.png){kind=icon}
返回
Granular computing in model based abdominal organs detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compmedimag.2015.03.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Detection of region specific voxel is a true challenge in many segmentation procedures. In this study a concept of implementing granular computing in the detection of anatomical structures in abdominal computed tomography (CT) scans is introduced. After proving the usefulness of the information granules to identify voxels that mark certain organs, an automatic model-based approach has been developed. A three-parameter granule that combines the interval and density distribution of voxels has been introduced and employed to identify organ specific voxels of the liver, spleen and kidneys. The specificity of the information granules varies between 90 and 99% for the liver and spleen and over 85% for the kidneys. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Granular computing
Information granules
Image processing
Abdominal computed tomography (CT)
Model based object extraction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.9
论文数:
2.4K
被引数:
5.0K
机构
引用论文
Automated hepatic volumetry for living related liver transplantation at multisection CT.png)
RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏Crystal Structure, DFT Calculation, and Hirshfeld Surface Analysis of the 1-(Cyclohex-1-en-1-yl)-3-(prop-2-yn-1-yl)-1,3-dihydro-2H-benzimidazol-2-oneCrystals
OAAI
CrystalsIF0
OAAI分享收藏
Multidimensional comprehensive and integrated analysis of the potential function of TMEM25 in renal clear cell carcinoma with low expression statusAgingOAAI
AgingIF0
OAAI分享收藏
查看更多内容