Trichuris Egg Larva Detection via Direction-Dependent Image Features
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Solution Overview
Problem
Current methods for assessing the biological potency of Trichuris spp. eggs, particularly those containing a fully developed larva, are inadequate as they cannot distinguish between eggs with and without larvae based on exterior size and shape, and the larvae may not be fully developed, making it difficult to determine the medicinal potency of helminthic therapy suspensions.
Innovation Solution
A computer vision-based method for extracting features from digital images of Trichuris spp. eggs, which involves detecting and classifying eggs based on the presence of a larva by measuring direction-dependent structures within the egg content region, using techniques such as edge detection and correlation coefficients, to determine the developmental stage and biological potency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exterior size and shape of eggs are used for classification, then different species of helminth eggs can be separated, but developmental stages and biological potency cannot be distinguished
Solution Approach 1:
The egg is segmented into distinct regions: the shell (exterior) and the egg content (interior). The method extracts features from both regions independently. The shell region provides species identification information, while the egg content region provides developmental stage information, thereby resolving the contradiction between species identification and developmental stage detection.
Solution Approach 2:
The method transitions from analyzing only the two-dimensional exterior shape to incorporating three-dimensional internal structure analysis through egg content image regions. By adding the internal dimension (egg contents) to the external dimension (shell shape), the system can simultaneously determine both species and developmental stage without losing either type of information.
2Measurement precision
If manual assessment of egg contents is performed, then developmental stage can be determined, but the process is time-consuming and labor-intensive
Solution Approach 1:
The manual mechanical inspection process is replaced with an automated computer vision system using digital image processing. The system uses algorithms to detect edges, calculate correlation coefficients, and extract features from egg content images, automatically determining developmental stages without human intervention, thereby eliminating time loss while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service assessment where the egg samples themselves provide all necessary information through their image characteristics. The automated analysis extracts developmental stage information directly from the egg content images without requiring external manual evaluation, making the process efficient and scalable.
3Ease of manufacture
If traditional image analysis methods are used, then species separation is achieved, but automated assessment of biological potency is not possible
Solution Approach 1:
The system is designed with multi-functionality to perform both species identification and developmental stage assessment using a unified image processing framework. By extracting features from both shell and egg content regions with the same system, it achieves universal applicability for both classification tasks, thereby improving adaptability while maintaining implementation simplicity through a single integrated solution.
Data Source
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AI summary
There is provided a computer vision based method for extracting features relating to the developmental stages of Trichuris spp. eggs, wherein for the final developmental stages a larva is present inside the egg, said Trichuris spp. eggs having a substantially oblong or elliptical shape with a protruding polar plug at each end, the shape of the Trichuris spp. eggs thereby defining a longitudinal direction and a transverse direction of the eggs. The method comprises a step of obtaining and storing one or more digital images of Trichuris spp. eggs suspended in a liquid solution, a step of detecting one or more Trichuris spp. eggs in the image(s), and a step of extracting one or more features from an egg content image region representing at least part of the egg contents of a detected egg. The extraction of one or more features from the egg content image region may include one or more measurements of the direction-dependent structures of the egg contents, such as one or more measurements of the longitudinal structures of the egg contents and/or one or more measurements of the transverse structures of the egg contents. The Trichuris spp. eggs may be Trichuris suis eggs.