Color Segmentation Applied to Study of the Angiogenesis. Part I

Authors:
Roberto M. Rodrí/guez;Teresa E. Alarcó/n;Roberto W. Navarro;Leudis S. Cuello
Affiliations:
Group of Digital Signal Processing (GUPDIS), Institute of Cybernetics, Mathematics and Physics (ICIMAF), Calle 15 No. 551 e/CyD CP 10400, La Habana, Cuba/ e-mail: rrm@cidet.icmf.inf.cu
Venue:
Journal of Intelligent and Robotic Systems
Year:
2002

Citing 4
Cited 7

ANGY: A Rule-Based Expert System for Automatic Segmentation of Coronary Vessels From Digital Subtracted Angiograms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edges: saliency measures and automatic thresholding

Machine Vision and Applications
Digital Picture Processing

Digital Picture Processing
Color image segmentation for Bladder Cancer Diagnosis

Mathematical and Computer Modelling: An International Journal

A Strategy for Reduction of Noise in Segmented Images. Its Use in the Study of Angiogenesis

Journal of Intelligent and Robotic Systems
A Strategy for Atherosclerosis Image Segmentation by Using Robust Markers

Journal of Intelligent and Robotic Systems
A comparison between two robust techniques for segmentation of blood vessels

Computers in Biology and Medicine
A strategy for blood vessels segmentation based on the threshold which combines statistical and scale space filter

Computer Methods and Programs in Biomedicine
A new strategy to obtain robust markers for blood vessels segmentation by using the watersheds method

Computers in Biology and Medicine
A Segmentation Algorithm Based on an Iterative Computation of the Mean Shift Filtering

Journal of Intelligent and Robotic Systems
An image segmentation algorithm using iteratively the mean shift

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of this work is the color image segmentation of blood vessels in the angiogenesis process. With the purpose of comparison we carried out some experiments using different methods and in different color spaces, arriving to the final conclusion that the best results are obtained, according to our application, in the RGB space. We performed a number of researches in the red, green and blue channels and we propose an alternative strategy that is, working in a single channel. The segmentation task can be decomposed into two stages. First, selections of color space, and second, selection of channel of major information and finally segmentation process. Our results were compared with manual segmentation realized by an expert, where difference errors of less than 4% were observed. It is demonstrated by extensive experimentation, using real image data, that proposed strategy is fast and adequate in the environment of a personal computer. These images will be subject to a further morphometrical analysis, in order to diagnose and prognosticate automatically malign tumors.