Karar ağaçları ve fraktal analiz kullanarak histopatolojik imgelerin sınıflandırılması

Bu çalışmada, histopatolojik imgelerden çeşitli hastalıkların tanınması işleminde, hekime yardımcı olacak ve kolaylık sağlayacak bir karar destek sistemi tasarlanmıştır. Geliştirilen karar destek sistemi, örüntü tanıma temellidir. Örüntü tanıma sürecinin, özellik çıkarım aşaması Fraktal analiz ve sınıflandırma aşaması içinse karar ağaçları kullanılmıştır. Hepatitli hastaların histopatolojik imgeleri ile geliştirilen sistemin başarısı değerlendirilmiştir. 50 hasta verisi üzerinde geliştirilen sistemin doğruluk yüzdesi %92 olarak bulunmuştur.

Histopathological images classification by using decision trees and fractal analysis

In this study, a decision support system is designed which helps the physicians and facilitates their works for detection of various diseases from histopathological images. The developed decision support system is based on pattern recognition. The feature extraction stage of the pattern recognition system is realized by using fractal analysis and a decision tree classifier is used for classification stage. The performance of the proposed pattern recognition system is evaluated with the histopathological images which are taken from hepatic patients. The correct classification rate is 92 % for 50 patient’s data.

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1. Pitas I., Digital image processing algorithms and applications, John Wiley & Sons Inc., 2000.
2. Gonzalez R.C., Woods R.E., Digital image processing, Prentice Hall, 2002.
3. Giansanti R., Fumelli P., Passerini G.,et al., “Imaging system for retinal change evaluation”, IPA97 Conference, Pub.No. 443, 1997.
4. Kara S., Şener F., Okandan M., et al., “Patolojik doku örneklerinin bilgisayar tabanlı analizi”, 246- 249, SIU, 2003.
5. Petersen M.E., Arts T., “Recognition of radiopaque markers in X-ray images using a neural network as nonlinear fitler”, Pattern Recognition Letters, 20(5), 521-533, May, 1999.
6. Esgiar A.N., Sharif B.S., Naguib R.N.G., et al., “Texture descriptions and classification for pathological analysis of cancerous colonic mucosa”, 7th International Conference on Image Processing And Its Applications, 1(13-15), 335-338, 1999.
7. Bauer W., Mackezie C.D., “Cancer detection via determination of fraktal cell dimension”, Pattern Formation and Solitons, 9506003, 07/1995.
8. Esgiar A.N., Naguib R.N.G., Sharif S., et al., “Fraktal Analysis in the Detection of Colonic Cancer Images”, IEEE Transactıons on Informatıon Technology in Bıomedıcıne, 6(1), March 2002.
9. Tzanakou E.M., Supervised and unsupervised pattern recognition, CRC Press LLC, 2000.
10. Rangayyan R. M., Nguyen T. M., “Pattern classification of breast masses via fraktal analysis of their contours”, International Congress Series, 1281, 1041-1046, 2005.
11. Chen D., Chang R., Chen C., et al., “Classification of breast ultrason images using fraktal feature”, Journal of Clinical Imaging, 29, 235-245, 2005.
12. Hotta N., Otsuka K., Murakami S., et al., “Fraktal analysis of heart rate variability and mortality in elderly community-dwelling people – Logitudinal Investigation fort he Longevity and Aging in Hokkaido County (LILAC) study”, Biomedicine & Pharmacothcrapy, 59, 45-48, 2005.
13. Masugi M., “Multi-fraktal analysis of IP-network traffic based on a hierarchical clustering approach”, Communications in Nonlinear Science and Numerical Simulation, 12(7), 1316-1325, 2006.
14. Huang K., Murphy R.F., “Automated classification of subcellular patterns in multicell images without segmentation inti single cells”, IEEE Transactions on Medical Imaging, 12(3), 478 – 485, 1993.
15. Türkoğlu İ., Arslan A., İlkay E., “Kalp kapak hastalıklarının teşhisi için bir karar destek sistemi”, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü Dergisi, 6(2) , 57-64 , Temmuz , 2002.
16. Dell’Acqua F., Gamba P., Trianni G., “Semiautomatic choice of scale-dependent features for satellite SAR image classification”, Pattern Recognition Letters, 27, 244-251, 2006.
17. Gamanya R., De Maeyer P., De Dapper M., “An automated satellite image classification desing using object-oriented segmentation algorithms:A move towards standardization”, Expert Systems with Applicatons, 32(2), 616-624, 2007.
18. Mitchell T.M., Machine learning, MIT press and the McGraw-Hill Companies Inc., Singapore, 1997.
19. Türkoğlu İ., Arslan A., ve İlkay E., “A pattern recognition system for diagnose of the heart mitral valve diseases based on wavelet packet and neural network”, Fırat Üniversitesi Fen ve Mühendislik Bilimleri Dergisi,14(1),1-10, 2002.
20. Yenerman M., Genel Patoloji, Nobel Tıp Kitabevleri, İstanbul, 1994.
21. Herbert J.F., Cameron J.L., Matthew W.D., “Is There Meaning in Fractal Analysis?”, Complexity International, 6, 1999.
22. Foroutan-pour K., Dutilleul P., Smith D.L., “Advances in the implementation of the boxcounting method of fraktal dimension estimation”, Applied mathematics and computation, 105, 195-210, 1999.
23. Gürsakal N., Oğuzlar A., Şentürk A., “Döviz Kuru Grafiklerinin Fraktal Boyutlarının Belirlenmesi”, III. Ulusal Ekonometri ve İstatistik Sempozyumu Bildirileri, Bursa, 1997
24. Sugumaran V., Muralidharan V., Ramachandran K.I., “Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing”, Mechanical Systems and Signal Processing, 21(2), 930-942, 2007.
25. Karmeshu, Entropy Measures, Maximum Entropy Principle and Emerging Applications, SpringerVerlag, Berlin Heidelberg, NewYork, 2003.