Utilization of medical image soft segmentation based on fuzzy sets classification process modified by local aggregation approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241599" target="_blank" >RIV/61989100:27240/18:10241599 - isvavai.cz</a>

Výsledek na webu

<a href="http://journal.utem.edu.my/index.php/jtec/article/view/3745/2618" target="_blank" >http://journal.utem.edu.my/index.php/jtec/article/view/3745/2618</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Utilization of medical image soft segmentation based on fuzzy sets classification process modified by local aggregation approach

Popis výsledku v původním jazyce

Medical image segmentation has been a challenging task for a long time. In the current age, we are overcrowded by medical image data acquired from various sources, such as CT, MR, ultrasound and many others. We usually need to perform segmentation, detection and extraction of objects of interest for further processing. This process includes quantification of parameters to determine a clinical evaluation. There are multiregional segmentation methods that allow for differentiation of individual morphological objects. However, the commonly used hard thresholding approaches lack of robustness in noisy environment leading to an incorrect pixel classification. Image segmentation based on fuzzy set theory brings much more effective alternative for image thresholding gained by local aggregation, making this method more noise resistive. We consciously performed a comparative analysis of articular cartilage and blood vessels segmentation. It was an obvious method utilization in which the native image features are badly recognizable and the objects features are well observed.

Název v anglickém jazyce

Utilization of medical image soft segmentation based on fuzzy sets classification process modified by local aggregation approach

Popis výsledku anglicky

Medical image segmentation has been a challenging task for a long time. In the current age, we are overcrowded by medical image data acquired from various sources, such as CT, MR, ultrasound and many others. We usually need to perform segmentation, detection and extraction of objects of interest for further processing. This process includes quantification of parameters to determine a clinical evaluation. There are multiregional segmentation methods that allow for differentiation of individual morphological objects. However, the commonly used hard thresholding approaches lack of robustness in noisy environment leading to an incorrect pixel classification. Image segmentation based on fuzzy set theory brings much more effective alternative for image thresholding gained by local aggregation, making this method more noise resistive. We consciously performed a comparative analysis of articular cartilage and blood vessels segmentation. It was an obvious method utilization in which the native image features are badly recognizable and the objects features are well observed.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Telecommunication, Electronic and Computer Engineering

ISSN

2180-1843

e-ISSN

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Svazek periodika

10

Číslo periodika v rámci svazku

1-8

Stát vydavatele periodika

MY - Malajsie

Počet stran výsledku

5

Strana od-do

109-113

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-85045181709