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Erschienen in:
Face Recognition via Domain Adaptation and Manifold Distance Metric Learning Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Leg Ulcer Long Term Analysis

verfasst von : Eros Pasero, Cristina Castagneri

Erschienen in: Intelligent Computing Theories and Application

Verlag: Springer International Publishing

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Abstract

Ulcers on legs and feet usually require long-term clinical treatment and follow-up. To facilitate the monitoring, we propose a fully automatic and low-cost method for ulcers detection and analysis. The ulcer segmentation is performed using an automatic processing based on pixel’s classification into background or not background classes. Features used to perform the classification are the values of three channels that define each pixel in the RGB color map and in the HSV color map.
We tested the algorithm on a dataset of 92 images, acquired from 14 different patients. The segmentation performances were evaluated in terms of overlap, recall and precision, by comparing the automatic segmentation with the manually one. The results show good average values of overlap, recall and precision.
Then, a Self-Organizing Map (SOM) was used for tissue classification. The SOM was trained in order to identify six colorimetric classes associated to different type of tissues.

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Vorheriges Kapitel Automatic License Plate Recognition Using Local Binary Pattern and Histogram Matching
Nächstes Kapitel Assessing Learners’ Reasoning Using Eye Tracking and a Sequence Alignment Method
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Metadaten
Titel
Leg Ulcer Long Term Analysis
verfasst von
Eros Pasero
Cristina Castagneri
Copyright-Jahr
2017
Buch
Intelligent Computing Theories and Application

Print ISBN: 978-3-319-63311-4

Electronic ISBN: 978-3-319-63312-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63312-1_4