Fetal kafatası imgelerinin yarı-otomatik buluşsal bölütlenmesi

Yıl 2023, Cilt: 38 Sayı: 2, 679 - 692, 07.10.2022

Umut Konur

https://doi.org/10.17341/gazimmfd.910514

Öz

Amaçları önceden tanımlanmış görevlerle ortaya konulan ve genellikle otomatik uygulamalar bağlamında anlamlı olan imge bölütleme problemi, ilgilenilen belli piksellerin çevrelerinden yalıtılmasını ele alır. İmgelerde çok ve ham durumda bulunarak artıklık, yararsızlık ve hatta görev-zorlaştırma barındıran verinin basitleştirilip, yalnız ilginç bölümleri içeren derlitoplu gösterimlerinin elde edilmesi ve bu bölümlerden tanımlayıcı özniteliklerin çıkarılması gerekir. Bu çalışmada, özel bir alanla ilgili olan ve ultrason görüntüleme ile edinilen enine-kesit fetal kafataslarını gösteren monokrom imgelerdeki kafatası çevritlerinin bulunması için kullanılan buluşsal bir yaklaşımdan söz edilmektedir. Bölütleme sürecinin başında, kullanıcının girdi imgedeki kafatası çevriti üstünde az sayıda noktayı elle işaretlemesi beklenmektedir. Çevritlerin parlak yoğunluklu piksellerden oluştuğu olgusundan ve görüntüleme teknolojisinden kaynaklanan bölütler arasında kopukluklar gözlenmesinden hareketle, açıklanan buluşsal bölütleme yöntemi, ortalama şekil modeli ve yoğunluğa-dayalı ortalama konum bulma kavramlarından yararlanmaktadır. Örnek imgelerdeki sonuçlar, hem görsel olarak hem de otomatik tanı sistemlerinde girdi olarak kullanıldığında, doyurucudur.

Anahtar Kelimeler

İmge bölütleme, ortalama şekil modeli, yoğunluğa-dayalı ortalama konum bulma, ultrason, enine-kesit

Semi-automatic heuristic segmentation of fetal skull images

Öz

The image segmentation problem, whose purposes are set by predefined tasks and which is generally significant in the context of automatic applications, deals with isolating image pixels that are of particular interest. Data; that are either redundant, useless or even task-complicating by being too much and raw, must be simplified, compact representations including only interesting portions must be obtained and descriptively useful features out of those must be extracted. In this work, a specific domain is considered and a heuristic approach for detecting skull contours in monochrome images acquired via the ultrasound technology displaying transcerebellar fetal skulls is described. At the start of the segmentation process, the user is expected to mark few points manually along the skull boundary on the input image. Knowing that skull contours are composed of pixels of high intensity and that discontinuities are observed between segments arising from the imaging technology, the described heuristic segmentation method utilizes the concepts of average shape model and intensity-based computation of average positions. The results for sample images, both visually and when they are used as inputs to automated diagnosis systems, are satisfactory.

Anahtar Kelimeler

Image segmentation, average shape model, intensity-based computation of average positions, ultrasound, transcerebellar

Kaynakça

• 1. Gonzalez R.C. ve Woods R.E., Digital Image Processing, Pearson Prentice Hall, New Jersey, 2008.
• 2. Alpaydın E., Introduction to Machine Learning, MIT Press, Cambridge, 2014.
• 3. Forsyth D. ve Ponce J., Computer Vision: A Modern Approach, Pearson, New Jersey, 2012.
• 4. Cootes T.F., Edwards G.J., Taylor, C., Active appearance models, Lecture Notes in Computer Science, Cilt 1407, Editör: Burkhardt H. ve Neumann B., Springer, Berlin, Heidelberg. 1998.
• 5. Stegmann M.B., Active appearance models: Theory, extensions and cases, Master Tezi, Technical University of Denmark, 2000.
• 6. Haralick R.M., Shanmugam K., Dinstein I., Textural features for image classification, IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610-621, 1973.
• 7. Humeau-Heurtier, A.. Texture feature extraction methods: a survey, IEEE Access, 7, 8975-9000, 2019.
• 8. Konur U., Gürgen F.S., Varol F., Akarun L., Computer aided detection of spina bifida using nearest neighbor classification with curvature scale space features of fetal skulls extracted from ultrasound images, Knowledge-Based Systems, 85, 80-95, 2015.
• 9. Konur U., Computerized detection of spina bifida using SVM with Zernike moments of fetal skulls in ultrasound screening, Biomedical Signal Processing and Control, 43, 18-30, 2018.
• 10. Watt A.H., Advanced animation and rendering techniques: Theory and Practice, ACM Press, 1992.
• 11. Otsu N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1), 62-66.1979.
• 12. Konur U., Computer aided detection of spina bifida using features derived from curvature scale space and Zernike moments, Doktora Tezi, Boğaziçi Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, 2015.
• 13. Mokhtarian F. ve Mackworth A.K., A theory of multiscale curvature-based shape representation for planar curves, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(18), 789-805, 1992.
• 14. Abbasi S., Mokhtarian F., Kittler J., Curvature scale space image in shape similarity retrieval, Multimedia Systems, 7(6), 467-476, 1999.
• 15. Teauge M.R., Image analysis via the general theory of moments, Journal of Optical Society of America, 70(8), 920-930, 1980.
• 16. Teh C.H. ve Chin R.T., On image analysis by the method of moments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4), 496-513, 1988.
• 17. Boser B., Guyon I., Vapnik V.N., A training algorithm for optimal margin classifiers, Annual Workshop on Computational Learning Theory, 1992.
• 18. Cortes C. ve Vapnik V., Support vector networks, Machine Learning, 20(3), 373-397, 1995.
• 19. Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16(1), 321-357, 2002.
• 20. Joshi M.V., On evaluating performance of classifiers for rare classes, Proceedings of the IEEE International Conference on Data Mining, 2002.
• 21. Kpalma K., Yang M., Ronsin J., Planar shapes descriptors based on the turning angle scalogram, Proceedings of the International Conference on Image Analysis and Recognition, 2008.
• 22. Kopf S., Haenselmann T., Effelsberg W., Enhancing curvature scale space features for robust shape classification, Proceedings of the International Conference on Multimedia and Expo, 2005.
• 23. Fawcett T., An introduction to ROC analysis, Pattern Recognition Letters – Special Issue: ROC Analysis in Pattern Recognition, 27(8), 861-874, 2006.
• 24. Chang C.C. ve Lin C.J., LIBSVM: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2(3), 1-27, 2011.
• 25. Han H., Wang W.Y., Mao B.H., Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning, Proceedings of the International Conference on Advances in Intelligent Computing, 2005.
• 26. Yanase J. ve Triantaphyllou E., A systematic survey of computer-aided diagnosis in medicine: past and present developments, Expert Systems with Applications, 138, 2019.
• 27. Mallat S.G. ve Zhang Z.., Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, 41(12), 3397-3415, 1993.
• 28. Bergeaud F. ve Mallat S.G., Matching pursuit of images, Proceedings of the International Conference on Image Processing, 53-56, 1995.
• 29. Mendels F., Vandergheynst P., Thiran, J.P., Matching pursuit-based shape representation and recognition using scale-space, Wiley Periodicals, 16(5), 162-180. 2007.