Characterising shape patterns using features derived from best-fitting ellipsoids
| Autor: | Benjamin J. Binder, Murk J. Bottema, Hayden Tronnolone, Amelia Gontar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Best fitting 02 engineering and technology Correlation 03 medical and health sciences Pseudohyphal growth Artificial Intelligence Dimorphic yeast 0202 electrical engineering electronic engineering information engineering medicine Marbling in beef Mathematics Landmark business.industry Cancellous bone Pattern recognition Ellipsoid Shape analysis 030104 developmental biology medicine.anatomical_structure Signal Processing Principal component analysis 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software Shape analysis (digital geometry) |
| Zdroj: | IndraStra Global. |
| ISSN: | 2381-3652 |
| Popis: | © 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (June 2018) in accordance with the publisher’s archiving policy A method is developed to characterise highly irregular shape patterns, especially those appearing in biomedical settings. A collection of best-fitting ellipsoids is found using principal component analysis, and features are defined based on these ellipsoids in four different ways. The method is defined in a general setting, but is illustrated using two-dimensional images of dimorphic yeast exhibiting pseudohyphal growth, three-dimensional images of cancellous bone and three-dimensional images of marbling in beef. Classifiers successfully distinguish between the yeast colonies with a mean classification accuracy of 0.843 (SD=0.021), and between cancellous bone from rats in different experimental groups with a mean classification accuracy of 0.745 (SD-0.024). A strong correlation (R2=0.797) is found between marbling ratio and a shape feature. Key aspects of the method are that local shape patterns, including orientation, are learned automatically from the data, and the method applies to objects that are irregular in shape to the point where landmark points cannot be identified between samples. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|