Medical Image Noise Estimation via Difference Standard Deviation
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Solution Overview
Problem
Operators face difficulties in setting a region of interest in medical image data with varying pixel values, especially in areas with fine structures, making it challenging to accurately determine noise levels in medical images generated by imaging apparatuses like X-ray CT and MRI machines.
Innovation Solution
A medical image processing apparatus that calculates differences between multiple image data sets, removes structural regions, and estimates noise levels using standard deviation analysis, allowing for noise level determination without manually setting a region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the operator manually sets a region of interest in medical image data, then the noise level can be determined, but the operation becomes complicated and it is difficult to secure sufficient data in regions with fine structures
Solution Approach 1:
The system automatically determines the region of interest by calculating the standard deviation of pixel values across the entire image and identifying regions with low standard deviation, eliminating the need for manual operator intervention. The apparatus performs self-service by autonomously selecting appropriate regions for noise level measurement without requiring operator input or manual region setting.
2Quantity of substance
If the operator sets the region of interest in a region with relatively small difference in pixel values, then sufficient data can be secured to obtain the noise level, but it is difficult to do so in regions with many fine structures where pixel values vary
Solution Approach 1:
The system evaluates the entire image space by calculating standard deviation across all pixels rather than relying on manually selected local regions. This dimensional approach allows the system to identify suitable regions objectively based on statistical properties, ensuring both sufficient data quantity and measurement precision without being constrained by fine structural variations in specific areas.
3Measurement precision
If manual region of interest setting is required, then noise level can be obtained, but the process complexity increases and time consumption increases
Solution Approach 1:
The system performs preliminary calculation of standard deviation for all pixels in the image before determining the noise level. This preliminary statistical evaluation automatically identifies suitable regions for noise measurement, eliminating the need for subsequent manual region setting and directly providing the noise level measurement, thereby saving time while maintaining measurement capability.
Data Source
AI summary
A medical image processing apparatus includes a difference calculator, a removal part, a first statistical processing part, and an estimation part. The difference calculator receives a plurality of medical image data with different imaging positions and obtains the difference between the plurality of medical image data, thereby generating difference image data that represents the difference. The removal part removes the region corresponding to a structure from the difference image data. The first statistical processing part obtains the first standard deviation of pixel values of each pixel of the difference image data with the region corresponding to the structure removed. The estimation part estimates the second standard deviation of the medical image data based on the first standard deviation. The medical image processing apparatus can estimate noise level of medical image data.