An Automatic Image Segmentation Algorithm Involving Shortest Path Basins

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43926142" target="_blank" >RIV/49777513:23520/15:43926142 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/article/10.1134%2FS1054661815010162" target="_blank" >http://link.springer.com/article/10.1134%2FS1054661815010162</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1134/S1054661815010162" target="_blank" >10.1134/S1054661815010162</a>

Result language
angličtina
Original language name
An Automatic Image Segmentation Algorithm Involving Shortest Path Basins
Original language description
Image segmentation is a process of partitioning input image into meaningful regions. It is a chal lenging task that is involved in almost every image processing system. Currently lot of methods for image seg mentation with different approaches was created. Between all of them the methods based on graph theory are more and more popular nowadays. Segmentation methods could be classified for example to interactive and automatic ones. The further class of methods benefits from a user interaction that provides valuable informa tion about a segmentation problem. The later class of methods doesn’t incorporate any user interaction. Nev ertheless fully automatic methods that are both precise and robust are still hard to find. In this paper a new method based on shortest path in a graph is presented. This method automatically places seed points that are further used for image segmentation in the sense of path basins. This method allows segment an input image to a predefined or to an undefined number of image segments. Derived seed points could also be used in other interactive methods instead of a user interaction. Experiments with this method show its potential for seg menting a general class of images.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems

Project
<a href="/en/project/NT13326" target="_blank" >NT13326: Improvement of resecability of the malignant processes using the more accurate methods for measuring parametres of the remnant liver parenchyma – computer asisted diagnostic and software modeling</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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