Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma

Neittaanmäki, N.; Salmivuori, M.; Pölönen, Ilkka; Jeskanen, L.; Ranki, A.; Saksela, O.; Snellman, E.; Grönroos, M.

doi:10.1111/bjd.15267

dc.contributor.author	Neittaanmäki, N.
dc.contributor.author	Salmivuori, M.
dc.contributor.author	Pölönen, Ilkka
dc.contributor.author	Jeskanen, L.
dc.contributor.author	Ranki, A.
dc.contributor.author	Saksela, O.
dc.contributor.author	Snellman, E.
dc.contributor.author	Grönroos, M.
dc.date.accessioned	2018-01-15T10:30:20Z
dc.date.available	2018-10-07T21:35:30Z
dc.date.issued	2017
dc.identifier.citation	Neittaanmäki, N., Salmivuori, M., Pölönen, I., Jeskanen, L., Ranki, A., Saksela, O., Snellman, E., & Grönroos, M. (2017). Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma. <i>British Journal of Dermatology</i>, <i>177</i>(6), 1742-1744. <a href="https://doi.org/10.1111/bjd.15267" target="_blank">https://doi.org/10.1111/bjd.15267</a>
dc.identifier.other	CONVID_26451019
dc.identifier.uri	https://jyx.jyu.fi/handle/123456789/56723
dc.language.iso	eng
dc.publisher	Blackwell Scientific Publications
dc.relation.ispartofseries	British Journal of Dermatology
dc.subject.other	spektrikuvaus
dc.subject.other	melanooma
dc.subject.other	spectral imaging
dc.subject.other	melanoma
dc.title	Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma
dc.type	journal article
dc.identifier.urn	URN:NBN:fi:jyu-201801101137
dc.contributor.laitos	Informaatioteknologian tiedekunta	fi
dc.contributor.laitos	Faculty of Information Technology	en
dc.contributor.oppiaine	Tietotekniikka	fi
dc.contributor.oppiaine	Mathematical Information Technology	en
dc.type.uri	http://purl.org/eprint/type/JournalItem
dc.date.updated	2018-01-10T10:15:29Z
dc.type.coar	http://purl.org/coar/resource_type/c_6501
dc.description.reviewstatus	nonPeerReviewed
dc.format.pagerange	1742-1744
dc.relation.issn	0007-0963
dc.relation.numberinseries	6
dc.relation.volume	177
dc.type.version	acceptedVersion
dc.rights.copyright	© 2016 British Association of Dermatologists. This is a final draft version of an article whose final and definitive form has been published by Wiley. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevel	openAccess	fi
dc.type.publication	article
dc.subject.yso	spektrikuvaus
dc.subject.yso	melanooma
jyx.subject.uri	http://www.yso.fi/onto/yso/p26364
jyx.subject.uri	http://www.yso.fi/onto/yso/p15128
dc.relation.doi	10.1111/bjd.15267
dc.type.okm	B1

Aineistoon kuuluvat tiedostot

Nimi:: neittaanmakietal2016britishjou ...
Koko:: 355.0Kb
Tiedostomuoto:: PDF
Kuvaus:: Final Draft

Katso/Avaa

Aineisto kuuluu seuraaviin kokoelmiin

Informaatioteknologian tiedekunta [2330]

Näytä suppeat kuvailutiedot

Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study

Räsänen, Janne; Salmivuori, Mari; Pölönen, Ilkka; Grönroos, Mari; Neittaanmäki, Noora (Society for Publication of Acta Dermato-Venereologica, 2021)

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this ...
Ablative fractional laser‐assisted photodynamic therapy for lentigo maligna : a prospective pilot study

Räsänen, J.E.; Neittaanmäki, N.; Jeskanen, L.; Pölönen, I.; Snellman, E.; Grönroos, M. (Wiley-Blackwell Publishing Ltd., 2020)

Background Lentigo maligna (LM) is an in‐situ form of melanoma carrying a risk of progression to invasive lentigo maligna melanoma (LMM). LM poses a clinical challenge, with subclinical extension and high recurrence rates ...
Delineating Margins of Lentigo Maligna Using a Hyperspectral Imaging System

Neittaanmäki-Perttu, Noora; Grönroos, Mari; Jeskanen, Leila; Pölönen, Ilkka; Ranki, Annamari; Saksela, Olli; Snellman, Erna (Society for the Publication of Acta Dermato - Venereologica, 2015)

Lentigo maligna (LM) is an in situ form of melanoma which can progress into invasive lentigo maligna melanoma (LMM). Variations in the pigmentation and thus visibility of the tumour make assessment of lesion ...
Deep semantic segmentation for skin cancer detection from hyperspectral images

Karhu, Anette (2020)

As skin cancer types are a growing concern worldwide, a new screening tool combined with automation may help the clinicians in clinical examinations of lesions. A novel hyperspectral imager prototype has been noted to be ...
Piecewise anomaly detection using minimal learning machine for hyperspectral images

Raita-Hakola, A.-M.; Pölönen, I. (Copernicus Publications, 2021)

Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are ...

Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma

Aineistoon kuuluvat tiedostot

Aineisto kuuluu seuraaviin kokoelmiin

Samankaltainen aineisto

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study ﻿

Ablative fractional laser‐assisted photodynamic therapy for lentigo maligna : a prospective pilot study ﻿

Delineating Margins of Lentigo Maligna Using a Hyperspectral Imaging System ﻿

Deep semantic segmentation for skin cancer detection from hyperspectral images ﻿

Piecewise anomaly detection using minimal learning machine for hyperspectral images ﻿

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study

Ablative fractional laser‐assisted photodynamic therapy for lentigo maligna : a prospective pilot study

Delineating Margins of Lentigo Maligna Using a Hyperspectral Imaging System

Deep semantic segmentation for skin cancer detection from hyperspectral images

Piecewise anomaly detection using minimal learning machine for hyperspectral images