Context-Based Segmentation of Renal Corpuscle from Microscope Renal Biopsy Image Sequence
Summary :
A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.5 pp.1114-1121
- Publication Date
- 2015/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.1114
- Type of Manuscript
- PAPER
- Category
- Image
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Keyword
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Jun ZHANG, Jinglu HU, "Context-Based Segmentation of Renal Corpuscle from Microscope Renal Biopsy Image Sequence" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 5, pp. 1114-1121, May 2015, doi: 10.1587/transfun.E98.A.1114.
Abstract: A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.1114/_p
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@ARTICLE{e98-a_5_1114,
author={Jun ZHANG, Jinglu HU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Context-Based Segmentation of Renal Corpuscle from Microscope Renal Biopsy Image Sequence},
year={2015},
volume={E98-A},
number={5},
pages={1114-1121},
abstract={A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.},
keywords={},
doi={10.1587/transfun.E98.A.1114},
ISSN={1745-1337},
month={May},}
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TY - JOUR
TI - Context-Based Segmentation of Renal Corpuscle from Microscope Renal Biopsy Image Sequence
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1114
EP - 1121
AU - Jun ZHANG
AU - Jinglu HU
PY - 2015
DO - 10.1587/transfun.E98.A.1114
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2015
AB - A renal biopsy is a procedure to get a small piece of kidney for microscopic examination. With the development of tissue sectioning and medical imaging techniques, microscope renal biopsy image sequences are consequently obtained for computer-aided diagnosis. This paper proposes a new context-based segmentation algorithm for acquired image sequence, in which an improved genetic algorithm (GA) patching method is developed to segment different size target. To guarantee the correctness of first image segmentation and facilitate the use of context information, a boundary fusion operation and a simplified scale-invariant feature transform (SIFT)-based registration are presented respectively. The experimental results show the proposed segmentation algorithm is effective and accurate for renal biopsy image sequence.
ER -
English
