Characterising shape patterns using features derived from best-fitting ellipsoids

Abstract

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 distinguishes 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 (R 2 = 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 irregular in shape to the point where landmark points cannot be identified between samples.

Modelling the Effects of Growth and Remodelling on the Density and Structure of Cancellous Bone

“…Previous work demonstrated that the structure attributes used here were successful in automatically classifying trabecular bone into Sham, OVX, and OVX + Zol groups (Gontar et al. 2018 ; Asiri et al. 2020 ) and so seem to capture key variation in structure between the groups.…”

“…These include fitting ellipsoids to local structure (Gontar et al. 2018 ; Doube 2015 ) and directly measuring local branching characteristics (Asiri et al. 2020 ).…”

“…Based on previous work (Gontar et al. 2018 ; Asiri et al. 2020 ) and some preliminary tests, eight attributes were selected: BS, BS/BV, TBPF, SMI, EUL, ConnD, AveBR and StdBR.…”