Radiographic Image Noise Index via Density Histogram Analysis
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Solution Overview
Problem
Existing radiographic imaging systems face challenges in calculating an appropriate exposure index due to the high computational load required for spatial frequency analysis, which often results in inefficient processing and suboptimal image quality due to scattered radiation effects.
Innovation Solution
An image processing apparatus that calculates the exposure index by creating a density histogram, extracting pixels within a specific range, analyzing signal value variations, and using these analyses to determine the exposure index, reducing the need for complex spatial frequency processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatial frequency analysis including Fourier transform processing is used to calculate the exposure index, then the measurement precision of noise evaluation is improved, but the device complexity and computing power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential statistical features (mean and standard deviation) from the pixel signal values within the region of interest, rather than performing complete spatial frequency analysis. This extraction approach maintains sufficient noise evaluation capability while dramatically reducing computational complexity by focusing only on the most relevant statistical parameters.
Solution Approach 2:
The patent uses simple statistical calculations (mean and standard deviation) as a computationally inexpensive alternative to complex Fourier transform processing. These simple calculations provide sufficient information for noise evaluation without requiring high-performance computing resources, making the system accessible to devices with limited processing power.
2Measurement precision
If spatial frequency analysis is performed to calculate the exposure index, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
The patent extracts only the essential statistical features (mean and standard deviation) from the pixel signal values, performing calculations that can be completed in real-time during image acquisition. This extraction approach maintains sufficient exposure index accuracy while enabling immediate feedback for imaging condition adjustment without time-consuming Fourier transform processing.
3Ease of operation
If the conventional exposure index is used to manage imaging conditions, then the radiation dose management is simplified, but the image quality cannot be optimized due to scattered radiation effects
Solution Approach 1:
The patent introduces a noise evaluation mechanism based on standard deviation calculation as an intermediary between the conventional exposure index and image quality assessment. This intermediary provides scattered radiation information that bridges the gap between simple dose management and complex image quality optimization, enabling better imaging condition control without requiring complex processing.
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 allows for a simpler and more efficient calculation of the exposure index, enabling real-time adjustment of imaging conditions and improving image quality by considering noise and scattered radiation effects, even with lower processing power devices.
Implementation Method 1
a detection sensor that detects radiation and performs conversion into an electrical signal
Data Source
AI summary
An image processing apparatus includes a first hardware processor that calculates an exposure index related to noise of a radiographic image based on image data of the radiographic image. The first hardware processor creates a density histogram of a region of interest set in the entire radiographic image or a part of the radiographic image, extracts a plurality of pixels having signal values within a specific range from the created density histogram, analyzes variations in the signal values of the plurality of extracted pixels, and calculates the exposure index based on an analysis result of the variations.