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Color based texture--classification of hysteroscopy images of the endometrium
- Source :
- Europe PubMed Central, Annual International Conference of the IEEE Engineering in Medicine and Biology-Proceedings, 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
- Publication Year :
- 2007
-
Abstract
- 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: (1) 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. © 2007 IEEE. 864 867 Sponsors: ACIES, Research Promotion and Management Consulting EOARD, European Office of Aerospace R and D Grand Lyon NSF, National Science Foundation Philips Research Europe Philips Research North America Conference code: 70818 Cited By :6
- Subjects :
- Wilcoxon signed-rank test
Image classification
Feature extraction
Color
HSL and HSV
Hysteroscopy
Grayscale
Sensitivity and Specificity
Pattern Recognition, Automated
Endometrium
Image texture
Artificial Intelligence
Image Interpretation, Computer-Assisted
Humans
Computer vision
Mathematics
Gynaecological cancer
Support vector machines
Contextual image classification
business.industry
Reproducibility of Results
Textures
Pattern recognition
Regions of Interest (ROI)
Image Enhancement
Statistical learning
Endometrial Neoplasms
Support vector machine
Hysteroscopy images
RGB color model
Colorimetry
Female
Artificial intelligence
business
Neural networks
Algorithms
Subjects
Details
- ISSN :
- 23757477
- Volume :
- 2007
- Database :
- OpenAIRE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Accession number :
- edsair.doi.dedup.....0e12ecbf926b3b1763c4c3d0ee4c81e5