The importance of illumination in a non-contact photoplethysmography imaging system for burn wound assessment

Mo, Wei; Mohan, Rachit; Li, Weizhi; Xu, Zhen; Sellke, Eric W.; Fan, Wei; DiMaio, J. Michael; Thatcher, Jeffrey E.

doi:10.1117/12.2080699

Cited by 14 publications

(8 citation statements)

References 10 publications

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“…In June 2015, using the keywords “Photoplethysmography/photoplethysmographic & camera”, we found 51 relevant studies in the Web of Science™ database. The main part of this topic review was based on 69 IPPG-related studies [9], [21], [37], [59], [60], [62]–[125]. A summary of several recent representative IPPG studies is presented in Table II.…”

Section: Imaging Photoplethysmographymentioning

confidence: 99%

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Sun

Thakor

2016

IEEE Trans. Biomed. Eng.

461

238

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Photoplethysmography (PPG) is a non-invasive optical technique for detecting microvascular blood volume changes in tissues. Its ease of use, low cost and convenience make it an attractive area of research in the biomedical and clinical communities. Nevertheless, its single spot monitoring and the need to apply a PPG sensor directly to the skin limit its practicality in situations such as perfusion mapping and healing assessments or when free movement is required. The introduction of fast digital cameras into clinical imaging monitoring and diagnosis systems, the desire to reduce the physical restrictions, and the possible new insights that might come from perfusion imaging and mapping inspired the evolution of conventional PPG technology to imaging PPG (IPPG). IPPG is a noncontact method that can detect heart-generated pulse waves by means of peripheral blood perfusion measurements. Since its inception, IPPG has attracted significant public interest and provided opportunities to improve personal healthcare. This study presents an overview of the wide range of IPPG systems currently being introduced along with examples of their application in various physiological assessments. We believe that the widespread acceptance of IPPG is happening, and it will dramatically accelerate the promotion of this healthcare model in the near future.

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Section: Imaging Photoplethysmographymentioning

confidence: 99%

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Sun

Thakor

2016

IEEE Trans. Biomed. Eng.

461

238

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“…The experiments of the problems of image annotation and image retrieval based on image tag completion over three benchmark data sets show the advantage of the proposed method. In the future, we will extend our work of CNN model to other machine learning problems beside image tag completion, such as computer vision [16,5,30,40,22,38,39], material engineering [32,33], portfolio choices [26,25,27,24,28], and biomedical engineering [4,3,2,1,21,13,10,23,12,31,29,9].…”

Section: Discussionmentioning

confidence: 99%

A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

Geng¹,

Zhang

et al. 2017

Lecture Notes in Computer Science

Self Cite

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In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

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“…The experiments over the benchmark data sets show its advantage over the existing cross-model data representation. In the future, we will also consider using the proposed method to other applications, such as integrated circuit design [41,42], software engineering [14,13,12], network measurement [3,4,2], commuter vision [44,43,5,35,27], medical imaging [23,29,17,34,22,33,10], etc. We will also consider to use some other loss function to learn the parameters of the CNN and the classifier to optimize the multivariate performance measures [36,24,21,26].…”

Section: Discussionmentioning

confidence: 99%

Cross-model convolutional neural network for multiple modality data representation

Wang

Cui

et al. 2017

Neural Comput & Applic

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A novel data representation method of convolutional neural network (CNN) is proposed in this paper to represent data of different modalities. We learn a CNN model for the data of each modality to map the data of different modalities to a common space, and regularize the new representations in the common space by a cross-model relevance matrix. We further impose that the class label of data points can also be predicted from the CNN representations in the common space. The learning problem is modeled as a minimization problem, which is solved by an augmented Lagrange method (ALM) with updating rules of Alternating direction method of multipliers (ADMM). The experiments over benchmark of sequence data of multiple modalities show its advantage.

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The importance of illumination in a non-contact photoplethysmography imaging system for burn wound assessment

Cited by 14 publications

References 10 publications

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging

A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

Cross-model convolutional neural network for multiple modality data representation

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