skip to content
Dovepress - Open Access to Scientific and Medical Research
View our mobile site

8838

An unsupervised strategy for biomedical image segmentation

Original Research

(2325) Views  (560) Full article downloads

Authors: Roberto Rodríguez, Rubén Hernández

Published Date September 2010 Volume 2010:3 Pages 67 - 73
DOI: http://dx.doi.org/10.2147/AABC.S11918

Roberto Rodríguez1, Rubén Hernández2
1Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, Mexico

Abstract: Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In this paper, we present an unsupervised strategy for biomedical image segmentation using an algorithm based on recursively applying mean shift filtering, where entropy is used as a stopping criterion. This strategy is proven with many real images, and a comparison is carried out with manual segmentation. With the proposed strategy, errors less than 20% for false positives and 0% for false negatives are obtained.

Keywords: segmentation, mean shift, unsupervised segmentation, entropy




 

Other articles by Professor Roberto Odriguez



Readers of this article also read:

Role of aliskiren in cardio-renal protection and use in hypertensives with multiple risk factors
Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction
Computer applications for prediction of protein–protein interactions and rational drug design
Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms
Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg–Marquardt neural network
Construction of random perfect phylogeny matrix
Perception of risk and benefit in patient-centered communication and care
The relationship between deliberate self-harm behavior, body dissatisfaction, and suicide in adolescents: current concepts
Zinc oxide nanoparticles as selective killers of proliferating cells
Cumulative clinical experience from over a decade of use of levofloxacin in community-acquired pneumonia: critical appraisal and role in therapy
  • Testimonials

    "... I was impressed at the rapidity of publication from submission to final acceptance." Dr Edwin Thrower, PhD, Yale University