Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

Abstract Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing spec...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Arnold, Mirko [verfasserIn] Ghosh, Anarta [verfasserIn] Ameling, Stefan [verfasserIn] Lacey, Gerard [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Colour Channel Endoscopic Image Colour Balance Image Analysis Algorithm Representative Colour

Übergeordnetes Werk:	Enthalten in: EURASIP journal on image and video processing - New York, NY : Hindawi Publishing Corp., 2007, 2010(2010), 1 vom: 13. Dez.
Übergeordnetes Werk:	volume:2010 ; year:2010 ; number:1 ; day:13 ; month:12

Links:	Volltext

DOI / URN:	10.1155/2010/814319

Katalog-ID:	SPR032064853

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spelling	10.1155/2010/814319 doi (DE-627)SPR032064853 (SPR)814319-e DE-627 ger DE-627 rakwb eng 620 004 ASE 53.73 bkl 53.78 bkl 54.74 bkl Arnold, Mirko verfasserin aut Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems. Colour Channel (dpeaa)DE-He213 Endoscopic Image (dpeaa)DE-He213 Colour Balance (dpeaa)DE-He213 Image Analysis Algorithm (dpeaa)DE-He213 Representative Colour (dpeaa)DE-He213 Ghosh, Anarta verfasserin aut Ameling, Stefan verfasserin aut Lacey, Gerard verfasserin aut Enthalten in EURASIP journal on image and video processing New York, NY : Hindawi Publishing Corp., 2007 2010(2010), 1 vom: 13. Dez. (DE-627)525478434 (DE-600)2272982-3 1687-5281 nnns volume:2010 year:2010 number:1 day:13 month:12 https://dx.doi.org/10.1155/2010/814319 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2055 GBV_ILN_2065 GBV_ILN_2108 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE 53.78 ASE 54.74 ASE AR 2010 2010 1 13 12
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language	English
source	Enthalten in EURASIP journal on image and video processing 2010(2010), 1 vom: 13. Dez. volume:2010 year:2010 number:1 day:13 month:12
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topic_facet	Colour Channel Endoscopic Image Colour Balance Image Analysis Algorithm Representative Colour
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container_title	EURASIP journal on image and video processing
authorswithroles_txt_mv	Arnold, Mirko @@aut@@ Ghosh, Anarta @@aut@@ Ameling, Stefan @@aut@@ Lacey, Gerard @@aut@@
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title	Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging
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title_sort	automatic segmentation and inpainting of specular highlights for endoscopic imaging
title_auth	Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging
abstract	Abstract Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.
abstractGer	Abstract Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.
abstract_unstemmed	Abstract Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.
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title_short	Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging
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Automatic Segmentation and Inpainting of Specular Highlights for Endoscopic Imaging

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