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Segmentation of extrapulmonary tuberculosis infection using modified automatic seeded region growing

Avazpour, Iman, Saripan, M. Iqbal, Nordin, Abdul Jalil and Abdullah, Raja Syamsul Azmir Raja 2009, Segmentation of extrapulmonary tuberculosis infection using modified automatic seeded region growing, Biological procedures online, vol. 11, no. 1, pp. 241-252, doi: 10.1007/s12575-009-9013-0.

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Title Segmentation of extrapulmonary tuberculosis infection using modified automatic seeded region growing
Author(s) Avazpour, ImanORCID iD for Avazpour, Iman orcid.org/0000-0002-0770-4751
Saripan, M. Iqbal
Nordin, Abdul Jalil
Abdullah, Raja Syamsul Azmir Raja
Journal name Biological procedures online
Volume number 11
Issue number 1
Start page 241
End page 252
Total pages 12
Publisher BioMed Central
Place of publication London, Eng.
Publication date 2009-07
ISSN 1480-9222
Keyword(s) seeded region growing
segmentation
dual modality imaging
positron emission tomography
computed tomography
Summary  In the image segmentation process of positron emission tomography combined with computed tomography (PET/CT) imaging, previous works used information in CT only for segmenting the image without utilizing the information that can be provided by PET. This paper proposes to utilize the hot spot values in PET to guide the segmentation in CT, in automatic image segmentation using seeded region growing (SRG) technique. This automatic segmentation routine can be used as part of automatic diagnostic tools. In addition to the original initial seed selection using hot spot values in PET, this paper also introduces a new SRG growing criterion, the sliding windows. Fourteen images of patients having extrapulmonary tuberculosis have been examined using the above-mentioned method. To evaluate the performance of the modified SRG, three fidelity criteria are measured: percentage of under-segmentation area, percentage of over-segmentation area, and average time consumption. In terms of the under-segmentation percentage, SRG with average of the region growing criterion shows the least error percentage (51.85%). Meanwhile, SRG with local averaging and variance yielded the best results (2.67%) for the over-segmentation percentage. In terms of the time complexity, the modified SRG with local averaging and variance growing criterion shows the best performance with 5.273 s average execution time. The results indicate that the proposed methods yield fairly good performance in terms of the over- and under-segmentation area. The results also demonstrated that the hot spot values in PET can be used to guide the automatic segmentation in CT image.
Language eng
DOI 10.1007/s12575-009-9013-0
Field of Research MD Multidisciplinary
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2009, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086623

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.