Breast Image Segmentation via Projection Curves and Exposure Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging technologies face challenges in accurately and efficiently segmenting and enhancing breast images, particularly in determining breast regions and adjusting exposure parameters, which affects diagnostic accuracy and patient safety.
Innovation Solution
A system and method that utilize a processor to analyze breast images by determining projection curves, valley points, and peak points to identify breast regions, and adjust exposure parameters based on glandular tissue content, incorporating machine learning for image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used on breast images, then the processing speed is relatively fast, but the segmentation accuracy and diagnostic reliability are insufficient
Solution Approach 1:
The patent segments the breast image processing task into multiple stages: initial segmentation to separate breast from background, projection curve analysis to identify anatomical features (valley points, peak points), and refined segmentation to delineate specific breast regions. This multi-stage segmentation approach improves accuracy while managing system complexity through modular processing steps.
Solution Approach 2:
The patent introduces projection curves as an intermediate representation that transforms 2D image data into 1D curve data with distinct valley and peak points. This dimensional transformation provides additional structural information about breast anatomy, enabling more accurate segmentation without requiring excessively complex 2D processing algorithms.
2Measurement precision
If higher X-ray dose is used to acquire breast images, then the image quality and signal-to-noise ratio improve, but the patient radiation exposure and safety risks increase
Solution Approach 1:
The patent performs preliminary analysis of the breast image including projection curve generation, valley point detection, and peak point identification before final diagnostic interpretation. These preliminary actions extract structural information that can guide optimal exposure parameter selection, enabling quality images at lower doses by预先 understanding the breast anatomy from the acquired image.
Solution Approach 2:
The system uses feedback from image analysis (projection curve characteristics, segmented region properties) to adjust exposure parameters for subsequent imaging. By analyzing features like valley depth, peak height, and region boundaries, the system can determine whether the acquired image quality is sufficient or if re-imaging with adjusted exposure is needed, avoiding unnecessary high-dose exposures.
3Measurement precision
If manual image analysis is performed by radiologists, then the diagnostic accuracy is high, but the processing time and workload are excessive
Solution Approach 1:
The patent implements automated image processing capabilities that perform initial segmentation, projection curve analysis, and feature extraction without radiologist intervention. The system autonomously identifies valley points, peak points, and breast regions, providing pre-processed results that radiologists can review and verify, thereby reducing manual workload while maintaining diagnostic accuracy.
Solution Approach 2:
The patent introduces an automated image processing system as an intermediary between image acquisition and radiologist interpretation. This intermediary performs computationally intensive tasks like projection curve analysis and region segmentation, presenting refined results to radiologists for final diagnosis, thus improving workflow efficiency without compromising diagnostic quality.
Data Source
AI summary
A method may include obtaining an original image. The method may also include determining a plurality of decomposition coefficients of the original image by decomposing the original image. The method may also include determining at least one enhancement coefficient by performing enhancement to at least one of the plurality of decomposition coefficients using a coefficient enhancement model. The method may also include generating an enhanced image corresponding to the original image based on the at least one enhancement coefficient.