Medical Image Segmentation for Mobile Electronic Patient Charts Using Numerical Modeling of IoT

Seung-Hoon Chae, Daesung Moon, Deok Gyu Lee and Sung Bum Pan

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

Abstract: Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2014/815039

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:815039

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().