research-article

A framework for the decomposition and features extraction from lung DICOM images

Authors:

Pietro Cinaglia,

Giuseppe Tradigo,

Giuseppe L. Cascini,

Pierangelo VeltriAuthors Info & Claims

IDEAS '18: Proceedings of the 22nd International Database Engineering & Applications Symposium

Pages 31 - 36

https://doi.org/10.1145/3216122.3216127

Published: 18 June 2018 Publication History

Abstract

Extracting morphological features from DICOM images is useful to obtain numerical anatomic values for population-wide studies. Currently software tools on medical devices are able to extract some parameters that can indicate the presence of diseases. Nevertheless, there still is a lot of not exploited information contained in images which can be useful for research as well as to characterize human behavior. For instance, measures for lung volume compared with reference data sets can be studied starting from clinical images.

In this paper we report preliminary results on a framework for the acquisition and decomposition of DICOM images applied on a dataset containing lung exams from which we extracted information and parameters useful for disease research studies. The here proposed algorithms for images segmentation and anatomical features extraction have been tested on a clinical dataset obtained from University Hospital of Catanzaro, providing the framework validity.

References

[1]

U. Bashir, G. Azad, M. M. Siddique, S. Dhillon, N. Patel, P. Bassett, D. Landau, V. Goh, and G. Cook. The effects of segmentation algorithms on the measurement of18F-FDG PET texture parameters in non-small cell lung cancer. EJNMMI Res, 7(1):60, Dec 2017.

[2]

C. H. Chen, C. K. Chang, C. Y. Tu, W. C. Liao, B. R. Wu, K. T. Chou, Y. R. Chiou, S. N. Yang, G. Zhang, and T. C. Huang. Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE, 13(2):e0192002, 2018.

[3]

P. Cinaglia, P. Veltri, and M. Cannataro. emiro: an ontology-based system for clinical data integration and analysis. In SEBD, 2017.

[4]

S. Echegaray, S. Bakr, D. L. Rubin, and S. Napel. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J Digit Imaging, Oct 2017.

[5]

K. Edmunds, M. Gislason, S. Sigur?sson, V. Gu?nason, T. Harris, U. Carraro, and P. Gargiulo. Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities. PLoS ONE, 13(3):e0193241, 2018.

[6]

N. Garg, M. Wang, E. Hyde, R. R. da Silva, A. V. Melnik, I. Protsyuk, A. Bouslimani, Y. W. Lim, R. Wong, G. Humphrey, G. Ackermann, T. Spivey, S. S. Brouha, N. Bandeira, G. Y. Lin, F. Rohwer, D. J. Conrad, T. Alexandrov, R. Knight, and P. C. Dorrestein. Three-Dimensional Microbiome and Metabolome Cartography of a Diseased Human Lung. Cell Host Microbe, 22(5):705--716, Nov 2017.

[7]

E. Gouillart, J. Nunez-Iglesias, and S. van der Walt. Analyzing microtomography data with Python and the scikit-image library. Adv Struct Chem Imaging, 2(1):18, 2017.

[8]

S. Hamidian, B. Sahiner, N. Petrick, and A. Pezeshk. 3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT. Proc SPIE Int Soc Opt Eng, 10134, 2017.

[9]

W. A. Kalender. X-ray computed tomography. Physics in Medicine and Biology, 51(13):R29, 2006.

[10]

H. Kamata, N. Higashihori, H. Fukuoka, M. Shiga, T. Kawamoto, and K. Moriyama. Comprehending the three-dimensional mandibular morphology of facial asymmetry patients with mandibular prognathism. Prog Orthod, 18(1):43, Dec 2017.

[11]

M. Larobina and L. Murino. Medical image file formats. J Digit Imaging, 27(2):200--206, Apr 2014.

[12]

Q. Li, J. Kim, Y. Balagurunathan, J. Qi, Y. Liu, K. Latifi, E. G. Moros, M. B. Schabath, Z. Ye, R.J. Gillies, and T.J. Dilling. CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol, 12(1):158, Sep 2017.

[13]

W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH Comput. Graph., 21(4):163--169, Aug. 1987.

Digital Library

[14]

H. Lusic and M. W. Grinstaff. X-ray-computed tomography contrast agents. Chem. Rev., 113(3):1641--1666, Mar 2013.

[15]

D. Mason. SuâĂŘeâĂŘtâĂŘ33: Pydicom: An open source dicom library. Medical Physics, 38(6Part10):3493--3493, 2011.

[16]

R. Palomar, F. A. Cheikh, B. Edwin, A. Beghdadhi, and O. J. Elle. Surface reconstruction for planning and navigation of liver resections. Comput Med Imaging Graph, 53:30--42, Oct 2016.

[17]

T. E. Reeves, P. Mah, and W. D. McDavid. Deriving Hounsfield units using grey levels in cone beam CT: a clinical application. Dentomaxillofac Radiol, 41(6):500--508, Sep 2012.

[18]

J. Y. Rho, D. A. Lynch, Y. J. Suh, J. W. Nah, J. A. Zach, J. D. Schroeder, C. W. Cox, R. P. Bowler, B. E. Fenster, M. T. Dransfield, J. M. Wells, J. E. Hokanson, D. Curran-Everett, A. Williams, M. K. Han, J. D. Crapo, and E. K. Silverman. CT measurements of central pulmonary vasculature as predictors of severe exacerbation in COPD. Medicine (Baltimore), 97(3):e9542, Jan 2018.

[19]

R. Smith-Bindman, J. Lipson, R. Marcus, K. P. Kim, M. Mahesh, R. Gould, A. Berrington de Gonzalez, and D. L. Miglioretti. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch. Intern. Med., 169(22):2078--2086, Dec 2009.

[20]

M. d. S. G. Tsuzuki, A. K. Sato, E. K. Ueda, T. d. C. Martins, R. Y. Takimoto, Y. Iwao, L. I. Abe, T. Gotoh, and S. Kagei. Propagation-based marching cubes algorithm using open boundary loop. The Visual Computer, Jul 2017.

[21]

S. van der Walt, J. L. Schonberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu. scikit-image: image processing in Python. Peer J, 2:e453, 2014.

[22]

P. Vizza, P. H. Guzzi, P. Veltri, A. Papa, G. L. Cascini, G. Sesti, and E. Succurro. Experiences on quantitative cardiac pet analysis. 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1148--1153, 2016.

[23]

S. v. d. Walt, S. C. Colbert, and G. Varoquaux. The numpy array: A structure for efficient numerical computation. Computing in Science and Engg., 13(2):22--30, Mar. 2011.

Digital Library

[24]

P. Xiao, X. Zhao, S. Leng, R. S. Tan, P. Wong, and L. Zhong. A Software Tool for Heart AVJ Motion Tracking Using Cine Cardiovascular Magnetic Resonance Images. IEEE J Transl Eng Health Med, 5:1900412, 2017.

Cited By

Meyer CRomero NEvangelista TCadot BLaporte JJeannin-Girardon ACollet PAyadi AChennen KPoch O(2024) IMPatienT: An I ntegrated Web Application to Digitize, Process and Explore M ultimodal PATIEN t da T a Journal of Neuromuscular Diseases10.3233/JND-23008511:4(855-870)Online publication date: 29-Apr-2024
https://doi.org/10.3233/JND-230085
Cinaglia P(2024)A Lightweight Method for Designing and Massive Processing of Genomics PipelinesIEEE Access10.1109/ACCESS.2024.346486712(168245-168255)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3464867
Cinaglia PCannataro M(2024)Quantum Computing for BioinformaticsReference Module in Life Sciences10.1016/B978-0-323-95502-7.00182-2Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95502-7.00182-2
Show More Cited By

Index Terms

A framework for the decomposition and features extraction from lung DICOM images
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics

Recommendations

CNN based Lung Indexing Method for DICOM CT Image
SMA 2020: The 9th International Conference on Smart Media and Applications

PET-CT medical images are widely used for the purpose of early diagnosis of cancer. PET-CT Imaging is a method of examing cancer by photographing the entire body and a larget set of DICOM images are generated for one patient such as 300 images. Since ...
Visualizing the Correspondence of Feature Point Mapping between DICOM Images before and after Surgery
ICBBT '19: Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical Technology

We extract feature point mapping between preoperative and postoperative Digital Imaging and Communications in Medicine (DICOM) images from magnetic resonance imaging (MRI) or from computer tomography (CT). The aim is to quantitatively investigate brain ...
Lung parenchyma segmentation from CT images based on material decomposition
ICIAR'06: Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II

We present a fully automated method for extracting the lung region from volumetric X-ray CT images based on material decomposition. By modeling the human thorax as a composition of different materials, the proposed method follows a threshold-based, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IDEAS '18: Proceedings of the 22nd International Database Engineering & Applications Symposium

June 2018

328 pages

ISBN:9781450365277

DOI:10.1145/3216122

Editor:
Bipin C. Desai
Concordia University
,
General Chairs:
Bipin C. Desai
Concordia University
,
Sergio Flesca
University of Calabria
,
Program Chairs:
Ester Zumpano
University of Calabria
,
Elio Masciari
ICAR-CNR
,
Luciano Caroprese
University of Calabria

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Concordia University: Concordia University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IDEAS 2018

IDEAS 2018: 22nd International Database Engineering & Applications Symposium

June 18 - 20, 2018

Villa San Giovanni, Italy

Acceptance Rates

Overall Acceptance Rate 74 of 210 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
84
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Meyer CRomero NEvangelista TCadot BLaporte JJeannin-Girardon ACollet PAyadi AChennen KPoch O(2024) IMPatienT: An I ntegrated Web Application to Digitize, Process and Explore M ultimodal PATIEN t da T a Journal of Neuromuscular Diseases10.3233/JND-23008511:4(855-870)Online publication date: 29-Apr-2024
https://doi.org/10.3233/JND-230085
Cinaglia P(2024)A Lightweight Method for Designing and Massive Processing of Genomics PipelinesIEEE Access10.1109/ACCESS.2024.346486712(168245-168255)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3464867
Cinaglia PCannataro M(2024)Quantum Computing for BioinformaticsReference Module in Life Sciences10.1016/B978-0-323-95502-7.00182-2Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95502-7.00182-2
Cinaglia P(2024)Time Series Analysis and Forecasting for Epidemiology and Pandemic SurveillanceReference Module in Life Sciences10.1016/B978-0-323-95502-7.00133-0Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95502-7.00133-0
Seye FDiop ADeme C(2024)A Survey for Extraction, Classification and Security of Medical Image DataInformation Systems and Technological Advances for Sustainable Development10.1007/978-3-031-75329-9_9(79-84)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-3-031-75329-9_9
Cinaglia PMilano M(2024)A Method for Inferring Candidate Disease-Disease AssociationsComputational Science – ICCS 202410.1007/978-3-031-63772-8_8(97-104)Online publication date: 28-Jun-2024
https://doi.org/10.1007/978-3-031-63772-8_8
ABENAVOLI LAQUILA ISACCO MPROCOPIO ACINAGLIA PZANZA CLONGHITANO YARENA VFAGOONEE SRICCI PLUZZA F(2023)Liver injury associated with high value of D-dimer plasmatic level in COVID-19 patientsMinerva Gastroenterology10.23736/S2724-5985.22.03189-869:1Online publication date: Feb-2023
https://doi.org/10.23736/S2724-5985.22.03189-8
Cinaglia PCannataro M(2023)A method for modelling and executing customized pipelines in serverless computing2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385856(3453-3458)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385856
Cinaglia PCannataro M(2023)A Flexible Automated Pipeline Engine for Transcript-Level Quantification from RNA-seqAdvances in Conceptual Modeling10.1007/978-3-031-22036-4_5(45-54)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-3-031-22036-4_5
Cagni EBotti AOrlandi MGalaverni MIotti CIori MLewis GSpezi E(2022)Evaluating the Quality of Patient-Specific Deformable Image Registration in Adaptive Radiotherapy Using a Digitally Enhanced Head and Neck PhantomApplied Sciences10.3390/app1219949312:19(9493)Online publication date: 22-Sep-2022
https://doi.org/10.3390/app12199493
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten