Research Article

Assessment of Similarity Rates of Liver Images Using Geometric Transformations

Volume: 17 Number: 1 March 27, 2017
EN

Assessment of Similarity Rates of Liver Images Using Geometric Transformations

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

References

  1. [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. [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. [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. [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. [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. [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. [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. [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.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Tuğba Palabaş Tapkın
YILDIZ TEKNIK UNIV
Türkiye

Tuncer Ergin This is me

Uygar Teomete This is me

Nizamettin Aydın This is me

Publication Date

March 27, 2017

Submission Date

January 31, 2017

Acceptance Date

-

Published in Issue

Year 2017 Volume: 17 Number: 1

APA
Palabaş Tapkın, T., Osman, O., Ergin, T., Teomete, U., Dandin, Ö., & Aydın, N. (2017). Assessment of Similarity Rates of Liver Images Using Geometric Transformations. IU-Journal of Electrical & Electronics Engineering, 17(1), 3113-3119. https://izlik.org/JA94DE39KL
AMA
1.Palabaş Tapkın T, Osman O, Ergin T, Teomete U, Dandin Ö, Aydın N. Assessment of Similarity Rates of Liver Images Using Geometric Transformations. IU-Journal of Electrical & Electronics Engineering. 2017;17(1):3113-3119. https://izlik.org/JA94DE39KL
Chicago
Palabaş Tapkın, Tuğba, Onur Osman, Tuncer Ergin, Uygar Teomete, Özgür Dandin, and Nizamettin Aydın. 2017. “Assessment of Similarity Rates of Liver Images Using Geometric Transformations”. IU-Journal of Electrical & Electronics Engineering 17 (1): 3113-19. https://izlik.org/JA94DE39KL.
EndNote
Palabaş Tapkın T, Osman O, Ergin T, Teomete U, Dandin Ö, Aydın N (March 1, 2017) Assessment of Similarity Rates of Liver Images Using Geometric Transformations. IU-Journal of Electrical & Electronics Engineering 17 1 3113–3119.
IEEE
[1]T. Palabaş Tapkın, O. Osman, T. Ergin, U. Teomete, Ö. Dandin, and N. Aydın, “Assessment of Similarity Rates of Liver Images Using Geometric Transformations”, IU-Journal of Electrical & Electronics Engineering, vol. 17, no. 1, pp. 3113–3119, Mar. 2017, [Online]. Available: https://izlik.org/JA94DE39KL
ISNAD
Palabaş Tapkın, Tuğba - Osman, Onur - Ergin, Tuncer - Teomete, Uygar - Dandin, Özgür - Aydın, Nizamettin. “Assessment of Similarity Rates of Liver Images Using Geometric Transformations”. IU-Journal of Electrical & Electronics Engineering 17/1 (March 1, 2017): 3113-3119. https://izlik.org/JA94DE39KL.
JAMA
1.Palabaş Tapkın T, Osman O, Ergin T, Teomete U, Dandin Ö, Aydın N. Assessment of Similarity Rates of Liver Images Using Geometric Transformations. IU-Journal of Electrical & Electronics Engineering. 2017;17:3113–3119.
MLA
Palabaş Tapkın, Tuğba, et al. “Assessment of Similarity Rates of Liver Images Using Geometric Transformations”. IU-Journal of Electrical & Electronics Engineering, vol. 17, no. 1, Mar. 2017, pp. 3113-9, https://izlik.org/JA94DE39KL.
Vancouver
1.Tuğba Palabaş Tapkın, Onur Osman, Tuncer Ergin, Uygar Teomete, Özgür Dandin, Nizamettin Aydın. Assessment of Similarity Rates of Liver Images Using Geometric Transformations. IU-Journal of Electrical & Electronics Engineering [Internet]. 2017 Mar. 1;17(1):3113-9. Available from: https://izlik.org/JA94DE39KL