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Neural Computing and Applications
Published in: Neural Computing and Applications 9/2020

05-10-2018 | Original Article

Lung nodule detection and classification based on geometric fit in parametric form and deep learning

Authors: Syed Muhammad Naqi, Muhammad Sharif, Arfan Jaffar

Published in: Neural Computing and Applications | Issue 9/2020

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Abstract

This study presents an automated detection and classification method to facilitate the radiologists in the diagnosis process. The major problem in these systems is the inclusion of false positives in the results, which may lead to inaccurate diagnosis. A nodule detection and classification method is proposed that consists of four major phases. First, the lung region extraction is performed based on optimal gray level threshold that is computed by fractional-order Darwinian particle swarm optimization. Then, a novel nodule candidate detection method, based on geometric fit in parametric form incorporating the geometric properties of the nodules, is proposed. In the next phase, a hybrid geometric texture feature descriptor is created for better representation of the candidate nodules, which is a combination of 2D as well as 3D information about nodule candidates. Finally, a deep learning approach based on stacked autoencoder and softmax for feature reduction and classification is applied to reduce false positives. Performance analysis on the largest publically available dataset, Lung Image Database Consortium and Image Database Resource Initiative, depicts that the proposed method has significantly reduced the number of false positives to 2.8 per scan with a promising sensitivity of 95.6%. The results demonstrate the significance of the methodology in automatic lung nodule detection and classification. Furthermore, it will facilitate and provide assistance to radiologists in precise nodule detection.
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Literature
1.
Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66(1):7–30
2.
3.
Petkovska I, Brown MS, Goldin JG, Kim HJ, McNitt-Gray MF, Abtin FG, Ghurabi RJ, Aberle DR (2007) The effect of lung volume on nodule size on CT. Acad Radiol 14(4):476–485
4.
Bach PB, Mirkin JN, Oliver TK, Azzoli CG, Berry DA, Brawley OW, Byers T, Colditz GA, Gould MK, Jett JR (2012) Benefits and harms of CT screening for lung cancer: a systematic review. JAMA 307(22):2418–2429
5.
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(3):108S–130S
6.
Moyer VA (2014) Screening for lung cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 160(5):330–338
7.
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
8.
Naqi SM, Sharif M (2017) Recent developments in computer aided diagnosis for lung nodule detection from CT images: a review. Curr Med Imaging Rev 13(1):3–19
9.
Endo M, Aramaki T, Asakura K, Moriguchi M, Akimaru M, Osawa A, Hisanaga R, Moriya Y, Shimura K, Furukawa H (2012) Content-based image-retrieval system in chest computed tomography for a solitary pulmonary nodule: method and preliminary experiments. Int J Comput Assist Radiol Surg 7(2):331–338
10.
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
11.
Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12(10):1799–1808
12.
Sun W, Zheng B, Qian W (2017) Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med 89:530–539
13.
Cascio D, Magro R, Fauci F, Iacomi M, Raso G (2012) Automatic detection of lung nodules in CT datasets based on stable 3D mass–spring models. Comput Biol Med 42(11):1098–1109
14.
Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 29(1):86–103MathSciNet
15.
Teramoto A, Fujita H (2013) Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 8(2):193–205
16.
17.
Dhara AK, Mukhopadhyay S, Dutta A, Garg M, Khandelwal N (2016) A combination of shape and texture features for classification of pulmonary nodules in lung ct images. J Digit Imaging 29(4):466–475
18.
Tan M, Deklerck R, Jansen B, Bister M, Cornelis J (2011) A novel computer-aided lung nodule detection system for CT images. Med Phys 38(10):5630–5645
19.
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
20.
Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R (2011) Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys 38(4):1962–1971
21.
Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209
22.
Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 56(7):1810–1820
23.
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
24.
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
25.
Choi W-J, Choi T-S (2014) Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor. Comput Methods Programs Biomed 113(1):37–54
26.
Brown MS, Lo P, Goldin JG, Barnoy E, Kim GHJ, McNitt-Gray MF, Aberle DR (2014) Toward clinically usable CAD for lung cancer screening with computed tomography. Eur Radiol 24(11):2719–2728
27.
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 J Biomed Health Inf 19(2):648–659
28.
29.
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
30.
Liao X, Zhao J, Jiao C, Lei L, Qiang Y, Cui Q (2016) A segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest. PLoS ONE 11(8):e0160556
31.
Jaffar MA, Hussain A, Mirza AM (2010) Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images. Knowl Inf Syst 24(1):91–111
32.
Couceiro M, Ghamisi P (2016) Fractional order Darwinian particle swarm optimization. Springer International Publishing, Berlin, pp 11–20MATH
33.
Gander W, Golub GH, Strebel R (1994) Least-squares fitting of circles and ellipses. BIT Numer Math 34(4):558–578MathSciNetMATH
34.
Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257–1265
35.
Yang X, Shen X, Long J, Chen H (2012) An improved median-based Otsu image thresholding algorithm. Aasri Procedia 3:468–473
36.
Arora S, Acharya J, Verma A, Panigrahi PK (2008) Multilevel thresholding for image segmentation through a fast statistical recursive algorithm. Pattern Recogn Lett 29(2):119–125
37.
Yu J, Wang S, Xi L (2008) Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing 71(4–6):1054–1060
38.
Couceiro M, Sivasundaram S (2016) Novel fractional order particle swarm optimization. Appl Math Comput 283:36–54MathSciNetMATH
39.
Žalik B, Mongus D, Lukač N, Žalik KR (2018) Efficient chain code compression with interpolative coding. Inf Sci 439:39–49MathSciNet
40.
Duggan N, Bae E, Shen S, Hsu W, Bui A, Jones E, Glavin M, Vese L (2015) A technique for lung nodule candidate detection in CT using global minimization methods. In: International Workshop on energy minimization methods in computer vision and pattern recognition. Springer, pp 478–491
41.
Boroczky L, Zhao L, Lee KP (2006) Feature subset selection for improving the performance of false positive reduction in lung nodule CAD. IEEE Trans Inf Technol Biomed 10(3):504–511
42.
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
43.
Ananda A, Purnama I, Purnomo M (2011) CT Lung image filtering based on Max-Tree method. In: 2011 2nd International conference on instrumentation, communications, information technology, and biomedical engineering (ICICI-BME). IEEE, pp 137–140
44.
Moraru L, Moldovanu S, Biswas A (2014) Optimization of breast lesion segmentation in texture feature space approach. Med Eng Phys 36(1):129–135
45.
Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z (2015) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imaging 28(1):99–115
46.
Bibicu D, Moraru L, Biswas A (2013) Thyroid nodule recognition based on feature selection and pixel classification methods. J Digit Imaging 26(1):119–128
47.
Han F, Zhang G, Wang H, Song B, Lu H, Zhao D, Zhao H, Liang Z (2013) A texture feature analysis for diagnosis of pulmonary nodules using LIDC–IDRI database. In: 2013 IEEE international conference on medical imaging physics and engineering (ICMIPE). IEEE, pp 14-–8
48.
Naqi S, Sharif M, Yasmin M, Fernandes SL (2018) Lung nodule detection using polygon approximation and hybrid features from CT images. Curr Med Imaging Rev 14(1):108–117
49.
Farag A, Ali A, Graham J, Farag A, Elshazly S, Falk R (2011) Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro. IEEE, pp 169–172
50.
Yang J, J-y Yang, Zhang D, J-f Lu (2003) Feature fusion: parallel strategy versus serial strategy. Pattern Recogn 36(6):1369–1381MATH
51.
Sudha D, Ramakrishna M (2017) Comparative study of features fusion techniques. In: 2017 International conference on recent advances in electronics and communication technology (ICRAECT). IEEE, pp 235–239
52.
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
53.
Meng L, Ding S, Zhang N, Zhang J (2016) Research of stacked denoising sparse autoencoder. Neural Comput Appl 30(7):2083–2100
54.
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533
55.
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
56.
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
57.
Jacobs C, Rikxoort EM, Murphy K, Prokop M, Schaefer-Prokop CM, Ginneken B (2016) Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database. Eur Radiol 26(7):2139–2147
58.
McGuinness K, O’connor NE (2010) A comparative evaluation of interactive segmentation algorithms. Pattern Recogn 43(2):434–444MATH
59.
Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 6:929–944
60.
Mignotte M (2014) A label field fusion model with a variation of information estimator for image segmentation. Inf Fusion 20:7–20
61.
Vapnik V, Izmailov R (2017) Knowledge transfer in SVM and neural networks. Ann Math Artif Intell 81(1–2):3–19MathSciNetMATH
62.
Zhang S, Li X, Zong M, Zhu X, Cheng D (2017) Learning k for knn classification. ACM Trans Intell Syst Technol (TIST) 8(3):43
63.
Liu X, Lu R, Ma J, Chen L, Qin B (2016) Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE J Biomed Health Inf 20(2):655–668
64.
Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674MathSciNet
Metadata
Title
Lung nodule detection and classification based on geometric fit in parametric form and deep learning
Authors
Syed Muhammad Naqi
Muhammad Sharif
Arfan Jaffar
Publication date
05-10-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3773-x

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