Texture feature extraction for the lung lesion density classification on computed tomography scan image

Main Article Content

Hasnely Hasnely
Hanung Adi Nugroho
Sunu Wibirama
Budi Windarta
Lina Choridah

Abstract

The radiology examination by computed tomography (CT) scan is an early detection of lung cancer to minimize the mortality rate. However, the assessment and diagnosis by an expert are subjective depending on the competence and experience of a radiologist. Hence, a digital image processing of CT scan is necessary as a tool to diagnose the lung cancer. This research proposes a morphological characteristics method for detecting lung cancer lesion density by using the histogram and GLCM (Gray Level Co-occurrence Matrices). The most well-known artificial neural network (ANN) architecture that is the multilayers perceptron (MLP), is used in classifying lung cancer lesion density of heterogeneous and homogeneous. Fifty CT scan images of lungs obtained from the Department of Radiology of RSUP Dr. Sardjito Hospital, Yogyakarta are used as the database. The results show that the proposed method achieved the accuracy of 98%, sensitivity of 96%, and specificity of 96%.


Downloads

Download data is not yet available.

Article Details

How to Cite
Hasnely, H., Nugroho, H. A., Wibirama, S., Windarta, B., & Choridah, L. (2016). Texture feature extraction for the lung lesion density classification on computed tomography scan image. Communications in Science and Technology, 1(1). https://doi.org/10.21924/cst.1.1.2016.14
Section
Articles

References

W. H. Organization, Cancer Media Centre, 2015. [Online]. Available: http://who.int/mediacentre/factsheets/fs297/en/. [Accessed: 09-Apr-2016].

2 Kementrian Kesehatan RI, Stop Kanker, Indonesia: Pusat Data and Informasi Kesehatan, 2015.

3 N. C. Institute, Lung Cancer—Patient Version.[Online]. Available: http://www.cancer.gov/types/lung. [Accessed: 13-Apr-2016].

4 A. Icksan, R.M . Faisal, Elisna, P. Astowo, H. Hidayat and J. Prihartono, Kriteria Diagnosis Kanker Paru Primer berdasarkan Gambaran Morfologi pada CT Scan Toraks Dibandingkan dengan Sitologi, Indonesian Journal of Cancer (2008).

5 S. Uyun, In term of : Model Komputasi penentuan faktor Resiko Kanker Payudara berdasarkan Pola dan Persentase Densitas Mamografi, Ph.D. Thesis, Universitas Gadjah Mada, Indonesia, 2014.

6 L. Devan, R. Santosham and R. Hariharan, ANOVA of Texture based Feature Set for Lung Tissue Characterization using Low-Dose CT Images, 7(1) (2014) 974-1925.

7 S. A. Patil and M. B. Kuchanur, Lung Cancer Classification Using Image Processing, 2(3) (2012) 2277-3754.

8 K. M. M. Tun and A. S. Khaing, Feature Extraction and Classification of Lung Cancer Nodule using Image Processing Techniques, 3(3) (2014) 2278–0181.

9 S. K. V. Anand, Segmentation coupled Textural Feature Classification for Lung Tumor Prediction, 10th Int. Conference Communication and Computing Technologies., Nagercoil, Tamil Nadu, India, 2010, pp. 518–524.

10 M. Y. Ahmad, A. Mohamed, Y. A. M. Yusof, and S. S. M. Ali., Colorectal Cancer Image Classification Using Image Pre-Processing and Multilayer Perceptron, Int. Conference on Computer and Information Science., Kuala Lumpur, Malaysia, 2012, pp. 275–280.

11 D. Mitrea, S. Nedevschi, M. Abrudean, and R. Badea, Colorectal Cancer Recognition from Ultrasound Images , Using Complex Textural Microstructure Cooccurrence Matrices , Based on Laws ’ Features, Int. Conference Telecomunnications Signal Processing., Prague, Czech Republic, 2015, pp. 458–462.

12 P. Valarmathi and S. Robinson, Efficacy of Feature Selection Techniques for Multilayer Perceptron Neural Network to Classify Mammogram, 6th Int. Conference on Advanced Computing., Chennai, India, 2014, pp. 26–31.

13 A. Kadir and A. Susanto, Pengolahan Citra Teori dan Aplikasi, 1st ed. Yogyakarta, Indonesia: ANDI, 2012.

14 F. Y. Shih, Image processing and pattern recognition: fundamentals and techniques. Johm Wiley & Sons, 2010.

15 D. Putra, Pengolahan Citra Digital, 1st ed. Yogyakarta: ANDI, 2010.

16 S. D. Newsam and C. Kamath, Comparing Shape and Texture Features for Pattern Recognition in Simulation Data, IS&T/SPIE's Annual Symposium on Electronic Imaging., San Jose, CA, United States, 2005, pp. 106–117.

17 R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics., 3(6) (1973) 610–621.

18 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. Wiley, 2001.

19 Suyanto, Artificial Intelligence. Bandung, Indonesia: Informatika, 2007.

20 I. S. Isa, Z. Saad, S. Omar, M. K. Osman, K. A. Ahmad, and H. A. M. Sakim, Suitable MLP network activation functions for breast cancer and thyroid disease detection, 2nd International Conference on Computational Intelligence, Modelling and Simulation, CIMSim, Bali, Indonesia, 2010, pp. 39–44.

21 M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The WEKA data mining software, SIGKDD Explor., vol. 11, no. 1, 2009, p. 10.

22 P. Refaeilzadeh, L. Tang and H. Liu, Cross-Validation, Encycl. Database Syst. (2009) 532–538.