Skin Image Segmentation Using a Self-Organizing Map and Genetic Algorithms

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概要

• 論文の詳細を見る

• In order to distinguish malignant from benign skin lesions dermatologists use a microscope that shows the pigmented structure of the skin. However, it can be difficult to classify a skin lesion as benign or malignant using a dermoscopic image alone. This motivates computer analysis of dermoscopic images by digital image processing. The first step for a computer analysis is the segmentation of the image into regions of the same color, i.e. regions of the same color should be assigned the same gray level and regions of different colors should be assigned different gray levels. The number of colors is not known in advance. This paper presents a color clustering method that determines the number of colors automatically. First the RGB image is transformed into the L∗u∗v∗ color space and segmented by a self-organizing map (SOM). After completion of the training a genetic algorithm groups the SOM neurons into clusters searching for a grouping that optimizes the Davies-Bouldin index. Various genetic algorithms are presented and evaluated for this purpose.

• 社団法人 電気学会の論文
• 2003-11-01

著者

• Murao Hajime

  Faculty Of Cross-cultural Studies Kobe University

• Tamaki H

  Department Of Computer And Systems Engineering Faculty Of Engineering Kobe University

• Tamaki Hisashi

  Kobe University

• GALDA Harald

  Department of Computer and Systems Engineering, Faculty of Engineering, Kobe University

• GALDA Harald

  Kobe University, Faculty of Engineering, Department of Computer and Systems Engineering

• MURAO Hajime

  Kobe University, Faculty of Cross-Cultural Studies

• KITAMURA Shinzo

  Kobe University, Faculty of Engineering, Department of Computer and Systems Engineering

• Galda Harald

  Department Of Computer And Systems Engineering Faculty Of Engineering Kobe University

• Kitamura Shinzo

  Department Of Computer And Systems Engineering Faculty Of Engineering Kobe University

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