Automatic Extraction of Appendix from Ultrasonography with Self-Organizing Map and Shape-Brightness Pattern Learning.
Accurate diagnosis of acute appendicitis is a difficult problem in practice especially when the patient is too young or women in pregnancy. In this paper, we propose a fully automatic appendix extractor from ultrasonography by applying a series of image processing algorithms and an unsupervised neur...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 11 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/12/2016
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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=114485467&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114485467 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/12/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 114485467 114485467 114485467 10.1155/2016/5206268 114485467 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Automatic Extraction of Appendix from Ultrasonography with Self-Organizing Map and Shape-Brightness Pattern Learning. aug: au: Kim, Kwang Baek Song, Doo Heon Park, Hyun Jun affil: Department of Computer Engineering, Silla University, Busan 46958, Republic of Korea sug: subj: Appendix Ultrasonography Appendicitis Ultrasonography Algorithms Automation Human Descriptive Statistics Fascia Ultrasonography Software Design Academic Medical Centers South Korea ab: Accurate diagnosis of acute appendicitis is a difficult problem in practice especially when the patient is too young or women in pregnancy. In this paper, we propose a fully automatic appendix extractor from ultrasonography by applying a series of image processing algorithms and an unsupervised neural learning algorithm, self-organizing map. From the suggestions of clinical practitioners, we define four shape patterns of appendix and self-organizing map learns those patterns in pixel clustering phase. In the experiment designed to test the performance for those four frequently found shape patterns, our method is successful in 3 types (1 failure out of 45 cases) but leaves a question for one shape pattern (80% correct). pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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