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

Department of Computer Science, University of Cyprus (UCY), Nicosia, Cyprus.
BioMedical Engineering OnLine (Impact Factor: 1.43). 02/2007; 6(1):44. DOI: 10.1186/1475-925X-6-44
Source: DOAJ


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.
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.
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.
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).

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Available from: Marios S Pattichis, Oct 06, 2015
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    • "The most popular methods according to [10] are based on the polynomial based correction method [11] and neural networks transformation [12]. However, a very limited number of studies used color correction for hysteroscopy or labaroscopy imaging [6]. "
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    ABSTRACT: The aim of this study was to investigate the reliability of two hysteroscopy cameras, the Circon IP 4 and Karl Storz HD, in relation to white balance, camera response over time, and color correction. Experimental results, show that for both the Circon and Karl Storz cameras: (i) for white balancing, either guaze, or white sheet, or the white color cheker can be used, (ii) white balance can be carried out at any distance between 0.5 to 3.0 cm, (iii) white balance can be carried out at 1cm and at any angle between 0 to 45 degrees, (iv) there was no camera color variation over both short (60 min) and long (4 weeks) time intervalls, and (v) color correction algorithm 2 gave better results. Most imortantantly, there was no significant difference between the MSE of the Circon and Storz cameras. The above results will be incorporated in a standardized protocol for texture feature analysis of endoscopic imaging for gyneacological cancer developed by our group, and will enable multi-center quantitative analysis (constrained by the use of the two cameras investigated). KeywordsEndoscopy imaging-laparoscopy imaging-hysteroscopy imaging-white balance-gamma correction algorithm-color systems-CCD medical camera
    XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010; 01/2010
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    • "ROIs of 64x64 pixels were manually cropped and classified into two categories: (i) normal ROIs (N=202) and (ii) abnormal ROIs (N=202) based on the physician's subjective criteria and the histopathological examination (see Fig. 1). All images were color corrected for camera sensor variations based on an extended gamma algorithm [2]. Furthermore, all ROIs were transformed to grey scale, HSV, and YCrCb. "
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    ABSTRACT: The objective of this study was to develop a CAD system for the classification of hysteroscopy images of the endometrium (with suspicious areas of cancer), based on two data mining procedures, the C4.5 and the Hybrid Decision Tree (HDT) algorithms. Twenty-six texture features were extracted from three texture features algorithms: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray level difference statistics (GLDS). A total of 404 ROIs of the endometrium in RGB system format were recorded (202 normal and 202 abnormal) from 40 subjects. Images were gamma corrected and converted to grey scale, and the HSV and YCrCb systems. Results show that abnormal ROIs had lower grey scale median and homogeneity values, and higher entropy and contrast values when compared to the normal ROIs. The maximum average correct classifications score was 72,2% and was achieved using the HDT algorithm using 26 texture features, for the Y channel. Similar performance was achieved with both the HDT and the C4.5 algorithms when trained with the YCrCb texture features. Although similar performance to these models was also achieved when using the SVM and PNN models, the decision tree algorithms investigated, facilitated also the rule extraction, and their use for classification. These models can help the physician especially in the assessment of difficult cases of gynaecological cancer. However, more cases have to be collected and analysed before the proposed CAD system can be exploited in clinical practise.
    4th European Congress of the International Federation for Medical and Biological Engineering, Antwerp, Belgium; 11/2008
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    • "A summary of a standardized protocol for capturing and analyzing endoscopy/laparoscopy/hysteroscopy images is given below [4], [5]: 1. Calibrate the camera following the guidelines by the manufacturer (i.e. white balance). "
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    ABSTRACT: The objective of this study was to develop a CAD system for the classification of hysteroscopy images of the endometrium based on color texture analysis for the early detection of gynaecological cancer. A total of 416 Regions of Interest (ROIs) of the endometrium were extracted (208 normal and 208 abnormal) from 40 subjects. RGB images were gamma corrected and were converted to the HSV and YCrCb color systems. The following texture features were extracted for each channel of the RGB, HSV, and YCrCb systems: (i) Statistical Features, (ii) Spatial Gray Level Dependence Matrices and (iii) Gray Level Difference Statistics. The PNN statistical learning and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using the Wilcoxon Rank Sum Test at a=0.05) between the texture features of normal and abnormal ROIs of the endometrium. Abnormal ROIs had higher gray scale median, variance, entropy and contrast and lower gray scale median and homogeneity values when compared to the normal ROIs. The highest percentage of correct classifications score was 79% and was achieved for the SVM models trained with the SF and GLDS features for differentiating between normal and abnormal ROIs. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue. Further work is needed to validate the system in more cases and organs.
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Lyon, France; 08/2007
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