Research Article
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Year 2021, Volume: 5 Issue: 2, 281 - 291, 15.08.2021

Ali Çınar Bengisu Topuz Semih Ergin

https://doi.org/10.35860/iarej.857579
Cited By: 2

Abstract

References

  • 1. Stewart, B., C.P. Wild, World Cancer Report 2014. 2015, WHO Press.
  • 2. Garfinkel, L., G. Murphy, W.J. Lawrence, R.J. Lenhard, American Cancer Society Textbook of Clinical Oncology. 1995, The Society Press.
  • 3. Abbas, W., K.B. Khan, M. Aqeel, M.A. Azam, M.H. Ghouri, F.H. Jaskani, Lungs Nodule Cancer Detection Using Statistical Techniques. IEEE 23rd International Multitopic Conference, 2020, Pakistan. p. 1-6.
  • 4. Parveen, S., K.B. Khan, Detection and classification of pneumonia in chest X-ray images by supervised learning. IEEE 23rd International Multitopic Conference, 2020, Pakistan, p. 1-5.
  • 5. Gonzalez, E.R., V. Ponomaryov, Automatic Lung nodule segmentation and classification in CT images based on SVM. 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, 2016, Ukraine. p. 1-4.
  • 6. Tun, K.M.M, A.S. Khaing, Feature Extraction and Classification of Lung Cancer Nodule using Image Processing Techniques. International Journal of Engineering Research & Technology, 2014. 3(3): p. 2204-2210.
  • 7. Shiraishi, J., S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi , K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi, Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR, 2000. 174(1): p. 71-74.
  • 8. Khan, K.B., A.A. Khaliq, M. Shadid, J.A. Shah, A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise. Tehnički vjesnik, 2016. 23 (6): p. 1755-62.
  • 9. Gallagher, N., G. Wise, A theoretical analysis of the properties of median filters. IEEE Transaction on Acoustic Speech Signal Processing, 1981. 29 (6): p. 1135–1141.
  • 10. Wang, C., A. Elazab, J. Wu, Q. Hua, Lung nodule classification using deep feature fusion in chest radiography. Computerized Medical Imaging and Graphics, 2017. 57: p. 10-18.
  • 11. Gabor, D., Theory of communication. Journal of the Institution of Electrical Engineers- Part III: Radio and Communication Engineering, 1946. 93(26): p. 429–457.
  • 12. Soleymanpour, E., H.R. Pourreza, E. Ansariour, M. Sadooghi, Fully Automatic Lung Segmentation and Rib Suppression Methods to Improve Nodule Detection in Chest Radiographs. Journal of Medical Signals and Sensors, 2011. 1(3): p. 191-199.
  • 13. Haddad, R.A., A.N. Akansu, A, Class of Fast Gaussian Binomial Filters for Speech and Image Processing, IEEE Transactions on Acoustics, Speech and Signal Processing, 1991. 39(3): p. 723-727.
  • 14. Chan, T.F., L.A. Vese, Active contours without edges. IEEE Transactions on Image Processing, 2001. 10(2): p. 266‐277.
  • 15. Esener, I.I., S. Ergin, T. Yuksel, A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis. Journal of Healthcare Engineering, 2017. 2017: p. 1-15.
  • 16. Ahonen, T., A. Hadid, M. Pietkäinen, Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. 28(12): p. 2037-2041.
  • 17. Song, L., X. Liu, L. Ma, C. Zhou, X. Zhao, Y. Zhao, Using HOG-LBP features and MMP learning to recognize imaging signs of lung lesions. 25.International Symposium On Computer-Based Medical Systems, 2012, Italy. p. 1-4.
  • 18. Wang, L., D.C. He,Texture Classification Using Texture Spectrum, Pattern Recognition, 1990. 23(8): p. 905-910.
  • 19. Esener, I.I., The Identification of Suspicious Regions on Mammography Images for Breast Cancer and the Classification of Breast Cancer Type. PhD Thesis, Eskisehir Osmangazi University, 2017.
  • 20. Haralick, R.M., K. Shanmugam, I. Dinstein, Textural features of image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973. SMC-3(6): p. 610-621.
  • 21. Soh. L., C. Tsatsoulis, Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 1999. 37(2): p. 780-795.
  • 22. Clausi, D.A., An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remore Sensing, 2002. 28(1): p. 45-62.
  • 23. Esener. I.I, S. Ergin, T. Yuksel, A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms. International Journal of Intelligent Systems and Applications in Engineering, 2016. 4(Special Issue): p. 124-129.
  • 24. Ergin. S., O. Kilinc, Using DSIFT and LCP features for detecting breast lesions. ISCSE, 2013. International Symposium on Computing in Science & Engineering. Proceedings: Izmir. p. 216-220.
  • 25. Kim. J., B.S. Kim, S. Savarese, Comparing image classification methods:K-nearest-neighbor and support-vector-machines. 6. WSEAS International Conference on Computer Engineering and Applications, 2012, World Scientific and Engineering Academy and Society: USA. p. 133-138.
  • 26. Akar. O, O. Gungor, Classification of multispectral images using Random Forest Algorithm. Journal of Geodesy and Geoinformation, 2012. 1(2): p. 105-112.
  • 27. Safavian, S.R., D. Landgrebe, A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991. 21(3): p. 660-674.
  • 28. Rish, I., An empirical study of the naive Bayes classifier. IJCAI Workshop on Empirical Methods in artificial intelligence, 2001, IBM New York: USA. p. 41-46.
  • 29. Webb, A.R., Linear discriminant analysis in Statistical Pattern Recognition. 2002, John Wiley & Sons.
  • 30. Ozkan, K., S. Ergin, S. Isik, I. Isikli, A new classification scheme of plastic wastes based upon recycling labels, Waste Management, 2015. 35: p. 29-35.
  • 31. Fisher, R.A., The use of multiple measurements in taxonomic problems. Annals of Eugenics, 1936. 7(2): p. 179-188.

A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification

Year 2021, Volume: 5 Issue: 2, 281 - 291, 15.08.2021

Ali Çınar Bengisu Topuz Semih Ergin

https://doi.org/10.35860/iarej.857579
Cited By: 2

Abstract

Suspicious regions in chest x-rays are detected automatically, and these regions are classified into three types, including “malignant nodule”, “benign nodule”, and “no nodule” in this study. Firstly, the areas except the lung tissues are removed in each chest x-ray using the thresholding method. Then, Poisson noise was removed from the images by applying the gradient filter. Ribs may overlap onto nodules. Since this circumstance will make the detection of a nodule difficult, it is necessary to distinguish and suppress the ribs. The location of the rib bones is determined by a template matching method, and then the corresponding bones are suppressed by applying the Gabor filter. After this stage, suspicious tissues in the chest x-rays are specified using the Chan-Vese active contour without edges. Then, some features are extracted from these suspicious regions. Six different features are extracted: Statistical, Histogram of Oriented Gradients (HOG)-based, Local Binary Pattern (LBP)-based, Geometrical, Gray Level Co-Occurrence Matrix (GLCM) Texture-based and Dense Scale Invariant Feature Transform (DSIFT)-based. Then, the classification stage is achieved using these features. The best classification result is obtained using statistical, LBP-based, and HOG-Based features. The classification results are evaluated with sensitivity, accuracy, and specificity analyses. K-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Logistic Linear Classifier (LLC), Support Vector Machines (SVM), Fisher’s Linear Discriminant Analysis (FLDA), and Naive Bayes (NB) methods are used for the classification purpose separately. The random forest classifier gives the best results with 57% sensitivity, 66% accuracy, 81% specificity values.

Keywords

Chest x-ray classification Nodule classification Nodule detection Rib detection Rib suppression ROI detection

References

  • 1. Stewart, B., C.P. Wild, World Cancer Report 2014. 2015, WHO Press.
  • 2. Garfinkel, L., G. Murphy, W.J. Lawrence, R.J. Lenhard, American Cancer Society Textbook of Clinical Oncology. 1995, The Society Press.
  • 3. Abbas, W., K.B. Khan, M. Aqeel, M.A. Azam, M.H. Ghouri, F.H. Jaskani, Lungs Nodule Cancer Detection Using Statistical Techniques. IEEE 23rd International Multitopic Conference, 2020, Pakistan. p. 1-6.
  • 4. Parveen, S., K.B. Khan, Detection and classification of pneumonia in chest X-ray images by supervised learning. IEEE 23rd International Multitopic Conference, 2020, Pakistan, p. 1-5.
  • 5. Gonzalez, E.R., V. Ponomaryov, Automatic Lung nodule segmentation and classification in CT images based on SVM. 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, 2016, Ukraine. p. 1-4.
  • 6. Tun, K.M.M, A.S. Khaing, Feature Extraction and Classification of Lung Cancer Nodule using Image Processing Techniques. International Journal of Engineering Research & Technology, 2014. 3(3): p. 2204-2210.
  • 7. Shiraishi, J., S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi , K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, K. Doi, Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. AJR, 2000. 174(1): p. 71-74.
  • 8. Khan, K.B., A.A. Khaliq, M. Shadid, J.A. Shah, A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise. Tehnički vjesnik, 2016. 23 (6): p. 1755-62.
  • 9. Gallagher, N., G. Wise, A theoretical analysis of the properties of median filters. IEEE Transaction on Acoustic Speech Signal Processing, 1981. 29 (6): p. 1135–1141.
  • 10. Wang, C., A. Elazab, J. Wu, Q. Hua, Lung nodule classification using deep feature fusion in chest radiography. Computerized Medical Imaging and Graphics, 2017. 57: p. 10-18.
  • 11. Gabor, D., Theory of communication. Journal of the Institution of Electrical Engineers- Part III: Radio and Communication Engineering, 1946. 93(26): p. 429–457.
  • 12. Soleymanpour, E., H.R. Pourreza, E. Ansariour, M. Sadooghi, Fully Automatic Lung Segmentation and Rib Suppression Methods to Improve Nodule Detection in Chest Radiographs. Journal of Medical Signals and Sensors, 2011. 1(3): p. 191-199.
  • 13. Haddad, R.A., A.N. Akansu, A, Class of Fast Gaussian Binomial Filters for Speech and Image Processing, IEEE Transactions on Acoustics, Speech and Signal Processing, 1991. 39(3): p. 723-727.
  • 14. Chan, T.F., L.A. Vese, Active contours without edges. IEEE Transactions on Image Processing, 2001. 10(2): p. 266‐277.
  • 15. Esener, I.I., S. Ergin, T. Yuksel, A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis. Journal of Healthcare Engineering, 2017. 2017: p. 1-15.
  • 16. Ahonen, T., A. Hadid, M. Pietkäinen, Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. 28(12): p. 2037-2041.
  • 17. Song, L., X. Liu, L. Ma, C. Zhou, X. Zhao, Y. Zhao, Using HOG-LBP features and MMP learning to recognize imaging signs of lung lesions. 25.International Symposium On Computer-Based Medical Systems, 2012, Italy. p. 1-4.
  • 18. Wang, L., D.C. He,Texture Classification Using Texture Spectrum, Pattern Recognition, 1990. 23(8): p. 905-910.
  • 19. Esener, I.I., The Identification of Suspicious Regions on Mammography Images for Breast Cancer and the Classification of Breast Cancer Type. PhD Thesis, Eskisehir Osmangazi University, 2017.
  • 20. Haralick, R.M., K. Shanmugam, I. Dinstein, Textural features of image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973. SMC-3(6): p. 610-621.
  • 21. Soh. L., C. Tsatsoulis, Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 1999. 37(2): p. 780-795.
  • 22. Clausi, D.A., An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remore Sensing, 2002. 28(1): p. 45-62.
  • 23. Esener. I.I, S. Ergin, T. Yuksel, A Genuine GLCM-based Feature Extraction for Breast Tissue Classification on Mammograms. International Journal of Intelligent Systems and Applications in Engineering, 2016. 4(Special Issue): p. 124-129.
  • 24. Ergin. S., O. Kilinc, Using DSIFT and LCP features for detecting breast lesions. ISCSE, 2013. International Symposium on Computing in Science & Engineering. Proceedings: Izmir. p. 216-220.
  • 25. Kim. J., B.S. Kim, S. Savarese, Comparing image classification methods:K-nearest-neighbor and support-vector-machines. 6. WSEAS International Conference on Computer Engineering and Applications, 2012, World Scientific and Engineering Academy and Society: USA. p. 133-138.
  • 26. Akar. O, O. Gungor, Classification of multispectral images using Random Forest Algorithm. Journal of Geodesy and Geoinformation, 2012. 1(2): p. 105-112.
  • 27. Safavian, S.R., D. Landgrebe, A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics, 1991. 21(3): p. 660-674.
  • 28. Rish, I., An empirical study of the naive Bayes classifier. IJCAI Workshop on Empirical Methods in artificial intelligence, 2001, IBM New York: USA. p. 41-46.
  • 29. Webb, A.R., Linear discriminant analysis in Statistical Pattern Recognition. 2002, John Wiley & Sons.
  • 30. Ozkan, K., S. Ergin, S. Isik, I. Isikli, A new classification scheme of plastic wastes based upon recycling labels, Waste Management, 2015. 35: p. 29-35.
  • 31. Fisher, R.A., The use of multiple measurements in taxonomic problems. Annals of Eugenics, 1936. 7(2): p. 179-188.
There are 31 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Research Articles
Authors

Ali Çınar 0000-0002-3947-7848

Bengisu Topuz This is me 0000-0001-6698-5154

Semih Ergin 0000-0002-7470-8488

Publication Date August 15, 2021
Submission Date January 12, 2021
Acceptance Date June 24, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Çınar, A., Topuz, B., & Ergin, S. (2021). A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification. International Advanced Researches and Engineering Journal, 5(2), 281-291. https://doi.org/10.35860/iarej.857579
AMA Çınar A, Topuz B, Ergin S. A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification. Int. Adv. Res. Eng. J. August 2021;5(2):281-291. doi:10.35860/iarej.857579
Chicago Çınar, Ali, Bengisu Topuz, and Semih Ergin. “A New Region-of-Interest (ROI) Detection Method Using the Chan-Vese Algorithm for Lung Nodule Classification”. International Advanced Researches and Engineering Journal 5, no. 2 (August 2021): 281-91. https://doi.org/10.35860/iarej.857579.
EndNote Çınar A, Topuz B, Ergin S (August 1, 2021) A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification. International Advanced Researches and Engineering Journal 5 2 281–291.
IEEE A. Çınar, B. Topuz, and S. Ergin, “A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification”, Int. Adv. Res. Eng. J., vol. 5, no. 2, pp. 281–291, 2021, doi: 10.35860/iarej.857579.
ISNAD Çınar, Ali et al. “A New Region-of-Interest (ROI) Detection Method Using the Chan-Vese Algorithm for Lung Nodule Classification”. International Advanced Researches and Engineering Journal 5/2 (August 2021), 281-291. https://doi.org/10.35860/iarej.857579.
JAMA Çınar A, Topuz B, Ergin S. A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification. Int. Adv. Res. Eng. J. 2021;5:281–291.
MLA Çınar, Ali et al. “A New Region-of-Interest (ROI) Detection Method Using the Chan-Vese Algorithm for Lung Nodule Classification”. International Advanced Researches and Engineering Journal, vol. 5, no. 2, 2021, pp. 281-9, doi:10.35860/iarej.857579.
Vancouver Çınar A, Topuz B, Ergin S. A new region-of-interest (ROI) detection method using the chan-vese algorithm for lung nodule classification. Int. Adv. Res. Eng. J. 2021;5(2):281-9.

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