Classification and Data Mining for Hysteroscopy Imaging in Gynaecology

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.