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Non-invasive detection and classification of skin cancer from visual and cross-sectional images

Published: 26 October 2011 Publication History

Abstract

This paper describes the various methods that are implemented to diagnose a sample of skin for malignancy. Skin cancer detection at the earliest stage possible is vital to increase the chance of survival of the affected patient. Imaging in this field happens to be at the cross-roads. Skin cancer imaging can be visual in nature (nevoscope imaging, electron microscope, naked eye) or non-visual (optical coherence tomography (OCT), Raman spectroscopy). ABCDs is a set of rules that are the first step that is applied to determine the nature of a mole. Although extensively used as front line methodology for malignancy in moles, it is not deterministic in nature. Each of the techniques described in this paper analyze the samples of the skin lesion under the scanner in a varied way. The samples of the skin lession can be either a visual depiction or in the form of a cross-section. We have after extensive experimentation arrived at two different ways to analyze the samples obtained as a result of the imaging. For the sample that we have obtained as a result of the nevoscope visual imaging, the power spectra appears to be the most discriminative and effective way of classification as against the use of discrete wavelet transformation in case of the cross-sections obtained from OCT. The aim is to ultimately build an automated system that has the capability to discriminating and classifying the skin samples into three main classes; namely, benign, precancerous and malignant independent of the scanning methodology.

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Cited By

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  • (2015)Human ringworm detection using wavelet energy signature2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)10.1109/ReTIS.2015.7232874(178-182)Online publication date: Jul-2015
  • (2012)Cancerous Lesion Detection from Nevoscope Skin Surface Images via Parametric Color ClusteringPattern Recognition10.1007/978-3-642-33506-8_46(367-375)Online publication date: 2012

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cover image ACM Other conferences
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
October 2011
949 pages
ISBN:9781450309134
DOI:10.1145/2093698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Universitat Pompeu Fabra
  • IEEE
  • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
  • River Publishers: River Publishers
  • CTTC: Technological Center for Telecommunications of Catalonia
  • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

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Author Tags

  1. nevoscope imaging
  2. periodogram
  3. power spectra
  4. skin cancer
  5. skin sample discrimination

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ISABEL '11
Sponsor:
  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

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Cited By

View all
  • (2015)Human ringworm detection using wavelet energy signature2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)10.1109/ReTIS.2015.7232874(178-182)Online publication date: Jul-2015
  • (2012)Cancerous Lesion Detection from Nevoscope Skin Surface Images via Parametric Color ClusteringPattern Recognition10.1007/978-3-642-33506-8_46(367-375)Online publication date: 2012

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