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Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması

Year 2020, Volume: 9 Issue: 1, 297 - 305, 13.03.2020
https://doi.org/10.17798/bitlisfen.557411

Abstract

Meme kanseri teşhisinde kullanılan mamografilerdeki
anormalliklerin sınıflandırılması için makine öğrenme araştırmaları büyük önem
arz etmektedir. Bu çalışmada Curated Breast Imaging Subset of Digital Database
for Screening Mammography (CBIS-DDSM) görüntü tabanındaki kitleli ve
kalsifikasyonlu mamografi görüntüleri sınıflandırılmıştır. Veri setindeki
görüntülerden Yerel İkili Örüntü(YİÖ), Yerel Türev Örüntü, Yerel Dörtlü
Örüntü(YDÖ), ve Gürültüye Dirençli Yerel İkili Örüntü yöntemleri ile doku
öznitelikleri çıkarılmıştır. Öznitelik çıkarım yöntemlerinden yerel çarpıklık
örüntü tabanlı ayrıntılı histogram yöntemiyle de öznitelik çıkarımı
yapılmıştır. Daha sonra öznitelik vektörleri doğrusal ve radyal tabanlı
fonksiyon kernel destek vektör makineleri(DVM) ve yapay sinir ağları (YSA)
kullanılarak sınıflandırılmıştır. Eğitim ve doğrulama verisi için 5-kez çapraz
doğrulama yöntemi uygulanmıştır. En yüksek sınıflandırma performansı veren eşik
seviyeleri ve pencere boyutları her bir öznitelik çıkarım yöntemi için
belirlenmiştir. Öznitelik çıkarımı için gerekli olan süreler tablo halinde
verilmiştir. Öznitelik çıkarım yöntemi olarak farklı çap ve nokta sayısı ile
hesaplanmış YİÖ vektörleri füzyonu ve sınıflandırıcı olarak 2 gizli katmanlı
YSA kullanılması durumunda test verisi için %85.74 başarı oranı elde
edilmiştir. Elde edilen başarı oranları literatürdeki makine öğrenmesi sonuçlarına
göre yüksek ve derin öğrenme sonuçları ile kıyaslanabilir sonuçlardır. 

References

  • 1. T Florence Nightingale Hastanesi. "Türkiye'de meme kanseri sıklığını biliyor musunuz?" https://www.florence.com.tr/saglikli-yasam/Detay/turkiyede-meme-kanseri-sikligini-biliyor-musunuz (26.12.2018)2. American Chemical Society. "About Breast Cancer". https://www.cancer.org/content/dam/CRC/PDF/Public/8577.00.pdf (10.04.2018)3. Tang J, Rangayyan RM, Xu J, Naqa IE, and Yang Y. "Computer aided detection and diagnosis of breast cancer with mammography: Recent advances," IEEE Transactions on Information Technology in Biomedicine, 13(2), 236-251, 2009.4. Türk Tabipleri Birliği "Meme Hastalıklarının Tanısında Mamografi" http://www.ttb.org.tr/STED/sted1200/3.html (25.02.2019)5. The Cancer Imaging Archive. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Daniel Rubin (2016) "Curated Breast Imaging Subset of DDSM". http://dx.doi.org/10.7937/K9/TCIA.2016.7O02S9CY (01.02.2019)6. Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. "A curated mammography data set for use in computer-aided detection and diagnosis research." Scientific Data 4(170177) 2017.7. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, 26(6), 1045-1057, 2013.8. Xi P, Shu C, Goubran R. "Abnormality Detection in Mammography using Deep Convolutional Neural Networks." 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 1-6, 2018.9. Ackerman LV, Gose EE. "Breast lesion classification by computer and xeroradiograph." Cancer, 30(4), 1025-1035, 1972.10. Kutluk S, Günsel B. “Tissue density classification in mammographic images using local features.” 2013 21st Signal Processing and Communications Applications Conference (SIU). 11. Talha M. "Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features." Biomedical Research, 27(2), 322-327, 2016.12. Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L. "Deep features for breast cancer histopathological image classification." International Conference on Systems, Man, and Cybernetics, Banff, Alberta, Kanada, 5-8 Ekim 2017. 13. Qayyum A, Basit A. "Automatic breast segmentation and cancer detection via SVM in mammograms." 2016 International Conference on Emerging Technologies. Islamabad, Pakistan18-19 Ekim 2016.14. Ojala T, Pietikäinen M, Mäenpää T. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987, 2002.15. Burçin K, Vasif NV. "Down syndrome recognition using local binary patterns and statistical evaluation of the system." Expert Systems with Applications. 38(7):8690-8695, 2011.16. Kaya Y, Uyar M, Tekin R, Yıldırım S. "1D-local binary pattern based feature extraction for classification of epileptic EEG signals." Applied Mathematics and Computation. 243:209-219, 2014.17. Oppedal K, Eftestøl T, Engan K, Beyer MK, Aarsland D. "Classifying dementia using local binary patterns from different regions in magnetic resonance images." International Journal of Biomedical Imaging. 1-14. 2015.18. Lenc L, Kral P. "LBP Features for Breast Cancer Detection." 2016 IEEE International Conference on Image Processing (ICIP) Phoenix, AZ, ABD, 25-28 Eylül 2016. 19. Nahid AA, Kong Y. "Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network." Information, 9(1), 19-45, 2018.20. Esener İI, Ergin S, Yüksel T. “Göğüs Kanseri Teşhisinde Bir Öznitelik Seçim Analizi” Tıp Teknolojileri Ulusal Kongresi, 15-18 Ekim 2015, Muğla. 21. Kadiroğlu Z, Şengür A, Deniz E. “Classifıcation of Histopathological Breast Cancer Images With Low Level Texture Features” International Engineering and Natural Sciences Conference (IENSC 2018), Kasım 2018. 22. “Acr bi-rads-mammography, ultrasound and magnetic resonance imaging”, 4th ed., American College of Radiology, 2003.23. Ojala T, Pietikäinen M, Mäenpää T. "Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns." Computer Vision - ECCV 2000, Berlin. 24. Zhang B, Gao Y, Zhao S, Liu J. "Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor" IEEE Transactions on Image Processing 19(2), 533-544, 2010.25. Murala S, Maheshwari RP, Balasubramanian R. "Local tetra patterns: a new feature descriptor for content-based image retrieval" IEEE Transactions on Image Processing 21(5), 2874-2886, 2012.26. Ren J, Jiang X, Yuan J. "Noise-resistant local binary pattern with an embedded error-correction mechanism." IEEE Transactions on Image Processing. 22(10) 4049-4060, 2013.27. Tiwari AT, Kanhangad V, Pachori RB. "Histogram refinement for texture descriptor based image retrieval." Signal Processing: Image Communication 53, 73-85, 2017.28. Cristianini N, Shawe-Taylor J. "An Introduction to Support Vector Machines and other kernel based learning methods." AI Magazine, Cambridge University Press, Cambridge, 2000. 29. Schölkopf B, Smola, AJ. "Learning with kernel: Support Vector Machines, Regularization, Optimization and Beyond." The MIT Press, 2001. 30. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. "Least Squares Support Vector Machines." World Scientific 2002. 31. Marquardt, D., "An Algorithm for Least-Squares Estimation of Nonlinear Parameters," SIAM Journal on Applied Mathematics, Vol. 11, No. 2, June 1963, pp. 431–441.32. Hagan, M.T., and M. Menhaj, "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp. 989–993, 1994.33. Tsochatzidis L, Costaridou L, Pratikakis I. Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study. Journal of Imaging 2019, 5, 37.34. Alhakeem Z, Jang S. A Convolution-Free Lbp-Hog Descriptor For Mammogram Classification. arXiv preprint arXiv:1904.00187, 2019.
Year 2020, Volume: 9 Issue: 1, 297 - 305, 13.03.2020
https://doi.org/10.17798/bitlisfen.557411

Abstract

References

  • 1. T Florence Nightingale Hastanesi. "Türkiye'de meme kanseri sıklığını biliyor musunuz?" https://www.florence.com.tr/saglikli-yasam/Detay/turkiyede-meme-kanseri-sikligini-biliyor-musunuz (26.12.2018)2. American Chemical Society. "About Breast Cancer". https://www.cancer.org/content/dam/CRC/PDF/Public/8577.00.pdf (10.04.2018)3. Tang J, Rangayyan RM, Xu J, Naqa IE, and Yang Y. "Computer aided detection and diagnosis of breast cancer with mammography: Recent advances," IEEE Transactions on Information Technology in Biomedicine, 13(2), 236-251, 2009.4. Türk Tabipleri Birliği "Meme Hastalıklarının Tanısında Mamografi" http://www.ttb.org.tr/STED/sted1200/3.html (25.02.2019)5. The Cancer Imaging Archive. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi, Daniel Rubin (2016) "Curated Breast Imaging Subset of DDSM". http://dx.doi.org/10.7937/K9/TCIA.2016.7O02S9CY (01.02.2019)6. Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. "A curated mammography data set for use in computer-aided detection and diagnosis research." Scientific Data 4(170177) 2017.7. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, 26(6), 1045-1057, 2013.8. Xi P, Shu C, Goubran R. "Abnormality Detection in Mammography using Deep Convolutional Neural Networks." 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 1-6, 2018.9. Ackerman LV, Gose EE. "Breast lesion classification by computer and xeroradiograph." Cancer, 30(4), 1025-1035, 1972.10. Kutluk S, Günsel B. “Tissue density classification in mammographic images using local features.” 2013 21st Signal Processing and Communications Applications Conference (SIU). 11. Talha M. "Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features." Biomedical Research, 27(2), 322-327, 2016.12. Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L. "Deep features for breast cancer histopathological image classification." International Conference on Systems, Man, and Cybernetics, Banff, Alberta, Kanada, 5-8 Ekim 2017. 13. Qayyum A, Basit A. "Automatic breast segmentation and cancer detection via SVM in mammograms." 2016 International Conference on Emerging Technologies. Islamabad, Pakistan18-19 Ekim 2016.14. Ojala T, Pietikäinen M, Mäenpää T. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987, 2002.15. Burçin K, Vasif NV. "Down syndrome recognition using local binary patterns and statistical evaluation of the system." Expert Systems with Applications. 38(7):8690-8695, 2011.16. Kaya Y, Uyar M, Tekin R, Yıldırım S. "1D-local binary pattern based feature extraction for classification of epileptic EEG signals." Applied Mathematics and Computation. 243:209-219, 2014.17. Oppedal K, Eftestøl T, Engan K, Beyer MK, Aarsland D. "Classifying dementia using local binary patterns from different regions in magnetic resonance images." International Journal of Biomedical Imaging. 1-14. 2015.18. Lenc L, Kral P. "LBP Features for Breast Cancer Detection." 2016 IEEE International Conference on Image Processing (ICIP) Phoenix, AZ, ABD, 25-28 Eylül 2016. 19. Nahid AA, Kong Y. "Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network." Information, 9(1), 19-45, 2018.20. Esener İI, Ergin S, Yüksel T. “Göğüs Kanseri Teşhisinde Bir Öznitelik Seçim Analizi” Tıp Teknolojileri Ulusal Kongresi, 15-18 Ekim 2015, Muğla. 21. Kadiroğlu Z, Şengür A, Deniz E. “Classifıcation of Histopathological Breast Cancer Images With Low Level Texture Features” International Engineering and Natural Sciences Conference (IENSC 2018), Kasım 2018. 22. “Acr bi-rads-mammography, ultrasound and magnetic resonance imaging”, 4th ed., American College of Radiology, 2003.23. Ojala T, Pietikäinen M, Mäenpää T. "Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns." Computer Vision - ECCV 2000, Berlin. 24. Zhang B, Gao Y, Zhao S, Liu J. "Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor" IEEE Transactions on Image Processing 19(2), 533-544, 2010.25. Murala S, Maheshwari RP, Balasubramanian R. "Local tetra patterns: a new feature descriptor for content-based image retrieval" IEEE Transactions on Image Processing 21(5), 2874-2886, 2012.26. Ren J, Jiang X, Yuan J. "Noise-resistant local binary pattern with an embedded error-correction mechanism." IEEE Transactions on Image Processing. 22(10) 4049-4060, 2013.27. Tiwari AT, Kanhangad V, Pachori RB. "Histogram refinement for texture descriptor based image retrieval." Signal Processing: Image Communication 53, 73-85, 2017.28. Cristianini N, Shawe-Taylor J. "An Introduction to Support Vector Machines and other kernel based learning methods." AI Magazine, Cambridge University Press, Cambridge, 2000. 29. Schölkopf B, Smola, AJ. "Learning with kernel: Support Vector Machines, Regularization, Optimization and Beyond." The MIT Press, 2001. 30. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. "Least Squares Support Vector Machines." World Scientific 2002. 31. Marquardt, D., "An Algorithm for Least-Squares Estimation of Nonlinear Parameters," SIAM Journal on Applied Mathematics, Vol. 11, No. 2, June 1963, pp. 431–441.32. Hagan, M.T., and M. Menhaj, "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp. 989–993, 1994.33. Tsochatzidis L, Costaridou L, Pratikakis I. Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study. Journal of Imaging 2019, 5, 37.34. Alhakeem Z, Jang S. A Convolution-Free Lbp-Hog Descriptor For Mammogram Classification. arXiv preprint arXiv:1904.00187, 2019.
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Details

Primary Language Turkish
Journal Section Araştırma Makalesi
Authors

Volkan Müjdat Tiryaki 0000-0003-1824-5260

Publication Date March 13, 2020
Submission Date April 24, 2019
Acceptance Date July 11, 2019
Published in Issue Year 2020 Volume: 9 Issue: 1

Cite

IEEE V. M. Tiryaki, “Mamografi görüntülerindeki anormalliklerin yerel ikili örüntü ve varyantları kullanılarak sınıflandırılması”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 297–305, 2020, doi: 10.17798/bitlisfen.557411.

Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS