Vitiligo Detection Using Cepstral Coefficients

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Vitiligo is a pathology that causes the appearance of macules achromic (white spots) in the skin. Besides, generates a negative emotional burden in the people that have it, what make necessary to develop suitable methods to identify and treat it properly. In this paper we propose a novel system formed by two stages: The Front End where the principal characteristics of the image are extracted using the Mel Frequency Cepstral Coefficients (MFCC) and i-Vectors (techniques widely used in speech processing) and the Back End, where these characteristics are received and through a classifier is define whether and image contains or not vitiligo. Artificial Neural Networks and Support Vector Machines were selected as classifiers. Results shows that both MFCC and i-Vectors could be used in the field of image processing. Although, the i-Vectors allows us to decrease more the dimensionality of a feature vector and without losing the characteristics of the high dimensionality, this was reflected in their performance with an accuracy of 95.28% to recognize correctly images.

Original languageEnglish
Title of host publicationAdvances in Emerging Trends and Technologies Volume 1
EditorsMiguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
PublisherSpringer Verlag
Pages389-398
Number of pages10
ISBN (Print)9783030320218
DOIs
StatePublished - 1 Jan 2020
Event1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador
Duration: 29 May 201931 May 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1066
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
Country/TerritoryEcuador
Cityquito
Period29/05/1931/05/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Dimensionality reduction
  • Feature extraction
  • i-Vectors
  • Medical image processing
  • MFCC

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