International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 12, No. 1, 8 Feb. 2020
Cover page and Table of Contents: PDF (size: 861KB)
Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering
Full Text (PDF, 861KB), PP.38-45
Views: 0 Downloads: 0
Author(s)
Index Terms
Fuzzy c-means clustering, morphological reconstruction, noise robustness, computational efficiency, lung tumor segmentation, tumor staging
Abstract
Lung tumor is the result of abnormal and uncontrolled cell division and growth in lung region. Earlier detection and staging of lung tumor is of great importance to increase the survival rate of the suffered patients. In this paper, a fast and robust Fuzzy c-means clustering method is used for segmenting the tumor region from lung CT images. Morphological reconstruction process is performed prior to Fuzzy c-means clustering to achieve robustness against noises. The computational efficiency is improved through median filtering of membership partition. Tumor masks are then reconstructed using surface based and shape based filtering. Different features are extracted from the segmented tumor region including maximum diameter and the tumor stage is determined according to the tumor staging system of American Joint Commission on Cancer. 3D shape of the segmented tumor is reconstructed from series of 2D CT slices for volume measurement. The accuracy of the proposed system is found as 92.72% for 55 randomly selected images from the RIDER Lung CT dataset of Cancer imaging archive. Lower complexity in terms of iterations and connected components as well as better noise robustness are found in comparison with conventional Fuzzy c-means and k-means clustering techniques.
Cite This Paper
Rupak Bhakta, A. B. M. Aowlad Hossain, " Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.12, No.1, pp. 38-45, 2020. DOI: 10.5815/ijigsp.2020.01.05
Reference
[1]C. Zappa and S. A. Mousa, “Non-small cell lung cancer: current treatment and future advances,” Transl Lung Cancer Res., vol. 5 no. 3, pp. 288–300, June 2016.
[2]W. R. Webb, “High resolution lung computed tomography. Normal anatomic and pathologic findings.” Radiol Clin North Am., vo. 29, no. 5, pp. 1051-1063, September 1991.
[3]V. Ginneken, B. M. ter Haar Romeny, and M. Viergever. “Computer-aided diagnosis in chest radiography: A survey,” IEEE Trans. Med. Imag., vol. 20, pp.1228–1241, 2001.
[4]N. Panpaliya, N. Tadas, S. Bobade, R. Aglawe, and A. Gudadhe, “A survey on early detection and prediction of lung cancer,” International Journal of Computer Science and Mobile Computing, vol. 4, no. 1, pp. 175–184, 2015.
[5]S. Uzelaltinbulat and B. Ugur, “Lung tumor segmentation algorithm,” in 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, 22-23 August 2017.
[6]G. Xiuhua et al., “Support vector machine prediction model of early-stage lung cancer based on curvelet transform to extract texture features of CT Image,” International Journal of Biomedical and Biological Engineering, vol. 4, no. 11, pp. 539-543, 2010.
[7]R. N. G. Naguib and G. V. Sherbet, Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management, CRC Press, 2001.
[8]E. Dandil et al., “Artificial neural network-based classification system for lung nodules on computed tomography scans,” in 6th International Conference of Soft Computing and Pattern Recognition, pp.382-386, 2014.
[9]G. Jakimovski, D. Davcev, “Using double convolution neural network for lung cancer stage detection,” Appl. Sci., vol. 9, no. 3, pp. 427, 2019.
[10]C. Zhang et al., “Toward an expert level of lung cancer detection and classification using a deep convolutional neural network,” The Oncologist, April 2019.
[11]P. Sarker, M. M. H. Shuvo, Z. Hossain, and S. Hasan, “Segmentation and classification of lung tumor from 3D CT image using k-means clustering algorithm,” in 4th International Conference on Advances in Electrical Engineering, 2017.
[12]S. Sivakumar and C. Chandrasekar, “Lung Nodule Segmentation through Unsupervised Clustering Models,” Procedia Engineering, vol. 38, pp. 3064-3073, 2012.
[13]P. Afshar, A. Ahmadi, and M. H. F. Zarandi, “Lung tumor area recognition in CT images based on Gustafson-Kessel clustering,” in IEEE International Conference on Fuzzy Systems, 2016.
[14]P. B. Sangamithraa and S. Govindaraju, “Lung tumour detection and classification using EK-Mean clustering,” in International Conference on Wireless Communications, Signal Processing and Networking, 2016.
[15]T. Lei et al., “Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering,” IEEE Transactions on Fuzzy Systems, vol. 26, pp. 3027-3041, 2018.
[16]F. C. Detterbeck, “The eighth edition TNM stage classification for lung cancer: What does it mean on main street?,” The Journal of Thoracic and Cardiovascular Surgery, vol. 155, pp. 356-359, 2018.
[17]B. Zhao, L. H. Schwartz, and M. G. Kris, “Data from RIDER Lung CT,” The Cancer Imaging Archive, 2015.
[18]B. Zhao et al., “Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non--small cell lung cancer,” Radiology, vol. 252, pp. 263-272, 2009.
[19]K. Clark et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository,” Journal of Digital Imaging, vol. 26, pp. 1045-1057, 2013.
[20]Y. Nian et al., “Graph-based unsupervised segmentation for lung tumor CT images,” in 3rd IEEE International Conference on Computer and Communications, 2017.
[21]R. C. Gonzalez and R. E. Woods, Digital Image Processing, Fourth edition, Pearson, 2018.
[22]B. Liu, M. Zhu, Z. Zhang, C. Yin, Z. Liu and J. Gu, “Medical image conversion with DICOM,” in Canadian Conference on Electrical and Computer Engineering, 2007.
[23]A. Fedorov et al., “3D Slicer as an Image Computing Platform for the Quantitative Imaging Network,” Magnetic Resonance Imaging, vol. 30, pp. 1323-1341, 2012.