DocumentCode :
471780
Title :
Texture-Based Classification of Hysteroscopy Images of the Endometrium
Author :
Neofytou, M.S. ; Pattichis, M.S. ; Pattichis, C.S. ; Tanos, V. ; Kyriacou, E.C. ; Koutsouris, D.D.
Author_Institution :
Dept. of Comput. Sci., Univ. of Cyprus, Nicosia
fYear :
2006
fDate :
Aug. 30 2006-Sept. 3 2006
Firstpage :
3005
Lastpage :
3008
Abstract :
The objective of this study was to classify hysteroscopy images of the endometrium based on texture analysis for the early detection of gynaecological cancer. A total of 418 regions of interest (ROIs) were extracted (209 normal and 209 abnormal) from 40 subjects. Images were gamma corrected and were converted to gray scale. The following texture features were extracted: (i) statistical features, (ii) spatial gray level dependence matrices (SGLDM), and (iii) gray level difference statistics (GLDS). The PNN and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using Wilcoxon rank sum test at a=0.05) between the texture features of normal and abnormal ROIs for both the gamma corrected and uncorrected images. Abnormal ROIs had lower gray scale median and homogeneity values, and higher entropy and contrast values when compared to the normal ROIs. The highest percentage of correct classifications score was 77% and was achieved for the SVM models trained with the SF and GLDS features. Concluding, texture features provide useful information differentiating between normal and abnormal ROIs of the endometrium
Keywords :
biomedical optical imaging; cancer; endoscopes; feature extraction; gynaecology; image classification; image texture; medical image processing; neural nets; probability; statistical analysis; support vector machines; tumours; SVM; Wilcoxon rank sum test; abnormal subjects; endometrium; gray level difference statistics; gray scale; gray scale median; gynaecological cancer detection; homogeneity values; hysteroscopy images; image classification; normal subjects; probabilistic neural network; spatial gray level dependence matrices; statistical features; texture analysis; texture feature extraction; Cancer detection; Feature extraction; Image analysis; Image converters; Image texture analysis; Matrix converters; Neural networks; Statistics; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
ISSN :
1557-170X
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2006.259811
Filename :
4462429
Link To Document :
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