NDLI: Color multiscale texture classification of hysteroscopy images of the endometrium

Content Provider	IEEE Xplore Digital Library
Author	Neofytou, M. S. Tanos, V. Pattichis, M. S. Kyriacou, E. C. Pattichis, C. S. Schizas, C. N.
Copyright Year	2008
Description	Author affiliation: Aretaeion Hospital, Nicosia, Cyprus (Tanos, V.) \|\| Dep. of Computer Science, University of Cyprus, Nicosia, Cyprus (Neofytou, M. S.; Pattichis, C. S.; Schizas, C. N.) \|\| Dep. of Computer Science and Engineering, Frederick University, Nicosia, Cyprus (Kyriacou, E. C.) \|\| Dep. of Electrical and Computer Engineering, University of New Mexico, USA (Pattichis, M. S.)
Abstract	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 1x1 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.
Starting Page	1226
Ending Page	1229
File Size	643297
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	endometrium Hysteroscopy imaging gynaecological cancer texture analysis color multiscale analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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