Segmentation of macular lesions using active shape contour method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099576" target="_blank" >RIV/61989100:27240/16:86099576 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-27644-1_20" target="_blank" >http://dx.doi.org/10.1007/978-3-319-27644-1_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-27644-1_20" target="_blank" >10.1007/978-3-319-27644-1_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Segmentation of macular lesions using active shape contour method

Popis výsledku v původním jazyce

Age-related macular degeneration (ARMD) is one of the most widespread diseases of the eye fundus and is the most common cause of vision loss for those over the age of 60. There are several ways to diagnose ARMD. One of them is the Fundus Autofluorescence (FAF) method, and is one of the modalities of Heidelberg Engineering diagnostic devices. The BluePeakTM modality utilizes the fluorescence of lipofuscin (a pigment in the affected cells) to display the extent of the disease's progression. The native image is further evaluated to more precisely specify the diagnosis of the disease-it is necessary to determine the size of the macular lesion area. Calculations of the geometric parameters of macular lesions were conducted in the MATLAB(R) software; the size of the lesion area was determined using the Image Processing Toolbox. The automated lesion detection method occurs using a parametric active contour (active contours driven by local Gaussian distribution fitting energy) that encloses the affected macular lesion, thereby allowing a precise determination of the affected area. This method is relatively quick for use in clinical practice and allows evaluation the macular lesions exactly based on the proportion with the feature extraction in advance. The proposed methodology is fully automatic. In the algorithm input we define area of interest and initial circle, which is placed inside of the object. Image background issuppressed by low pass filter. Final contour is formed in consecutive steps, up to shape of macular lesion. (C) Springer International Publishing Switzerland 2016.

Název v anglickém jazyce

Segmentation of macular lesions using active shape contour method

Popis výsledku anglicky

Age-related macular degeneration (ARMD) is one of the most widespread diseases of the eye fundus and is the most common cause of vision loss for those over the age of 60. There are several ways to diagnose ARMD. One of them is the Fundus Autofluorescence (FAF) method, and is one of the modalities of Heidelberg Engineering diagnostic devices. The BluePeakTM modality utilizes the fluorescence of lipofuscin (a pigment in the affected cells) to display the extent of the disease's progression. The native image is further evaluated to more precisely specify the diagnosis of the disease-it is necessary to determine the size of the macular lesion area. Calculations of the geometric parameters of macular lesions were conducted in the MATLAB(R) software; the size of the lesion area was determined using the Image Processing Toolbox. The automated lesion detection method occurs using a parametric active contour (active contours driven by local Gaussian distribution fitting energy) that encloses the affected macular lesion, thereby allowing a precise determination of the affected area. This method is relatively quick for use in clinical practice and allows evaluation the macular lesions exactly based on the proportion with the feature extraction in advance. The proposed methodology is fully automatic. In the algorithm input we define area of interest and initial circle, which is placed inside of the object. Image background issuppressed by low pass filter. Final contour is formed in consecutive steps, up to shape of macular lesion. (C) Springer International Publishing Switzerland 2016.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název periodika

Advances in Intelligent Systems and Computing. Volume 423

ISSN

2194-5357

e-ISSN

—

Svazek periodika

423

Číslo periodika v rámci svazku

2016

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

213-221

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84958964415