Assessment of Similarity Rates of Liver Images Using Geometric Transformations

Year 2017, Volume: 17 Issue: 1, 3113 - 3119, 27.03.2017

Tuğba Palabaş Tapkın , Onur Osman , Tuncer Ergin Uygar Teomete Özgür Dandin , Nizamettin Aydın

Abstract

In this study, similarity rates of the liver images
which are obtained from different peoples are determined using 3D geometric
transformation methods.  The similarity
is evaluated based on the numerical comparisons and visual results. 10 intact
liver images which are drawn by the radiologists are used. Three geometric
transformation methods scaling, rotating, and translating are consecutively
applied to the liver images. All images are used both as atlas and as test
images. The Dice coefficient values are calculated to show the similarity of
each test image to atlas. The scaling, rotating, and translating amounts of the
image are retained for the atlas which the similarity rate is highest. The
liver images of different persons are similar to each other at an average rate
of 67



















0.09 % according to Dice coefficient values which
express the similarity. This study is presented as a step to prepare atlas
database for segmentation of the injured liver.

Keywords

Scaling, rotation, translating, liver segmentation

References

• [1] Linguraru, M.G., Sandberg J.K., Li Z., Shah F., Summers R.M., “Automated Segmentation and Quantification of Liver and Spleen from CT Images using Normalized Probabilistic Atlases and Enhancement Estimation, Medical Pyysics, 37(2):771-783, 2010..
• [2] Campadelli P., Casiraghi E., Pratissoli S., A Segmentation Framework for Abdominal Organs from CT Scans, Artificial Intelligence in Medicine, 50:3–11, 2010.9.
• [3] Li C., Wang X., Li J., Eberl S., Fulham M., Yin Y., Feng D.D., Joint Probabilistic Model of Shape and Intensity for Multiple Abdominal Organ Segmentation from Volumetric CT Images, IEEE Journal of Bıomedical and Health Informatics, 17( 1): 92-102, 2013.
• [4] Chen X., Udupa J.K., Bagci U, Zhuge Y., Yao J., Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models, IEEE Transactions on Image Processing, 21(4): 2035-2046, 2012.
• [5] Wolz R., Chu C., Misawa K., Fujiwara M., Mori K., Rueckert D., Automated Abdominal Multi-Organ Segmentation with Subject-Specific Atlas Generation, IEEE Transactions on Medical Imaging, 32(9): 1723-1730, 2013.
• [6] Linguraru M.G., Pura J.A, Chowdhury A.S., Summers R.M., Multi-Organ Segmentation from Multi-Phase Abdominal CT via 4D Graphs using Enhancement, Shape and Location Optimization, Medical Image Computing and Computer-Assisted Intervention – MICCAI , 13(Pt 3): 89–96, 2010.
• [7] Palabaş T., Osman, O., Ergin T, Teomete U., Dandin Ö., Automated Segmentation of the Injured Liver, Medical Technologies National Conference (TIPTEKNO),2015, DOI: 10.1109/TIPTEKNO.2015.7374590.
• [8] Dandin, Ö., Teomete, U., Osman, O., Tulum, G., Ergin, T., Sabuncuoğlu, M.Z., “Automated Segmentation of the Injured Spleen”, International Journal of Computer Assisted Radiology and Surgery, 2015, DOI:10.1007/s11548-015-1288-9.
• [9] Selver, M. A., Kocaoğlu, A., Doğan, H., Demir, G. K., Dicle, O. ve Güzeliş, C., “Nakil Öcesi Verici Değerlendirmeleri için Otomatik Karaciğer Bölütleme Yöntemi”, Hastane ve Yaşam, Ocak 2008, 80-87.
• [10] Çınar, A. ve Durkaya, A. K., “Karaciğer Görüntüsü Ayıklamak için Genişleme Prensipli Yaklaşım”, 2010 National Conference on Electrical Electronics and Computer Engineering-ELECO 2010, December 2010, 588-591.