Welcome to the InfoSci Platform
Reference Hub2
Microscope Volume Segmentation Improved through Non-Linear Restoration

Microscope Volume Segmentation Improved through Non-Linear Restoration

Moacir P. Ponti
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 10
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781613502969|DOI: 10.4018/jncr.2010100104
Cite Article Cite Article

MLA

Ponti, Moacir P. "Microscope Volume Segmentation Improved through Non-Linear Restoration." IJNCR vol.1, no.4 2010: pp.37-46. http://doi.org/10.4018/jncr.2010100104

APA

Ponti, M. P. (2010). Microscope Volume Segmentation Improved through Non-Linear Restoration. International Journal of Natural Computing Research (IJNCR), 1(4), 37-46. http://doi.org/10.4018/jncr.2010100104

Chicago

Ponti, Moacir P. "Microscope Volume Segmentation Improved through Non-Linear Restoration," International Journal of Natural Computing Research (IJNCR) 1, no.4: 37-46. http://doi.org/10.4018/jncr.2010100104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

An efficient segmentation technique based on the use of a modified k-Means algorithm and the Otsu’s thresholding method is improved through a non-linear restoration of microscope volumes. An algorithm is proposed to automatically compute the k value for the clustering k-Means method. The unsupervised algorithm is used in the context of segmentation by considering regions as clusters. A comparison between the segmentation results before and after restoration is presented. The evaluation of the region segmentation included the root mean squared error and a normalized uniformity measure. Results showed significant improvement of segmentation when using the non-linear restoration method based on prior known information, such as the imaging system and the noise statistics.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.