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Title: Classification of histological images of the endometrium using texture features
Authors: Βλαχοκώστα, Αλεξάνδρα Α.
Ασβεστάς, Παντελής Α.
Ματσόπουλος, Γεώργιος Κ.
Κόνδη-Παφίτη, Αγάθη
Βλάχος, Νίκος
Item type: Journal article
Keywords: Neural networks;Endometrium;Νευρωνικό δίκτυο;Ενδομήτριο
Subjects: Medicine
Biomedical engineering
Βιοϊατρική τεχνολογία
Issue Date: 9-Feb-2015
Publisher: Science Printers and Publishers Inc
Abstract: OBJECTIVE: To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium. STUDY DESIGN: A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features. RESULTS: The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%. CONCLUSION: The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.
Language: English
Citation: Vlachokosta, A., Asvestas, P., Matsopoulos, G., Kondi-Pafiti, A. and Vlachos, N., (2013). Classification of histological images of the endometrium using texture features. Analytical and Quantitative Cytopathology and Histopathology. 35(2). Science Printers and Publishers Inc: 2013.
Journal: Analytical and Quantitative Cytopathology and Histopathology
Type of Journal: With a review process (peer review)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
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