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


CONCLUSIONS
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).
Department of Computer Science, University of Cyprus (UCY), Nicosia, Cyprus. mneoph@ucy.ac.cy
Marios S
MS
Neofytou
Vasilis
V
Tanos
Marios S
MS
Pattichis
Constantinos S
CS
Pattichis
Efthyvoulos C
EC
Kyriacou
Dimitris D
DD
Koutsouris
D000818:Animals; D016477:Artifacts; D002138:Calibration; D002417:Cattle; D002645:Chickens; D019411:Clinical Laboratory Techniques; D003116:Color; D003624:Darkness; D016002:Discriminant Analysis; D016889:Endometrial Neoplasms / Q000175:diagnosis / Q000473:pathology; D004724:Endoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement; D018715:Microscopy, Video / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012015:Reference Standards; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique