Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models
Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi - 36(2019), 6 vom: 25. Dez., Seite 945-956 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Xiong, Xiaoliang [VerfasserIn] |
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| Links: |
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| Themen: |
Adaptive sub-regional evolution |
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| Anmerkungen: |
Date Completed 16.01.2020 Date Revised 01.07.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.7507/1001-5515.201902011 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM304755281 |
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| 245 | 1 | 0 | |a Kidney tumor segmentation in ultrasound images using adaptive sub-regional evolution level set models |
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| 500 | |a Date Revised 01.07.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a adaptive sub-regional evolution | |
| 650 | 4 | |a centroid | |
| 650 | 4 | |a kidney tumor | |
| 650 | 4 | |a level set | |
| 650 | 4 | |a ultrasound image | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Dai |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Zhaoxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Sheng |e verfasserin |4 aut | |
| 700 | 1 | |a Xin, Xiaojie |e verfasserin |4 aut | |
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