IOCAS-IR  > 海洋地质与环境重点实验室
Wound intensity correction and segmentation with convolutional neural networks
Lu, Huimin1,2,3; Li, Bin4; Zhu, Junwu4; Li, Yujie1,4; Li, Yun4; Xu, Xing5,8; He, Li6; Li, Xin3; Li, Jianru7; Serikawa, Seiichi1
2017-03-25
Source PublicationCONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Volume29Issue:6
SubtypeArticle
AbstractWound 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.
KeywordIllumination Correction Big Data Level Set Model Ehealth Analysis System
DOI10.1002/cpe.3927
Indexed BySCI ; ISTP
Language英语
WOS IDWOS:000398035700013
Citation statistics
Cited Times:42[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Version出版稿
Identifierhttp://ir.qdio.ac.cn/handle/337002/136753
Collection海洋地质与环境重点实验室
Affiliation1.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
Recommended Citation
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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