An Image-based Approach of Apex Type Descriptor State Establishment using Elliptic Fourier Analysis

Abstract

An objective method to establish apex and base type descriptor states is proposed using elliptic Fourier descriptors derived from appropriately segmented images. Using pili (Canarium ovatum Engl.) kernel-in-shell apex as a model, 51 pili accessions represented by 10 kernel-in-shell images per accession were acquired using a calibrated VideometerLab3 setup. An appropriate segmentation scheme was applied by developing an algorithm that would segment blobs that cover only the apical region (upper 25%) of the closed contours using OpenCV implemented in Python 3.7. From the segmented blobs, elliptic Fourier descriptors (EFDs) were derived. Subsequently, the EFDs were analyzed using principal component analysis and cluster analysis. Two effective principal components were able to explain 94.18% of the variation which were visualized to show differences involving length to width ratio, apex angle and left-to-right leaning orientation of the segmented images. Although a single apex shape was observed, mean EFDs of three clusters were able to represent three apex type descriptor states-narrowly acute, acute and widely acute. This indicated that elliptic Fourier analysis can characterize variations in the apical regions, particularly apex shape and angle, given the proper segmentation protocol.