Multispectral Tissue Classification Using Reference Profile Distributions
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Solution Overview
Problem
Existing methods for distinguishing between different types of organic tissue based on their electromagnetic spectrum are prone to errors due to tissue inhomogeneity, variability, and external conditions, particularly in pathophysiological changes, leading to inaccurate classification.
Innovation Solution
A medical device with a multispectral image sensor arrangement that detects characteristic distributions of multispectral intensity profiles for reference tissue sections and tissue regions, using a computing device to assign tissue sections to reference types, and a display device to indicate the assignments, allowing for improved accuracy and flexibility in external conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If spectral angle mapper is used to classify tissue sections, then classification can be performed, but accuracy deteriorates due to tissue inhomogeneity and variability
Solution Approach 1:
The tissue section is divided into multiple pixel elements, and each pixel's intensity spectrum is individually compared against reference spectra. This segmentation allows the system to handle tissue inhomogeneity by evaluating each local region separately rather than treating the entire section as uniform.
Solution Approach 2:
The patent applies a liberal classification criterion where a pixel is classified as matching a reference tissue type if its intensity spectrum falls within a predetermined angular range (e.g., 20 degrees) of the reference spectrum. This partial matching approach reduces false negatives by accepting approximate matches rather than requiring exact spectral alignment.
2Adaptability or versatility
If reference spectra are recorded under varying external conditions, then flexibility is improved, but classification accuracy deteriorates due to condition variability
Solution Approach 1:
The system transforms the spectral comparison from absolute intensity matching to angular distance measurement in spectral space. By calculating the angle between the measured pixel spectrum and reference spectrum vectors, the classification becomes invariant to multiplicative factors such as lighting intensity changes, allowing reference spectra recorded under different external conditions to remain valid.
Solution Approach 2:
Reference tissue sections are pre-recorded and stored in a database before actual classification tasks. These reference spectra serve as templates that can be reused across multiple examinations, eliminating the need to re-record references under each new external condition while maintaining classification accuracy through the angular comparison method.
3Productivity
If spectral measurements are performed in complex environments, then real-time examination is enabled, but reliability deteriorates due to stray light and reflections
Solution Approach 1:
The classification metric is changed from absolute spectral matching to angular distance in parameter space. This transformation makes the measurement robust against additive noise and stray light, as the angular relationship between spectra remains relatively stable even when external light conditions vary during real-time examination.
Solution Approach 2:
The system provides immediate visual feedback by displaying classified tissue sections in real-time during the examination. This allows the operator to verify classifications on the spot and adjust the examination process if needed, improving reliability through operator-in-the-loop validation while maintaining real-time productivity.
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
The method and device provide significantly improved accuracy in tissue classification by accounting for tissue fluctuations and external conditions, enabling intuitive handling and real-time applications with reduced sensitivity to stray light effects.
Implementation Method 1
a multispectral image sensor arrangement which is designed: to detect at least one respective characteristic distribution of multispectral intensity profiles, CDMI, for each reference tissue section defined
Data Source
AI summary
The invention relates to a medical device (100) and to a method for examining an organic tissue. According to the invention, a reference tissue section (11) is defined, thereby simultaneously defining a reference tissue type. For this reference tissue section (11) a characteristic distribution of multispectral intensity profiles is detected. For a tissue region to be examined multispectral intensity profiles are detected. Tissue sections of the tissue region of interest are associated with the reference tissue types on the basis of the intensity profiles and the characteristic distributions, for example on the basis of a similarity.