Thermal Image Pixel Block Classification for Cancer Detection
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Solution Overview
Problem
Current methods for detecting cancerous tissue in breast cancer screening are limited, especially in remote areas, due to the infrequent availability of screening technologies, necessitating the development of new detection methodologies.
Innovation Solution
A system and method that analyzes blocks of pixels in thermal images of exposed skin tissue using learning matrices derived from temperature vectors to classify tissue as cancerous or non-cancerous, employing a classifier to initiate alerts for cancerous tissue detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional screening methods are used, then detection accuracy may be maintained, but screening frequency is limited due to resource constraints in remote areas
Solution Approach 1:
The patent replaces traditional mechanical/manual screening methods with a thermal imaging-based automated detection system. The system uses thermal cameras to capture heat patterns and employs machine learning algorithms (classifiers trained on matrix data) to automatically identify cancerous tissue, eliminating the need for manual examination while maintaining high detection accuracy.
Solution Approach 2:
The patent creates a digital model (matrix representation) of thermal image data that can be processed and stored efficiently. By converting physical thermal patterns into digital matrix form that can be replicated and analyzed computationally, the system enables frequent screening without requiring physical presence of specialists in remote areas.
2Ease of operation
If thermal imaging with block pixel analysis is implemented, then screening accessibility in remote areas is improved, but system complexity increases due to image processing requirements
Solution Approach 1:
The patent divides the thermal image into discrete blocks of pixels, where each block is independently analyzed by the classifier. This segmentation approach simplifies the processing by breaking down the complex full-image analysis into smaller, manageable units, making the system more computationally efficient and easier to implement in resource-constrained remote environments.
Solution Approach 2:
The patent transforms the thermal image data into a different parameter space by creating matrix representations of temperature values. This parameter transformation converts spatial temperature distributions into algebraic structures that can be efficiently processed by classifiers, reducing computational complexity while preserving diagnostic information.
3Speed
If matrix-based classification is used, then detection speed is improved for frequent screening, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential diagnostic information from thermal images by converting them into compact matrix representations. By taking out only the relevant temperature pattern data and representing it in a standardized matrix form, the system reduces the volume of data that needs to be processed and stored, while maintaining the speed necessary for frequent screening.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables early detection of cancerous tissue, improving treatment likelihood by facilitating frequent screening and addressing the limitations of existing technologies in remote areas through efficient analysis of thermal images.
Implementation Method 1
a thermal image of a subject is received and a region of exposed skin tissue is identified
Data Source
AI summary
What is disclosed is a system and method for the detection of cancerous tissue by analyzing blocks of pixels in a thermal image of a region of exposed skin tissue. In one embodiment, matrices are received which have been derived from vectors of temperature values associated with pixels in blocks of pixels which have been isolated from a plurality of thermal images of both cancerous and non-cancerous tissue. The vectors are rearranged to form matrices. A thermal image of a subject is received. Blocks of pixels which reside within a region of exposed skin tissue are identified and isolated. For each identified pixel block, an image vector comprising temperature values associated with these pixels is formed. The vector is provided to a classifier which uses the matrices to classify tissue associated with this block of pixels as being either cancerous or non-cancerous tissue.