Classification of hysteroscopical images using texture and vessel descriptors.
In recent years, hysteroscopy, used as an outpatient office procedure, in combination with endometrial biopsy, has demonstrated its great potential as the method of first choice in the diagnosis of various gynecological abnormalities including abnormal uterine bleeding (AUB) and endometrial cancer (...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 8; pp. 859 - 868 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104081476&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104081476 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2013 vid: 51 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104081476 NLM23504345 2012188594 10.1007/s11517-013-1058-1 NLM23504345 104081476 ppf: 859 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Classification of hysteroscopical images using texture and vessel descriptors. aug: au: Vlachokosta, Alexandra A Asvestas, Pantelis A Gkrozou, Fani Lavasidis, Lazaros Matsopoulos, George K Paschopoulos, Minas affil: School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou str, Zografou Campus, 15780, Athens, Greece, aleka16381@hotmail.com. sug: subj: Endometrium Blood Supply Hysteroscopy Methods Image Processing, Computer Assisted Methods Neural Networks (Computer) Algorithms Cluster Analysis Endometrial Neoplasms Pathology Endometrium Pathology Female Logic Human Sensitivity and Specificity Uterine Hemorrhage Pathology Female ab: In recent years, hysteroscopy, used as an outpatient office procedure, in combination with endometrial biopsy, has demonstrated its great potential as the method of first choice in the diagnosis of various gynecological abnormalities including abnormal uterine bleeding (AUB) and endometrial cancer (CA). In patients suffering with AUB, the blood vessels of the endometrium are hypertrophic, whereas in the case of CA vascularization is irregular or anarchic. In this paper, a methodology for the classification of hysteroscopical images of endometrium using vessel and texture features is presented. A total of 28 patients with abnormal uterine bleeding, 10 patients with endometrial cancer and 39 subjects with no pathological condition were imaged. 16 of the patients with AUB were premenopausal and 12 postmenopausal, all with CA were postmenopausal, and all with no pathological condition were premenopausal. All images were examined for the appearance of endometrial vessels and non-vascular structures. For each image, 167 texture and vessel's features were initially extracted, which were reduced after feature selection in only 4 features. The images were classified into three categories using artificial neural networks and the reported classification accuracy was 91.2 %, while the specificity and sensitivity were 83.8 and 93.6 % respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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