Academic Journals Database
Disseminating quality controlled scientific knowledge

An unsupervised strategy for biomedical image segmentation

Author(s): Roberto Rodríguez | Rubén Hernández

Journal: Advances and Applications in Bioinformatics and Chemistry
ISSN 1178-6949

Volume: 2010;
Issue: default;
Start page: 67;
Date: 2010;
Original page

Roberto Rodríguez1, Rubén Hernández21Digital Signal Processing Group, Institute of Cybernetics, Mathematics, and Physics, Havana, Cuba; 2Interdisciplinary Professional Unit of Engineering and Advanced Technology, IPN, MexicoAbstract: 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

Tango Jona
Tangokurs Rapperswil-Jona

RPA Switzerland

RPA Switzerland

Robotic process automation