Gradient weighted CO-HOG for analysis of caudal vein structural changes in toxin exposed zebrafish embryo

Abstract

In this paper, we propose an approach to detect abnormalities in the vascular development of zebrafish embryos due to exposure to toxins. Morphological changes due to toxin exposure is modeled based on the proposed gradient weighted co-occurrence histogram of oriented gradients (gCo-HOG). These features are compared to more commonly used histogram of oriented gradients (HOG) features and Co-HOG features that utilizes spatial distribution of neighboring pixels to capture spatial structure. The features are used to train a linear SVM classifier to identify morphological changes in a dataset of zebrafish embryo images. Results show that proposed gCo-HOG based technique clearly outperforms HOG and Co-HOG features. The importance of descriptor lies in the fact that features are discriminative enough to capture subtle morphological changes between normal anatomy and toxin induced changes.

Identification of vascular disruptor compounds by analysis in zebrafish embryos and mouse embryonic endothelial cells

“…For the 34 chemicals visually identified as VDCs, automated quantitative image analysis was performed on microscope images of the embryos for the two most commonly affected vascular disruption endpoints: perturbation of ISVs and CVP (Supplementary Figure 1) [37, 38]. The percentage of embryos in a treatment group with perturbed vasculature for either ISVs or CVP was recorded.…”

“…Segmentation and feature extraction algorithms were developed to analyze images of exposed zebrafish embryos for the two most common malformations, ISV morphology (ISV count; average distance between ISV; total area of ISV; and average ISV length) and condensation of CVP (shape quantification of CVP and fenestrations) (Supplementary Figure 1), as have been described previously [37, 38]. Briefly, the algorithms extracts whole zebrafish embryos from images, while excluding other objects, such as the edges of well plates using triangle thresholding [39].…”