Color multiscale texture classification of hysteroscopy images of the endometrium
AuthorNeofytou, Marios S.
Pattichis, Marios S.
Kyriacou, Efthyvoulos C.
Pattichis, Constantinos S.
Schizas, Christos N.
SourceProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Google Scholar check
MetadataShow full item record
The objective of this study was to investigate the diagnostic performance of a Computer Aided Diagnostic (CAD) system based on color multiscale texture analysis for the classification of hysteroscopy images of the endometrium, in support of 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 45 subjects. RGB images were gamma corrected and were converted to the YCrCb color system. The following texture features were extracted from the Y, Cr and Cb channels: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). The Probabilistic Neural Network (PNN), statistical learning and the Support Vector Machine (SVM) neural network classifiers were also applied for the investigation of classifying normal and abnormal ROIs in different scales. Results showed that the highest percentage of correct classification (%CC) score was 79% and was achieved for the SVM models trained with the SF and GLDS features for the lxl scale. This %CC was higher by only 2% when compared with the CAD system developed, based on the SF and GLDS feature sets computed from the Y channel only. Further increase in scale from 2×2 to 9×9, dropped the %CC in the region of 60% for the SF, SGLDM, and GLDS, feature sets, and their combinations. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue in difficult cases of gynaecological cancer. The proposed system has to be investigated with more cases before it is applied in clinical practise. © 2008 IEEE.
Showing items related by title, author, creator and subject.
Liasis, G.; Pattichis, Constantinos S.; Petroudi, Styliani (2012)Mammographic breast density refers to the prevalence of fibroglandular tissue as it appears on a mammogram. Breast density is not only an important risk for developing breast cancer but can also mask abnormalities. Breast ...
Evaluation of spatial dependence matrices on multiscale instantaneous amplitude for mammogram classification Petroudi, Styliani; Constantinou, Ioannis P.; Pattichis, Marios S.; Tziakouri, Chrysa H.; Tziakouri, Chrysa H.; Pattichis, Constantinos S. (Springer Verlag, 2015)Breast cancer is the most common cancer in women. Mammography is the only breast cancer screening method that has proven to be effective. Mammographic breast density is increasingly assessed towards the development of more ...
Brain white matter lesions classification in multiple sclerosis subjects for the prognosis of future disability Loizou, Christos P.; Kyriacou, Efthyvoulos C.; Seimenis, Ioannis; Pantzaris, Marios C.; Christodoulou, Chris C.; Pattichis, Constantinos S. (2011)This study investigates the application of classification methods for the prognosis of future disability on MRI-detectable brain white matter lesions in subjects diagnosed with clinical isolated syndrome (CIS) of multiple ...