Wound intensity correction and segmentation with convolutional neural networks

doi:10.1002/cpe.3927

IOCAS-IR > 海洋地质与环境重点实验室

	Wound intensity correction and segmentation with convolutional neural networks
	Lu, Huimin 1,2,3; Li, Bin 4; Zhu, Junwu 4; Li, Yujie 1,4; Li, Yun 4; Xu, Xing 5,8; He, Li 6; Li, Xin3 ; Li, Jianru 7; Serikawa, Seiichi 1
	2017-03-25
发表期刊	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
卷号	29 期号:6
文章类型	Article
摘要	Wound area changes over multiple weeks are highly predictive of the wound healing process. A big data eHealth system would be very helpful in evaluating these changes. We usually analyze images of the wound bed for diagnosing injury. Unfortunately, accurate measurements of wound region changes from images are difficult. Many factors affect the quality of images, such as intensity inhomogeneity and color distortion. To this end, we propose a fast level set model-based method for intensity inhomogeneity correction and a spectral properties-based color correction method to overcome these obstacles. State-of-the-art level set methods can segment objects well. However, such methods are time-consuming and inefficient. In contrast to conventional approaches, the proposed model integrates a new signed energy force function that can detect contours at weak or blurred edges efficiently. It ensures the smoothness of the level set function and reduces the computational complexity of re-initialization. To increase the speed of the algorithm further, we also include an additive operator-splitting algorithm in our fast level set model. In addition, we consider using a camera, lighting, and spectral properties to recover the actual color. Numerical synthetic and real-world images demonstrate the advantages of the proposed method over state-of-the-art methods. Experimental results also show that the proposed model is at least twice as fast as methods used widely. Copyright (C) 2016 John Wiley & Sons, Ltd.
关键词	Illumination Correction Big Data Level Set Model Ehealth Analysis System
DOI	10.1002/cpe.3927
收录类别	SCI ; ISTP
语种	英语
WOS记录号	WOS:000398035700013
引用统计	被引频次：117[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
版本	出版稿
条目标识符	http://ir.qdio.ac.cn/handle/337002/136753
专题	海洋地质与环境重点实验室
作者单位	1.Kyushu Inst Technol, Dept Elect & Elect Engn, Kitakyushu, Fukuoka 8048550, Japan 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China 3.Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China 4.Yangzhou Univ, Sch Informat Engn, Yangzhou 225009, Jiangsu, Peoples R China 5.Kyushu Univ, Dept Informat Sci & Elect Engn, Fukuoka 8190395, Japan 6.Qualcomm R&D Ctr, San Diego, CA 92121 USA 7.Tongji Univ, State Key Lab Marine Geol, Shanghai 200092, Peoples R China 8.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
第一作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Lu, Huimin,Li, Bin,Zhu, Junwu,et al. Wound intensity correction and segmentation with convolutional neural networks[J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,2017,29(6).
APA	Lu, Huimin.,Li, Bin.,Zhu, Junwu.,Li, Yujie.,Li, Yun.,...&Serikawa, Seiichi.(2017).Wound intensity correction and segmentation with convolutional neural networks.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,29(6).
MLA	Lu, Huimin,et al."Wound intensity correction and segmentation with convolutional neural networks".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 29.6(2017).

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文件名称/大小	文献类型	版本类型	开放类型	使用许可
Wound intensity corr（364KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	浏览