Cancer Detection System Using Multispectral Imaging and Local Phase Quantization
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Solution Overview
Problem
Current cancer diagnostic procedures, particularly for colorectal cancer, are time-consuming and prone to false positives due to the reliance on manual histopathologic analysis by pathologists, which can be inconsistent and labor-intensive, necessitating a more efficient and accurate computer-aided diagnostic system.
Innovation Solution
A cancer detection system combining a microscope with halogen illumination, a multispectral filter, and a camera, connected to a computer that captures and analyzes images across various wavelength bands, including infrared, using local phase quantization features for image processing and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual histopathologic analysis by pathologists is used, then diagnostic accuracy can be maintained through expert judgment, but the procedure becomes time-consuming and prone to human error and inconsistency
Solution Approach 1:
The patent replaces the manual mechanical process of pathologist examination with an automated image processing system using computer algorithms and machine learning models to analyze histopathologic images, thereby eliminating human fatigue and inconsistency while maintaining diagnostic accuracy
Solution Approach 2:
The system creates digital copies of biopsy slides through high-resolution scanning and uses these digital replicas for automated analysis, allowing multiple analyses without consuming the original sample and enabling parallel processing to reduce time
2Productivity
If automated image processing is implemented, then diagnostic speed and consistency are improved, but system complexity and initial setup requirements increase
Solution Approach 1:
The system is designed to handle multiple types of histopathologic images and cancer classifications using a single integrated platform with modular architecture, allowing the same system to serve various diagnostic purposes without requiring separate specialized equipment for each application
Solution Approach 2:
The system incorporates automated quality control algorithms that self-adjust parameters and validate results without requiring constant manual intervention, and includes built-in calibration routines that automatically optimize performance, reducing the need for complex manual configuration and maintenance
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 system enables rapid and reliable detection and classification of cancer cells by automating the analysis of biopsy samples, reducing human error and increasing diagnostic speed, thereby improving the accuracy and efficiency of cancer screening.
Implementation Method 1
a microscope with halogen illumination
Implementation Method 2
a filter coupled to the microscope. The filter is configured to be tuned to a specified individual wavelength to capture an image
Implementation Method 3
a camera attached to the filter. The camera is configured to capture images through the filter
Data Source
AI summary
A cancer cell detection device includes a computer with a database and a display and a microscope coupled to the computer. The microscope has a base upon which a biopsy sample can be placed. The device further includes a camera coupled to the microscope and computer. The camera is configured to capture images of the biopsy sample. The device also has a filter configured to attach to the microscope and a connection feature for connecting the computer to the camera and the filter. The computer further includes a processor that processes the images captured by the camera and classifies the images according to known variables stored in the database.