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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2013, 18(3), 511-520;

Performance Analysis of Distance Transform Based Inter-Slice Similarity Information on Segmentation of Medical Image Series

Department of Electrical and Electronics Engineering, Dokuz Eylül University, 35160 Kaynaklar Kampusu, Buca, Izmir, Turkey
Author to whom correspondence should be addressed.
Published: 1 December 2013
PDF [811 KB, uploaded 10 March 2016]


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 three-dimensional 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 α, which determines the order of the power of distance transforms applied to the image. Since there is no study discussing the effect of α on segmentation performance, the aim of this study is to analyze how changes on α 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.
Keywords: Segmentation; Medical Image; Distance Transform; Classification Segmentation; Medical Image; Distance Transform; Classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Selvi, E.; Özdemir, M.; Selver, M.A. Performance Analysis of Distance Transform Based Inter-Slice Similarity Information on Segmentation of Medical Image Series. Math. Comput. Appl. 2013, 18, 511-520.

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