Dermatologic Hyperspectral Imaging System for Skin Cancer Diagnosis Assistance

Fabelo, Himar; Carretero, G.; Almeida, Pablo; García, Aday; Hernández, J; Godtliebsen, Fred; Melian, V.M.; Martínez, Beatriz; Beltran, Patricia; Ortega, Samuel; Marrero, Margarita Mele; Sarmiento, Roberto; Castaño, Irene Mencía

doi:10.1109/dcis201949030.2019.8959869

Cited by 20 publications

(29 citation statements)

References 18 publications

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“…We are currently in the process of acquiring a large number of HSI data sets related to skin moles and lesions in collaboration with several hospitals in the Canary Islands, Spain. Our long term goal is to design a successful classifier for such data and the preliminary results obtained so far are promising [14]. However, we believe that a system capable of monitoring dynamical changes in a mole will be even more important as it is likely to be the best way to detect severe skin cancer at an early stage.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…Recall from Section 1 that the data in our artificial data example were first obtained by acquiring a hyperspectral image of a skin mole and then modifying it manually in order to introduce a small distortion that could simulate a change in the mole itself. The HSI system used for the mole example differs from the one described in Section 3 and a detailed description can be found in [14]. In our analysis, we compared the proposed novel technique to the method of Hindberg et al described in [7].…”

Section: Artificial Mole Examplementioning

confidence: 99%

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Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

et al. 2020

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Given an object of interest that evolves in time, one often wants to detect possible changes in its properties. The first changes may be small and occur in different scales and it may be crucial to detect them as early as possible. Examples include identification of potentially malignant changes in skin moles or the gradual onset of food quality deterioration. Statistical scale-space methodologies can be very useful in such situations since exploring the measurements in multiple resolutions can help identify even subtle changes. We extend a recently proposed scale-space methodology to a technique that successfully detects such small changes and at the same time keeps false alarms at a very low level. The potential of the novel methodology is first demonstrated with hyperspectral skin mole data artificially distorted to include a very small change. Our real data application considers hyperspectral images used for food quality detection. In these experiments the performance of the proposed method is either superior or on par with a standard approach such as principal component analysis.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Artificial Mole Examplementioning

confidence: 99%

Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

et al. 2020

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“…The HS dermatologic acquisition system used in this work for the assistance in the diagnosis of PSLs is a custom development described in detail in [ 22 ]. The system is composed by a snapshot HS camera (Cubert UHD 185, Cubert GmbH, Ulm, Germany) capable of capturing HS data in the visual and near-infrared (VNIR) spectral range from 450 to 950 nm, having a spectral resolution of 8 nm (125 spectral bands) and a spatial resolution of 50 × 50 pixels (pixel size of 240 × 240 µm) ( Figure 1 a).…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Leon

Martinez-Vega

Fabelo

et al. 2020

JCM

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Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

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“…The device was positioned at a short distance from the skin and illumination was provided by a halogen lamp with fiber optic ring. Fabelo et al 62 also used a handheld prototype, with a snapshot HSI camera at its core. The system captures a 12 × 12 mm 2 area with a small spatial resolution (50 × 50 pixels) in less than a second.…”

Section: Hyperspectral Prototype Systemsmentioning

confidence: 99%

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

et al. 2022

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Significance: Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.Aim: We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.Approach: A systematic review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.Results: HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.Conclusions: To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.

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Dermatologic Hyperspectral Imaging System for Skin Cancer Diagnosis Assistance

Cited by 20 publications

References 18 publications

Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

Early Detection of Change by Applying Scale-Space Methodology to Hyperspectral Images

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

Hyperspectral and multispectral image processing for gross-level tumor detection in skin lesions: a systematic review

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