Aberrant Pixel Detection and Correction in X-ray Imaging
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Solution Overview
Problem
Aberrant pixels in digital images, such as unusually bright or dark pixels, can confuse users and be misinterpreted as actual image features, particularly in x-ray imaging systems where high-energy x-ray photons can penetrate scintillators, leading to inaccurate representations.
Innovation Solution
An imaging system comprising a detector and an image processor that identifies aberrant pixels by determining a range of expected values based on neighboring pixels and adjusts pixel values to correct aberrant readings, using noise models and comparison methods to distinguish between normal and aberrant pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-energy x-ray photons are used for imaging, then imaging capability is improved, but aberrant pixels are generated due to photon penetration of scintillators
Solution Approach 1:
The patent applies preliminary action by detecting and correcting aberrant pixels before they can mislead image interpretation. The system proactively identifies pixels with values outside the expected range based on neighboring pixel statistics, and replaces them with corrected values derived from local image gradients and neighboring pixel data, preventing potential misdiagnosis.
Solution Approach 2:
The patent uses an intermediary approach by introducing a correction mechanism that acts as a mediator between the raw pixel data and the final image output. The correction process uses neighboring pixels as intermediaries to estimate and replace aberrant pixel values, thereby eliminating the harmful effect of penetration events while preserving the useful imaging information.
2Device complexity
If aberrant pixels are not corrected, then image processing is simple, but image accuracy deteriorates due to false interpretations
Solution Approach 1:
The patent applies self-service by enabling the image data itself to identify and correct its own aberrations. The correction algorithm uses the statistical properties of neighboring pixels and local image gradients to automatically detect and replace aberrant values without requiring external intervention or complex manual processing, thereby maintaining simplicity while improving accuracy.
Solution Approach 2:
The patent uses feedback by continuously comparing each pixel value against the expected range derived from neighboring pixels. When a pixel falls outside this range, the system provides feedback by replacing it with a corrected value based on local image characteristics, creating a self-correcting mechanism that improves image accuracy through iterative refinement.
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
Effectively detects and corrects aberrant pixels, improving image accuracy and reducing false positives, thereby enhancing the reliability of image interpretation by radiologists and other users.
Implementation Method 1
a scintillator which generates visible light when excited by ionizing radiation, such as x-rays (i.e., converts x-ray photons to light)
Data Source
AI summary
Some embodiments include determining a value of an identified pixel of a plurality of pixels of an image from a detector; determining a noise value based on the value of the identified pixel and the detector; determining a range based on the noise value and the value of the identified pixel; comparing the range and a value of at least one pixel of the pixels other than the identified pixel; and adjusting the value of the identified pixel in response to the comparison.