Journals / Mathematical and Computational Applications / 2013 / Cilt: 18 - Sayı: 3

PERFORMANCE ANALYSIS OF DISTANCE TRANSFORM BASED INTER- SLICE SIMILARITY INFORMATION ON SEGMENTATION OF MEDICAL IMAGE SERIES

Pages
511–520
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The simulations performed on several medical image series and for four different abdominal organs show the importance of parameter analysis for distance transformation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Segmentation of organs from CT and MR image series is a challenging research area in all fields of medical imaging.
📊
Key Findings & Data:Since there is no study discussing the effect of ? on segmentation performance, the aim of this study is to analyze how changes on ? affects performance in terms of accuracy, computation and time requirements.
💡
Conclusion & Impact:The simulations performed on several medical image series and for four different abdominal organs show the importance of parameter analysis for distance transformation.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Segmentation of organs from CT and MR image series is a challenging research area in all fields of medical imaging. Although, organs of interest are threedimensional in nature, slice-by-slice approaches are widely used in clinical applications because of their ease of integration with the current manual segmentation scheme (i.e. gold standard). Moreover, the high anisotropy of CT and MR data makes intra-slice information more reliable than inter-slice features. Nevertheless, slice-by-slice techniques should be supported with adjacent slice information since it is shown that features using the similarity of adjacent image slices outperform measures based on single-slice features in all cases. One of this similarity features is the distance transform which is shown to be effective on providing inter-slice similarity of abdominal organs. A parameter that control the vicinity of search area using the distance transform is ?, which determines the order of the power of distance transforms applied to the image. Since there is no study discussing the effect of ? on segmentation performance, the aim of this study is to analyze how changes on ? affects performance in terms of accuracy, computation and time requirements. The simulations performed on several medical image series and for four different abdominal organs show the importance of parameter analysis for distance transformation.

🔖

Cite This Article

SELVİ, E., ÖZDEMİR, M., SELVER, M. A. (2013). PERFORMANCE ANALYSIS OF DISTANCE TRANSFORM BASED INTER- SLICE SIMILARITY INFORMATION ON SEGMENTATION OF MEDICAL IMAGE SERIES. Mathematical and Computational Applications, Cilt: 18 - Sayı: 3, 511–520. https://ajindex.com/articles/279855

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (6)

  1. G. Flohr, S. Schaller, K. Stierstorfer, H. Bruder, B. M. Ohnesorge, Multi detector row CT systems and image reconstruction techniques, Radiology 235, 756-773, 2005
  2. L. Bidaut, Data and Image Processing for abdominal imaging, Abdominal Imaging, vol. 25, Springer Verlag, Berlin, 341-360, 2000
  3. J. E. Koss, F. D. Newman, T. K. Johnson, D. L. Kirch, Abdominal organ segmentation using texture transforms and a hopfield neural network, IEEE Transactions on Medical Imaging 18 (7) 640-648, 1999
  4. C. C. Lee, P. C. Chung, H. M. Tsai, Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules, IEEE Transactions on Information Technology in Biomedicine 7 (3), 208-217, 2003
  5. L. Vincent, Morphological grayscale reconstruction in image analysis: applications and efficient algorithms, IEEE Transactions on Image Processing 2(2) 176-201, 1993
  6. M. Alper Selver, A. Kocaoğlu, G. Demir, H. Doğan, O. Dicle, C. Güzeliş, Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation, Computers in Biology and Medicine 38(7), 765-784, 2008