Abstract
Early detection and treatment opportunities for lung cancer is reduced the mortality of this disease. Chest radiography is one of the commonly used screening methods for the preliminary diagnosis of lung cancer. In this study, analgorithm fornodule detection in chest radiograph imageis presented. It takes into account the suspicious salient regions. Firstly, in order to enhance the image contrast, the CLAHE filter is applied. Then, local maximal regions are extracted by multi-scale approach based on optimum lifetime. Some of theseregions are eliminated by decision tree using the morphologic and the intensity features for detection and segmentation of candidate nodules. Finally, the texture features extracted from the segmented regions are classified by using RusBoost method. The method has been tested on the JSRT (Japanese Society of Radiological Technology) database images. Experimental results demonstrate that theproposed method achieves a very satisfactory performance for detection and segmentation of the suspicious salientregionsat the same time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Siegel, Rebecca L., Kimberly D. Miller, and Ahmedin Jemal. “Cancer statistics, 2016.” CA: a cancer journal for clinicians 66.1 (2016): 7-30.
Schalekamp, Steven, et al. “Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.” Radiology 272.1 (2014): 252-261.
White, Charles S., et al. “Use of a Computer-aided Detection System to Detect Missed Lung Cancer at Chest Radiography 1.” Radiology 252.1 (2009): 273-281.
Kligerman, Seth, Ling Cai, and Charles S. White. “The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph.” Journal of thoracic imaging 28.4 (2013): 244-252.
Amir, Guy J., and Harold P. Lehmann. “After Detection:: The Improved Accuracy of Lung Cancer Assessment Using Radiologic Computer-aided Diagnosis.” Academic radiology 23.2 (2016): 186-191.
Schalekamp, S., et al. “Effect of Computer-aided detection on the Detection of Pulmonary Nodules.” ADVANCED PROCESSING IN CHEST RADIOGRAPHY (2015): 89.
Li, Feng, et al. “Lung Cancers Missed on Chest Radiographs: Results Obtained with a Commercial Computer-aided Detection Program 1.” Radiology 246.1 (2008): 273-280.
Schalekamp, S. Advanced processing in chest radiography: impact on observer performance. Diss. [Sl: sn], 2015.
Hardie, Russell C., et al. “Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs.” Medical Image Analysis 12.3 (2008): 240-258.
Schilham, Arnold MR, Bram van Ginneken, and Marco Loog. “Multi-scale nodule detection in chest radiographs.” Medical Image Computing and Computer-Assisted Intervention-MICCAI 2003. Springer Berlin Heidelberg, 2003. 602-609.
Chen, Sheng, Kenji Suzuki, and Heber MacMahon. “Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification.” Medical physics 38.4 (2011): 1844-1858.
Chen, Sheng, and Kenji Suzuki. “Computerized detection of lung nodules by means of “virtual dual-energy” radiography.” IEEE Transactions on Biomedical Engineering 60.2 (2013): 369-378.
Dominguez, Alfonso Rojas, and Asoke K. Nandi. “Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection.” Computerized Medical Imaging and Graphics 32.4 (2008): 304-315.
Hong, Byung-Woo, and Bong-Soo Sohn. “Segmentation of regions of interest in mammograms in a topographic approach.” IEEE Transactions on Information Technology in Biomedicine 14.1 (2010): 129-139.
Zuiderveld, Karel. “Contrast limited adaptive histogram equalization.” Graphics gems IV. Academic Press Professional, Inc., 1994.
Keserci, Bilgin, and Hiroyuki Yoshida. “Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model.” Medical Image Analysis 6.4 (2002): 431-447.
Shi, Zhenghao, et al. “A computer aided pulmonary nodule detection system using multiple massive training svms.” Appl. Math 7.3 (2013): 1165-1172.
Dey, Emon Kumar, and Hossain Muhammad Muctadir. “Chest X-ray analysis to detect mass tissue in lung.” Informatics, Electronics & Vision (ICIEV), 2014 International Conference on. IEEE, 2014.
Theresa, M. Mercy, and V. Subbiah Bharathi. “CAD for lung nodule detection in chest radiography using complex wavelet transform and shearlet transform features.” Indian Journal of Science and Technology 9.1 (2016).
Haralick, Robert M., and Karthikeyan Shanmugam. “Textural features for image classification.” IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ture, H., Kayikcioglu, T. (2017). Detection and Segmentation of Nodules in Chest Radiographs Based on Lifetime Approach. In: Badnjevic, A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-10-4166-2_82
Download citation
DOI: https://doi.org/10.1007/978-981-10-4166-2_82
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4165-5
Online ISBN: 978-981-10-4166-2
eBook Packages: EngineeringEngineering (R0)