Skip to main content

Advertisement

SpringerLink
Log in

HoTPiG: a novel graph-based 3-D image feature set and its applications to computer-assisted detection of cerebral aneurysms and lung nodules

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

A novel image feature set named histogram of triangular paths in graph (HoTPiG) is presented. The purpose of this study is to evaluate the feasibility of the proposed HoTPiG feature set through two clinical computer-aided detection tasks: nodule detection in lung CT images and aneurysm detection in head MR angiography images.

Methods

The HoTPiG feature set is calculated from an undirected graph structure derived from a binarized volume. The features are derived from a 3-D histogram in which each bin represents a triplet of shortest path distances between the target node and all possible node pairs near the target node. First, the vessel structure is extracted from CT/MR volumes. Then, a graph structure is extracted using an 18-neighbor rule. Using this graph, a HoTPiG feature vector is calculated at every foreground voxel. After explicit feature mapping with an exponential-χ2 kernel, each voxel is judged by a linear support vector machine classifier. The proposed method was evaluated using 300 CT and 300 MR datasets.

Results

The proposed method successfully detected lung nodules and cerebral aneurysms. The sensitivity was about 80% when the number of false positives was three per case for both applications.

Conclusions

The HoTPiG image feature set was presented, and its high general versatility was shown through two medical lesion detection applications.

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

Similar content being viewed by others

References

  1. Nomura Y, Hayashi N, Masutani Y, Yoshikawa T, Nemoto M, Hanaoka S, Miki S, Maeda E, Ohtomo K (2010) CIRCUS: an MDA platform for clinical image analysis in hospitals. Trans Mass Data Anal Images Signals 2(1):112–127

    Google Scholar 

  2. Hentschke CM, Beuing O, Nickl R, Tönnies KD (2011) Automatic cerebral aneurysm detection in multimodal angiographic images. In: Nuclear science symposium and medical imaging conference (NSS/MIC). IEEE, pp 3116–3120

  3. Li Q, Li F, Doi K (2008) Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol 15(2):165–175. https://doi.org/10.1016/j.acra.2007.09.018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Sone S, Doi K (2004) Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. 1. Acad Radiol 11(6):617–629

    Article  PubMed  Google Scholar 

  5. Hentschke CM, Beuing O, Paukisch H, Scherlach C, Skalej M, Tönnies KD (2014) A system to detect cerebral aneurysms in multimodality angiographic data sets. Med Phys 41(9):091904

    Article  PubMed  Google Scholar 

  6. Nomura Y, Masutani Y, Miki S, Nemoto M, Hanaoka S, Yoshikawa T, Hayashi N, Ohtomo K (2014) Performance improvement in computerized detection of cerebral aneurysms by retraining classifier using feedback data collected in routine reading environment. J Biomed Graph Comput 4(4):12

    Google Scholar 

  7. Nakao T, Hanaoka S, Nomura Y, Sato I, Nemoto M, Miki S, Maeda E, Yoshikawa T, Hayashi N, Abe O (2018) Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging 47(4):948–953

    Article  PubMed  Google Scholar 

  8. 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. https://doi.org/10.1016/j.media.2010.02.004

    Article  PubMed  Google Scholar 

  9. 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. https://doi.org/10.1109/TBME.2009.2017027

    Article  PubMed  Google Scholar 

  10. Kobashi S, Kondo K, Yutaka H (2006) Computer-aided diagnosis of intracranial aneurysms in MRA images with case-based reasoning. IEICE Trans Inf Syst 89(1):340–350

    Article  Google Scholar 

  11. Suniaga S, Werner R, Kemmling A, Groth M, Fiehler J, Forkert ND (2012) Computer-aided detection of aneurysms in 3D time-of-flight MRA datasets. In: International workshop on machine learning in medical imaging. Springer, Berlin, pp 63–69

    Chapter  Google Scholar 

  12. Yang X, Blezek DJ, Cheng LT, Ryan WJ, Kallmes DF, Erickson BJ (2011) Computer-aided detection of intracranial aneurysms in MR angiography. J Digit Imaging 24(1):86–95

    Article  PubMed  Google Scholar 

  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. https://doi.org/10.1016/j.compbiomed.2012.09.002

    Article  CAS  PubMed  Google Scholar 

  14. Teramoto A, Fujita H (2012) Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int J Comput Assist Radiol Surg 8(2):193–205. https://doi.org/10.1007/s11548-012-0767-5

    Article  PubMed  Google Scholar 

  15. 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. https://doi.org/10.1118/1.3633941

    Article  PubMed  Google Scholar 

  16. Uchiyama Y, Ando H, Yokoyama R, Hara T, Fujita H, Iwama T (2005) Computer-aided diagnosis scheme for detection of unruptured intracranial aneurysms in MR angiography. In: 2005 IEEE engineering in medicine and biology 27th annual conference, pp 3031–3034

  17. Hanaoka S, Nomura Y, Nemoto M, Miki S, Yoshikawa T, Hayashi N, Ohtomo K, Masutani Y, Shimizu A (2015) HoTPiG: a novel geometrical feature for vessel morphometry and its application to cerebral aneurysm detection. In: Navab N, Hornegger J, Wells MW, Frangi FA (eds) Medical image computing and computer-assisted intervention—MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, Part II. Springer, Cham, pp 103–110. https://doi.org/10.1007/978-3-319-24571-3_13

    Google Scholar 

  18. Nomura Y, Nemoto M, Masutani Y, Hanaoka S, Yoshikawa T, Miki S, Maeda E, Hayashi N, Yoshioka N, Ohtomo K (2014) Reduction of false positives at vessel bifurcations in computerized detection of lung nodules. J Biomed Graph Comput 4(3):36

    Google Scholar 

  19. Sreekanth V, Vedaldi A, Zisserman A, Jawahar C (2010) Generalized RBF feature maps for efficient detection. In: Proceedings of the British machine vision conference (BMVC), 31 August–3 September 2010, Aberystwyth, pp 1–11

  20. van Ginneken B, Armato SG, de Hoop B, van Amelsvoort-van de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, Camarlinghi N, Bagagli F, Gori I, Hara T, Fujita H, Gargano G, Bellotti R, Tangaro S, Bolaños L, Carlo FD, Cerello P, Cristian Cheran S, Lopez Torres E, Prokop M (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Image Anal 14(6):707–722. https://doi.org/10.1016/j.media.2010.05.005

    Article  PubMed  Google Scholar 

  21. Nomura Y, Masutani Y, Kudo S, Nakaguchi T, Hanaoka S, Nemoto M, Miki S, Yoshikawa T, Hayashi N (2015) Preliminary study for training and evaluation of various computerized detection software of cerebral aneurysms in-hospital testbed environment. Int J Comput Assist Radiol Surg 10(Suppl. 1):S132–S133

    Google Scholar 

  22. Setio AAA, Traverso A, de Bel T, Berens MSN, Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, Gugten R, Heng PA, Jansen B, de Kaste MMJ, Kotov V, Lin JY-H, Manders JTMC, Sóñora-Mengana A, García-Naranjo JC, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ET, Scholten L, Snoeren MM, Torres EL, Vandemeulebroucke J, Walasek N, Zuidhof GCA, Ginneken B, Jacobs C (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal 42:1–13. https://doi.org/10.1016/j.media.2017.06.015

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported in part by JSPS Grants-in-Aid for Scientific Research KAKENHI Grant Nos. 17H05282, 15K19775 and 18K12095. I would like to apply this paper for the IJCARS special issue on JAMIT 2017 and 2018.

Author information

Authors and Affiliations

  1. Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Shouhei Hanaoka, Takahiro Nakao & Osamu Abe

  2. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan

    Yukihiro Nomura, Tomomi Takenaga, Masaki Murata, Soichiro Miki, Takeharu Yoshikawa & Naoto Hayashi

  3. Tokyo University of Agriculture and Technology, Tokyo, Japan

    Akinobu Shimizu

Authors
  1. Shouhei Hanaoka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukihiro Nomura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tomomi Takenaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masaki Murata
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takahiro Nakao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Soichiro Miki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takeharu Yoshikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Naoto Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Osamu Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Akinobu Shimizu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shouhei Hanaoka.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1975 Helsinki declaration, as revised in 2008(5). For this type of study, formal consent is not required.

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

Hanaoka, S., Nomura, Y., Takenaga, T. et al. HoTPiG: a novel graph-based 3-D image feature set and its applications to computer-assisted detection of cerebral aneurysms and lung nodules. Int J CARS 14, 2095–2107 (2019). https://doi.org/10.1007/s11548-019-01942-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-019-01942-0

Keywords

Search

Navigation