Lupsix: A Cascade Framework for Lung Parenchyma Segmentation in Axial CT Images

Hasan Koyuncu

doi:10.18201/ijisae.2018448460

Authors

Hasan Koyuncu Konya Technical University Faculty of Engineering and Natural Sciences Electrical & Electronics Engineering Department http://orcid.org/0000-0003-4541-8833

DOI:

https://doi.org/10.18201/ijisae.2018448460

Keywords:

Axial Analysis, Computed Tomography, Cascade Framework, Lung Parenchyma, Medical Image Segmentation

Abstract

Lung imaging and computer aided diagnosis (CAD) play a critical role in detection of lung diseases. The most significant part of a lung based CAD is to fulfil the parenchyma segmentation, since disease information is kept in the parenchyma texture. For this purpose, parenchyma segmentation should be accurately performed to find the necessary diagnosis to be used in the treatment. Besides, lung parenchyma segmentation remains as a challenging task in computed tomography (CT) owing to the handicaps oriented with the imaging and nature of parenchyma. In this paper, a cascade framework involving histogram analysis, morphological operations, mean shift segmentation (MSS) and region growing (RG) is proposed to perform an accurate segmentation in thorax CT images. In training data, 20 axial CT images are utilized to define the optimum parameter values, and 150 images are considered as test data to objectively evaluate the performance of system. Five statistical metrics are handled to carry out the performance assessment, and a literature comparison is realized with the state-of-the-art techniques. As a result, parenchyma tissues are segmented with success rates as 98.07% (sensitivity), 99.72% (specificity), 99.3% (accuracy), 98.59% (Dice similarity coefficient) and 97.23% (Jaccard) on test dataset.

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Author Biography

Hasan Koyuncu, Konya Technical University Faculty of Engineering and Natural Sciences Electrical & Electronics Engineering Department

He graduated from Electrical and Electronics Engineering Department of Selcuk University with B.Sc. degree in 2011, with M.Sc. degree in 2013, and with Ph.D. degree in 2018.
His bacholar, master and doctoral thesis are respectively related to medical signal analysis, medical pattern classification and medical image analysis.
His current research interests are hybrid classifiers, ensemble learning, image segmentation, image denoising, image reconstruction, medical image analysis, pattern recognition and optimization.

References

S. Zhou, Y. Cheng, and S. Tamura, “Automated lung segmentation and smoothing techniques for inclusion of juxtapleural nodules and pulmonary vessels on chest CT images,” Biomed. Signal Process., vol. 13, pp. 62-70, 2014.

S. Shen, A. A. Bui, J. Cong, and W. Hsu, “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy,” Comput. Biol. Med., vol. 57, pp. 139-149, 2015.

M. Obert, M. Kampschulte, R. Limburg, S. Barańczuk, and G. A. Krombach, “Quantitative computed tomography applied to interstitial lung diseases,” Eur. J. Radiol., 2018.

M. Z. Ur Rehman, M. Javaid, S. I. A. Shah, S. O. Gilani, M. Jamil, and S. I. Butt, “An appraisal of nodules detection techniques for lung cancer in CT images,” Biomed. Signal Process., vol. 41, pp. 140-151, 2018.

A. Mansoor, U. Bagci, B. Foster, Z. Xu, G. Z. Papadakis, L. R. Folio, J. K. Udupa, and D. J. Mollura, “Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends,” RadioGraphics, vol 35, no. 4, pp. 1056-1076, 2015.

B. A. Skourt, A. El Hassani, and A. Majda, “Lung CT Image Segmentation Using Deep Neural Networks,” Procedia Comput. Sci., vol. 127, pp. 109-113, 2018.

X. Liao, J. Zhao, C. Jiao, L. Lei, Y. Qiang, and Q. Cui, “A segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest,” Plos One, vol. 11, no. 8, e0160556, 2016.

T. Doel, D. J. Gavaghan, and V. Grau, “Review of automatic pulmonary lobe segmentation methods from CT,” Comput. Med. Imag. Grap., vol. 40, pp. 13-29, 2015.

Y. Wei, G. Shen, and J. J. Li, “A fully automatic method for lung parenchyma segmentation and repairing,” J. Digit. Imaging, vol. 26, no.3, pp. 483-495, 2013.

S. Dai, K. Lu, J. Dong, Y. Zhang, and Y. Chen, “A novel approach of lung segmentation on chest CT images using graph cuts,” Neurocomputing, vol. 168, pp. 799-807, 2015.

N. M. Noor, J. C. Than, O. M. Rijal, R. M. Kassim, A. Yunus, A. A. Zeki, M. Anzidei, L. Saba, and J. S. Suri, “Automatic lung segmentation using control feedback system: morphology and texture paradigm,” J. Med. Syst., vol. 39, no.3, 22, 2015.

A. R. Pulagam, G. B. Kande, V. K. R. Ede, and R. B. Inampudi, “Automated lung segmentation from HRCT scans with diffuse parenchymal lung diseases,” J. Digit. Imaging, vol. 29, no.4, pp. 507-519, 2016.

S. H. Chae, H. M. Moon, Y. Chung, J. Shin, and S. B. Pan, “Automatic lung segmentation for large-scale medical image management,” Multimed. Tools Appl., vol. 75, no.23, pp. 15347-15363, 2016.

W. Zhang, X. Wang, P. Zhang, and J. Chen, “Global optimal hybrid geometric active contour for automated lung segmentation on CT images,” Comput. Biol. Med., vol. 91, pp. 168-180, 2017.

S. Armato III, G. McLennan, L. Bidaut, M. McNitt-Gray, C. Meyer, A. Reeves, and L. Clarke, “Data from LIDC-IDRI,” The cancer imaging archive, 2015

S. G. Armato et al., “The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans,” Med. Phys., vol. 38, no.2, pp. 915-931, 2011.

K. Clark et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository,” J. Digit. Imaging, vol. 26, no. 6, pp. 1045-1057, 2013.

P. Charles, “Digital Video and HDTV Algorithms and Interfaces,” Morgan Kaufmann Publishers, San Francisco, 2003.

R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital image processing using MATLAB,” McGraw Hill Education, New York, 2009.

H. Koyuncu, R. Ceylan, M. Sivri, H. Erdogan, “An Efficient Pipeline for Abdomen Segmentation in CT Images,” J. Digit. Imaging, vol. 31, no.2, pp. 262-274, 2018.

K. Zuiderveld, “Contrast limited adaptive histogram equalization,” in: Graphics gems IV, P. S. Heckbert, Ed. San Diego, CA, USA: Academic Press Professional Inc., 1994, pp. 474–485.

P. Soille, “Morphological Image Analysis: Principles and Applications,” Springer-Verlag, 1999, pp. 173-174.

D. Comaniciu, P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE T. Pattern Anal., vol. 24, no. 5, pp. 603-619, 2002.

H. Koyuncu, R. Ceylan, H. Erdogan, M. Sivri, “A novel pipeline for adrenal tumour segmentation,” Comput. Meth. Prog. Bio., vol. 159, pp. 77-86, 2018.

S. W. Franklin, S. E. Rajan, “Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images,” Biocybern. Biomed. Eng., vol. 34, pp. 117-124, 2014.