Biomedical Image Label Detection Using HSV Thresholding
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Solution Overview
Problem
Existing methods for detecting labels in biomedical images, such as edge and shape detection, are computationally complex and time-consuming, making them unsuitable for large numbers of images, especially when sensitive information needs to be scrubbed from digital pathology records.
Innovation Solution
A lightweight algorithm that converts biomedical images from RGB to HSV space, applies a threshold value to identify potential label pixels, and counts contiguous pixels to detect labels, allowing for efficient detection and prevention of sensitive information transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature detection techniques (edge detection or shape detection) are used to detect labels in biomedical images, then label detection accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent transforms the image data from RGB color space to HSV color space, changing the parameter representation of pixel values. This transformation enables the use of simple thresholding on the V (value/brightness) channel instead of complex feature detection, as labels typically have different brightness characteristics than the biological tissue. The parameter change from RGB to HSV space allows for efficient separation of label regions based on intensity differences.
Solution Approach 2:
The patent extracts only the essential characteristic needed for label detection - the brightness/intensity difference between labels and tissue - by using the V-channel from HSV transformation. This extraction approach ignores other complex features like edges and shapes, focusing solely on the intensity parameter that sufficiently distinguishes labels, thereby simplifying the detection process while maintaining effectiveness.
2Reliability
If feature detection techniques are used for label detection, then detection capability is improved, but processing speed decreases making it unsuitable for large numbers of images
Solution Approach 1:
By changing from RGB to HSV color space parameters, the patent enables the use of simple threshold comparison operations instead of computationally intensive feature detection algorithms. This parameter transformation maintains detection reliability while dramatically improving processing speed, making it suitable for batch processing of large numbers of biomedical images.
Solution Approach 2:
The patent uses a simple, computationally inexpensive thresholding operation that can be rapidly applied to each pixel or small region of the image. This lightweight approach processes images quickly without requiring complex algorithms, enabling high-throughput processing of large image datasets while maintaining adequate detection capability.
3Reliability
If manual scrubbing of sensitive information is performed from biomedical images, then data security is improved, but time consumption and labor requirements increase
Solution Approach 1:
The system performs automatic label detection and identification of sensitive information regions without requiring manual review. The algorithm independently processes each image, detects label regions through HSV transformation and thresholding, and identifies sensitive data that needs to be scrubbed, eliminating the need for manual time-consuming review while maintaining security standards.
Solution Approach 2:
The patent performs label detection and sensitive information identification as a preliminary step before image distribution or sharing. By automatically detecting and flagging label regions containing sensitive information in advance, the system prepares images for secure handling without requiring manual scrubbing at the time of sharing, thus saving time while ensuring data security.
Data Source
AI summary
Presented herein are systems and methods for detecting labels in biomedical images. A computing system having one or more processors coupled with memory may identify, from a data source, a biomedical image having a first plurality of pixels in a first color representation. The computing system may convert the first plurality of pixels from the first color representation to a second color representation to generate a second plurality of pixels. The computing system may identify, from the second plurality of pixels, a subset of pixels having a color value satisfying a threshold value. The computing system may detect the biomedical image as having at least one label based at least on a number of pixels in the subset of pixels satisfying a threshold count. The computing system may store, in one or more data structures, an indication for the biomedical image as having the at least one label.