Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning

Rey-Barroso, Laura; Burgos-Fernández, Francisco J.; Ares, Miguel; Royo, Santiago; Puig, Susana; Malvehy, Josep; Pellacani, Giovanni; Espinar, Daniel; Sicilia, Natàlia; Vilaseca, Meritxell

doi:10.1364/boe.10.003404

Cited by 9 publications

(8 citation statements)

References 11 publications

(12 reference statements)

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“…It also incorporated a color camera to take a conventional RGB image of the lesions. Rey-Barroso et al [ 65 ] used the same 3D imaging scanner to obtain morphological parameters of skin lesions related to the perimeter, area, and volume with micrometric precision and found significant differences between melanoma and nevus.…”

Section: Three-dimensional Topographymentioning

confidence: 99%

“…In the last two decades, datasets acquired with these techniques have been employed as inputs in machine and deep learning algorithms to provide an objective judgement during the physician’s evaluation for the early detection of equivocal lesions. Although several approaches have been proposed, most of them are based on images as input data, while only a few relies on spectroscopic [ 106 ], OCT [ 107 ], or 3D [ 65 ] information.…”

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

“…There is not an ideal classification algorithm that outperforms the others as it depends on the dataset, image segmentation, and feature extraction (see Figure 9 ); however, in the literature, some of them have been found to provide better discrimination among skin lesions. Support Vector Machine (SVM) is the most applied classifier, as it shows the best performance [ 107 , 109 , 110 , 111 , 112 , 114 , 115 , 116 , 117 , 118 ] followed by k-Nearest Neighbors (KNN) [ 65 , 106 , 107 , 108 , 109 , 110 , 111 , 116 , 117 , 118 ], Neural Networks (NN) [ 110 , 114 , 115 , 117 , 119 ], Random Forest (RF) [ 107 , 109 , 113 , 114 ] and Decision Trees (DT) [ 65 , 109 , 110 , 111 , 117 , 118 ]. SVMs are based on statistics to build hyperplanes that maximize the distance between sets of data points [ 120 , 121 ].…”

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

“… From Rey-Barroso et al [ 65 ], ( a ) 3D fringe projector. The fringe images are reconstructed and unwrapped to obtain 3D images with superimposed color texture.…”

Section: Figurementioning

confidence: 99%

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Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

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Section: Three-dimensional Topographymentioning

confidence: 99%

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

Section: Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

“… From Rey-Barroso et al [ 65 ], ( a ) 3D fringe projector. The fringe images are reconstructed and unwrapped to obtain 3D images with superimposed color texture.…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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“…3D scanning technology is becoming an essential tool in many medical subfields [16]. It has been applied for the monitoring of skin treatment therapy [17], or the characterization of cancer lesions [18]. However, each procedure or application has specific metrological requirements, like the field of view or the depth resolution, which typically render a device unsuitable for multiple applications.…”

Section: Introductionmentioning

confidence: 99%

Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting

Pineda¹,

Vargas²,

Romero³

et al. 2019

PLoS ONE

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The conventional reading of the skin prick test (SPT) for diagnosing allergies is prone to inter- and intra-observer variations. Drawing the contours of the skin wheals from the SPT and scanning them for computer processing is cumbersome. However, 3D scanning technology promises the best results in terms of accuracy, fast acquisition, and processing. In this work, we present a wide-field 3D imaging system for the 3D reconstruction of the SPT, and we propose an automated method for the measurement of the skin wheals. The automated measurement is based on pyramidal decomposition and parametric 3D surface fitting for estimating the sizes of the wheals directly. We proposed two parametric models for the diameter estimation. Model 1 is based on an inverted Elliptical Paraboloid function, and model 2 on a super-Gaussian function. The accuracy of the 3D imaging system was evaluated with validation objects obtaining transversal and depth accuracies within ± 0.1 mm and ± 0.01 mm, respectively. We tested the method on 80 SPTs conducted in volunteer subjects, which resulted in 61 detected wheals. We analyzed the accuracy of the models against manual reference measurements from a physician and obtained that the parametric model 2 on average yields diameters closer to the reference measurements (model 1: -0.398 mm vs. model 2: -0.339 mm) with narrower 95% limits of agreement (model 1: [-1.58, 0.78] mm vs. model 2: [-1.39, 0.71] mm) in a Bland-Altman analysis. In one subject, we tested the reproducibility of the method by registering the forearm under five different poses obtaining a maximum coefficient of variation of 5.24% in the estimated wheal diameters. The proposed method delivers accurate and reproducible measurements of the SPT.

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Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal

Luo,

Zhong,

et al. 2023

Computers in Biology and Medicine

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Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning

Cited by 9 publications

References 11 publications

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting

Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal

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