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 |
| Sumario: | 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). |
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