A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer

Neofytou, MS; Tanos, V; Pattichis, MS; Pattichis, CS; Kyriacou, EC; Koutsouris, DD

dc.contributor.author	Neofytou, MS	en
dc.contributor.author	Tanos, V	en
dc.contributor.author	Pattichis, MS	en
dc.contributor.author	Pattichis, CS	en
dc.contributor.author	Kyriacou, EC	en
dc.contributor.author	Koutsouris, DD	en
dc.date.accessioned	2014-03-01T01:25:51Z
dc.date.available	2014-03-01T01:25:51Z
dc.date.issued	2007	en
dc.identifier.issn	1475-925X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17766
dc.subject	Color Correction	en
dc.subject	Difference Set	en
dc.subject	Image Acquisition	en
dc.subject	Standardisation	en
dc.subject	Statistical Test	en
dc.subject	Texture Features	en
dc.subject	Tissue Classification	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.other	Gray Level Difference Statistics	en
dc.subject.other	Spatial Gray Level Dependence Matrices	en
dc.subject.other	Texture feature analysis	en
dc.subject.other	Tissue classification methods	en
dc.subject.other	Approximation theory	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image acquisition	en
dc.subject.other	Image analysis	en
dc.subject.other	Optical correlation	en
dc.subject.other	Statistical methods	en
dc.subject.other	Tissue	en
dc.subject.other	Biomedical engineering	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	clinical protocol	en
dc.subject.other	color discrimination	en
dc.subject.other	controlled study	en
dc.subject.other	endometrium cancer	en
dc.subject.other	endoscopy	en
dc.subject.other	female	en
dc.subject.other	gynecologic cancer	en
dc.subject.other	human	en
dc.subject.other	illumination	en
dc.subject.other	image analysis	en
dc.subject.other	image quality	en
dc.subject.other	process optimization	en
dc.subject.other	standardization	en
dc.subject.other	statistical analysis	en
dc.subject.other	statistical significance	en
dc.subject.other	videorecording	en
dc.subject.other	animal	en
dc.subject.other	artifact	en
dc.subject.other	automated pattern recognition	en
dc.subject.other	calibration	en
dc.subject.other	cattle	en
dc.subject.other	chicken	en
dc.subject.other	color	en
dc.subject.other	darkness	en
dc.subject.other	diagnosis, measurement and analysis	en
dc.subject.other	discriminant analysis	en
dc.subject.other	endometrium tumor	en
dc.subject.other	image enhancement	en
dc.subject.other	image subtraction	en
dc.subject.other	methodology	en
dc.subject.other	microscopy	en
dc.subject.other	pathology	en
dc.subject.other	reproducibility	en
dc.subject.other	signal processing	en
dc.subject.other	standard	en
dc.subject.other	Animals	en
dc.subject.other	Artifacts	en
dc.subject.other	Calibration	en
dc.subject.other	Cattle	en
dc.subject.other	Chickens	en
dc.subject.other	Color	en
dc.subject.other	Darkness	en
dc.subject.other	Discriminant Analysis	en
dc.subject.other	Endometrial Neoplasms	en
dc.subject.other	Endoscopy	en
dc.subject.other	Female	en
dc.subject.other	Humans	en
dc.subject.other	Image Enhancement	en
dc.subject.other	Laboratory Techniques and Procedures	en
dc.subject.other	Microscopy, Video	en
dc.subject.other	Pattern Recognition, Automated	en
dc.subject.other	Reference Standards	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Signal Processing, Computer-Assisted	en
dc.subject.other	Subtraction Technique	en
dc.title	A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer	en
heal.type	journalArticle	en
heal.identifier.primary	10.1186/1475-925X-6-44	en
heal.identifier.secondary	http://dx.doi.org/10.1186/1475-925X-6-44	en
heal.identifier.secondary	44	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Background: In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. Methods: We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results: For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. Conclusion: This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal). © 2007 Neofytou et al; licensee BioMed Central Ltd.	en
heal.publisher	BIOMED CENTRAL LTD	en
heal.journalName	BioMedical Engineering Online	en
dc.identifier.doi	10.1186/1475-925X-6-44	en
dc.identifier.isi	ISI:000254002300001	en
dc.identifier.volume	6	en