Article  Google Scholar 

  47. Taşcı E, Uğur A (2015) Shape and texture based novel features for automated juxtapleural nodule detection in lung cts. J Med Syst 39(5):46

    Article  Google Scholar 

  48. Teramoto A, Fujita H (2018) Automated Lung Nodule Detection Using Positron Emission Tomography/Computed Tomography. In: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Springer, pp 87–110

  49. Ukil S, Reinhardt JM (2009) Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Trans Med Imaging 28(2):202–214

    Article  Google Scholar 

  50. Wang Z, Hu Y, Wang Y, Han W, Wang L, Xue F, Sui X, Song W, Shi R, Jiang J (2016) Can CT Screening Give Rise to a Beneficial Stage Shift in Lung Cancer Patients? Systematic Review and Meta-Analysis. PLoS One 11(10):e0164416

    Article  Google Scholar 

  51. Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms. Appl Soft Comput 18:261–276

    Article  Google Scholar 

  52. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Transactions on Biomedical Engineering 56(7):1810–1820

    Article  Google Scholar 

  53. Yim Y, Hong H (2008) Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images. Comput Biol Med 38(8):845–857

    Article  Google Scholar 

  54. Zhang H (2005) Exploring conditions for the optimality of naive Bayes. Int J Pattern Recognit Artif Intell 19(02):183–198

    Article  Google Scholar 

  55. Zhang W, Wang X, Li X, Chen J (2018) 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets. Comput Biol Med 92:64–72

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

    Syed Muhammad Naqi & Muhammad Sharif

  2. Department of Computer Science, University of Gujrat, Gujrat, Pakistan

    Ikram Ullah Lali

Authors
  1. Syed Muhammad Naqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Sharif
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ikram Ullah Lali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Syed Muhammad Naqi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naqi, S.M., Sharif, M. & Lali, I.U. A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection. Multimed Tools Appl 78, 26287–26311 (2019). https://doi.org/10.1007/s11042-019-07819-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-07819-3

Keywords

Search

Navigation