Color based texture--classification of hysteroscopy images of the endometrium.
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.
Neofytou
M S
MS
Department of Computer Science, University of Cyprus, Nicosia, Cyprus. mneoph@ucy.ac.cy
Tanos
V
V
Pattichis
M S
MS
Pattichis
C S
CS
Kyriacou
E C
EC
Pavlopoulos
S
S
D000465:Algorithms; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D016889:Endometrial Neoplasms / Q000473:pathology*; D005260:Female; D006801:Humans; D015907:Hysteroscopy / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity