Neural Network X-Ray Image Pixel Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional X-ray detection systems rely on manual analysis by trained operators, which is time-consuming and prone to human error, leading to false positive or false negative readings, and do not automatically produce data for improving future processes.
Innovation Solution
A system using a probabilistic analysis technique that inputs raw x-ray images into a neural network, such as a convolutional neural network (CNN), to classify each pixel based on probability values, thereby automatically detecting objects of interest in real-time or near real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis by trained operators is used, then detection accuracy can be maintained through human judgment, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent replaces the mechanical system of manual human analysis with an automated computer-based image analysis system that processes X-ray images algorithmically. This substitution eliminates human factors causing delays and errors while maintaining or improving detection accuracy through consistent automated evaluation of image features.
Solution Approach 2:
The system enables self-service detection by automatically analyzing images without requiring continuous human intervention. The automated analysis engine independently evaluates images, identifies objects of interest, and generates results, making the detection process self-sufficient and eliminating dependency on operator availability.
2Adaptability or versatility
If manual review processes are used, then human operators can interpret complex image data, but the system cannot automatically produce data for improving future processes
Solution Approach 1:
The system implements feedback mechanisms where detection results and image data are automatically processed to generate insights for improving future detection processes. The automated system learns from analyzed images and adjusts its evaluation criteria, creating a continuous improvement loop that enhances detection capabilities over time without manual intervention.
Solution Approach 2:
The system performs preliminary automated analysis and data generation before human review is needed. By pre-processing images and generating detection data automatically, the system prepares information in advance that can be used to improve future detection processes, reducing the need for manual data preparation.
3Ease of manufacture
If conventional detection methods are used, then simple material identification is possible, but shape and visual details containing relevant information are not utilized
Solution Approach 1:
The system segments the image analysis process into multiple components: basic material identification, shape analysis, and visual detail extraction. Each segment processes specific features independently and combines results, allowing the system to maintain simplicity in individual tasks while comprehensively utilizing all visual information including shape and detailed characteristics.
4Productivity
If automated neural network analysis is implemented, then real-time detection is achieved, but the system requires processing of raw image data through complex algorithms
Solution Approach 1:
The system performs preliminary processing of X-ray images to prepare data in an optimal format for neural network analysis. By pre-processing images to enhance relevant features and reduce noise before neural network input, the system accelerates the detection process while managing algorithmic complexity through optimized data preparation.
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 system enables efficient and accurate automatic detection of objects of interest, reducing human error and providing real-time data for improving detection processes, while also allowing for the classification of pixels into threat or non-threat categories.
Implementation Method 1
emitting an incident x-ray radiation beam through a scanning volume having an object therein
Implementation Method 2
detecting x-ray signals transmitted through at least one of the scanning volume and the object
Data Source
AI summary
A method for detecting at least one object of interest in at least one raw data x-ray image includes the steps of emitting an incident x-ray radiation beam through a scanning volume having an object therein, detecting x-ray signals transmitted through at least one of the scanning volume and the object, deriving the at least one raw data x-ray image from the detected x-ray signals, inputting the raw data x-ray image, expressed according to an attenuation scale, into a neural network, for each pixel in the raw data x-ray image, outputting from the neural network a probability value assigned to that pixel, and, classifying each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.