Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 7/2018

28-02-2018 | Original Article

Multistage segmentation model and SVM-ensemble for precise lung nodule detection

Authors: Syed Muhammad Naqi, Muhammad Sharif, Mussarat Yasmin

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 7/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Purpose

Lung cancer detection at its initial stages increases the survival chances of patients. Automatic detection of lung nodules facilitates radiologists during the diagnosis. However, there is a challenge of false positives in automated systems which may lead to wrong findings. Precise segmentation facilitates to accurately extract nodules from lung CT images in order to improve performance of the diagnostic method.

Methods

A multistage segmentation model is presented in this study. The lung region is extracted by applying corner-seeded region growing combined with differential evolution-based optimal thresholding. In addition to this, morphological operations are applied in boundary smoothing, hole filling and juxtavascular nodule extraction. Geometric properties along with 3D edge information are applied to extract nodule candidates. Geometric texture features descriptor (GTFD) followed by support vector machine-based ensemble classification is employed to distinguish actual nodules from the candidate set.

Results

A publicly available dataset, namely lung image database consortium and image database resource initiative, is used to evaluate performance of the proposed method. The classification is performed over GTFD feature vector and the results show 99% accuracy, 98.6% sensitivity and 98.2% specificity with 3.4 false positives per scan (FPs/scan).

Conclusion

A lung nodule detection method is presented to facilitate radiologists in accurately diagnosing cancer from CT images. Results indicate that the proposed method has not only reduced FPs/scan but also significantly improved sensitivity as compared to related studies.
previous article Force estimation from OCT volumes using 3D CNNs
next article In vitro assessment of the accuracy of digital impressions prepared using a single system for full-arch restorations on implants

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
2.
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–suppl):108S–130SCrossRef
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):476–485CrossRefPubMedPubMedCentral
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):2418–2429CrossRefPubMedPubMedCentral
5.
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):331–338CrossRefPubMed
6.
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):845–857CrossRefPubMed
7.
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):3–19CrossRef
8.
van Ginneken B, Armato SG III, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6):707–722CrossRefPubMed
9.
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):5630–5645CrossRefPubMed
10.
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):408–417CrossRefPubMed
11.
Ozekes S, Osman O, Ucan ON (2008) Nodule detection in a lung region that’s segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding. Korean J Radiol 9(1):1–9CrossRefPubMedPubMedCentral
12.
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):37–54CrossRefPubMed
13.
Taşcı E, Uğur A (2015) Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs. J Med Syst 39(5):46CrossRefPubMed
14.
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):1962–1971CrossRefPubMed
15.
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):1810–1820CrossRefPubMed
16.
Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 29(1):86–103CrossRefPubMed
17.
Armato SG III, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28(8):1552–1561CrossRefPubMed
18.
Elizabeth DS, Nehemiah HK, Raj C, Kannan A (2012) A novel segmentation approach for improving diagnostic accuracy of CAD systems for detecting lung cancer from chest computed tomography images. J Data Inf Qual 3(2):4
19.
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 Inform 19(2):648–659CrossRefPubMed
20.
Tan Y, Schwartz LH, Zhao B (2013) Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 40(4):4350201–4350210CrossRef
21.
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 57:139–149CrossRefPubMed
22.
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):1098–1109CrossRefPubMed
23.
Kuruvilla J, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113(1):202–209CrossRefPubMed
24.
Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J Comput Assist Radiol Surg 12(10):1799–1808CrossRefPubMed
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. Artif Intell Med 60(3):165–177CrossRefPubMed
26.
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):193–205CrossRefPubMed
27.
Cuevas E, Zaldivar D, Pérez-Cisneros M (2010) A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst Appl 37(7):5265–5271CrossRef
28.
Dhara AK, Mukhopadhyay S, Mehre SA, Khandelwal N, Prabhakar N, Garg M, Kalra N (2017) A study of retrieval accuracy of pulmonary nodules based on external attachment. In: SPIE medical imaging. International Society for Optics and Photonics, pp 101343T–101346
29.
Lafarge F, Descombes X (2010) Geometric feature extraction by a multimarked point process. IEEE Trans Pattern Anal Mach Intell 32(9):1597–1609CrossRefPubMed
30.
Haralick RM, Shanmugam K (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621CrossRef
31.
Yang F, Xu Y-Y, Wang S-T, Shen H-B (2014) Image-based classification of protein subcellular location patterns in human reproductive tissue by ensemble learning global and local features. Neurocomputing 131:113–123CrossRef
32.
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):915–931CrossRefPubMedPubMedCentral
33.
Moyer VA (2014) Screening for lung cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 160(5):330–338CrossRefPubMed
34.
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):2139–2147CrossRefPubMed
Metadata
Title
Multistage segmentation model and SVM-ensemble for precise lung nodule detection
Authors
Syed Muhammad Naqi
Muhammad Sharif
Mussarat Yasmin
Publication date
28-02-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 7/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1715-9

Other articles of this Issue 7/2018

International Journal of Computer Assisted Radiology and Surgery 7/2018 Go to the issue

Original Article

Laparoscopic ultrasound manipulator with a spring-based elastic mechanism

Original Article

Development of a vibration haptic simulator for shoulder arthroplasty

Original Article

A hybrid image fusion system for endovascular interventions of peripheral artery disease

Original Article

Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method

Original Article

Force estimation from OCT volumes using 3D CNNs

Original Article

Automatic preoperative planning of DBS electrode placement using anatomo-clinical atlases and volume of tissue activated

Premium Partner

    Image Credits