Tissue Image Classification Using Fiducial-Guided Segmentation
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Solution Overview
Problem
Conventional methods for tissue classification in complex biological images require human input, leading to inefficiencies, high labor costs, and susceptibility to errors, making them unsuitable for high-throughput applications.
Innovation Solution
An automated system and method using fiducial markers and heuristic classifiers to distinguish tissue from background in images, followed by a graph cut segmentation algorithm for precise tissue classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods for tissue classification are used, then accuracy can be maintained through human judgment, but productivity is reduced due to manual intervention and high labor costs
Solution Approach 1:
The system enables automated tissue classification where the computational algorithm performs the classification task independently without requiring human intervention. The machine learning model processes images autonomously, making the system self-sufficient for the classification function while maintaining accuracy through trained algorithms.
Solution Approach 2:
The patent replaces the mechanical human visual inspection and manual classification process with an automated computational image processing system. The mechanical action of human eyes and brain processing is substituted with electronic image analysis algorithms that can process multiple images simultaneously, dramatically increasing throughput while maintaining or improving classification accuracy.
2Measurement precision
If manual tissue classification is performed, then complex image analysis can be handled with human expertise, but loss of time occurs due to labor-intensive processes
Solution Approach 1:
The automated system enables continuous processing of tissue images without the interruptions inherent in manual classification. The computational algorithm can process images in a continuous stream, eliminating idle time between manual inspections and allowing parallel processing of multiple images simultaneously, thus reducing total processing time while maintaining precision through consistent algorithmic application.
3Measurement precision
If human input is required for tissue classification, then complex biological images can be interpreted with expert knowledge, but device complexity increases due to manual operation requirements
Solution Approach 1:
The system is designed to be self-sufficient by incorporating all necessary processing capabilities within the automated workflow. The software handles image preprocessing, feature extraction, classification, and output generation automatically, eliminating the need for external human intervention and reducing operational complexity despite the sophisticated algorithms employed.
4Adaptability or versatility
If conventional manual methods are used, then flexibility in handling diverse images can be maintained, but productivity is reduced due to sequential processing
Solution Approach 1:
The automated classification system is designed with universal applicability to handle diverse tissue image types and formats through a single integrated platform. The software can process various image formats, resolutions, and tissue types using the same core algorithmic framework, enabling both flexibility in handling diverse inputs and high throughput through automated parallel processing capabilities.
Data Source
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AI summary
Systems and methods for tissue classification are provided. An image of tissue on a substrate is obtained as a plurality of pixels. Fiducial markers are on the substrate boundary. Pixels are assigned to a first class, indicating tissue sample, or a second class, indicating background. The assigning uses the fiducial markers to define a bounding box within the image and disregards pixels outside the box. Then, heuristic classifiers are applied to the pixels: for each respective pixel in the plurality of pixels, each heuristic classifier votes for the respective pixel between the first and second class, thereby forming an aggregated score for each pixel that in one of first class, likely first class, likely second class, and obvious second class. The aggregated score and intensity of each pixel is applied to a segmentation algorithm to assign a probability to each pixel of being tissue sample or background.