Medical Image Storage Using Purpose-Driven Segmentation
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Solution Overview
Problem
Current medical image data storage systems face challenges with large data file sizes due to increasing resolutions, leading to high storage requirements and slow transmission times, and existing compression techniques do not provide sufficient compression ratios or rapid compression/decompression, especially when only parts of the image are relevant for diagnosis or treatment.
Innovation Solution
The technique selectively stores medical image data by segmenting regions of interest and decomposing images into multiple resolution levels, where regions of interest are stored at full resolution and others at lower resolution, using integer wavelet decomposition to reduce storage needs and allow for flexible transmission based on bandwidth and viewing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire medical image files are stored and transmitted, then complete image data is available for review, but storage space requirements and transmission time increase significantly
Solution Approach 1:
The patent segments medical image data into multiple resolution levels (e.g., full resolution, half resolution, quarter resolution) and organizes them in a pyramid structure. This allows selective storage and transmission of only the necessary resolution levels based on diagnostic needs, rather than storing complete high-resolution data for all cases.
Solution Approach 2:
The patent extracts and stores only the essential diagnostic information at appropriate resolution levels. By using image pyramids, the system extracts key features and details at different scales, retaining only what is necessary for diagnosis while discarding redundant data.
2Reliability
If entire medical image files are transmitted, then complete image data is available for review, but transmission time increases significantly
Solution Approach 1:
The patent segments medical image data into multiple resolution levels and transmits only the necessary segments based on diagnostic requirements. This segmentation enables selective transmission of appropriate resolution levels, reducing overall transmission time while maintaining diagnostic quality.
Solution Approach 2:
The patent applies partial action by transmitting only the necessary portion of image data at appropriate resolution levels rather than complete high-resolution files. The system transmits exactly what is needed for diagnosis, avoiding unnecessary transmission of excessive data.
3Measurement precision
If image resolution is increased, then diagnostic quality improves, but storage requirements and processing complexity increase
Solution Approach 1:
The patent segments image data into multiple resolution levels, storing high-resolution data only where diagnostically necessary and lower-resolution data elsewhere. This segmentation allows the system to maintain high diagnostic quality in critical areas while reducing overall storage requirements.
Solution Approach 2:
The patent applies local quality by varying the resolution of different regions within the image pyramid structure. Critical diagnostic regions are maintained at high resolution, while non-critical regions use lower resolution, optimizing both diagnostic quality and storage efficiency.
4Quantity of substance
If current compression techniques are applied to entire image files, then storage space is reduced, but compression and decompression speed becomes insufficient for system throughput requirements
Solution Approach 1:
The patent segments image data into resolution levels and applies compression selectively to different segments. This allows the system to compress only the necessary portions of images at appropriate levels, improving both storage efficiency and processing speed by avoiding compression of entire high-resolution files.
Solution Approach 2:
The patent applies partial compression action by compressing only the necessary resolution levels and regions rather than entire high-resolution files. This partial approach achieves sufficient storage reduction while maintaining decompression speeds adequate for system throughput.
Data Source
AI summary
A technique for selecting portions of a multi-resolution medical image data set to be stored and the portions of the multi-resolution medical image data set to be discarded in order to reduce the overall amount of image data that is stored for each image data set. The selection is based on the clinical purpose for obtaining the medical image data. The clinical purpose for obtaining the medical image is used to define regions of interest in the medical image. At each resolution level of the multi-resolution medical image data set, the regions of interest are stored at the full resolution, while the remaining portions of the medical image are stored at a lesser resolution. A three-dimensional bit mask of the regions of interest is produced from a segmentation of the regions of interest. The segmentation list and the multi-resolution medical image data set are decomposed into multiple resolution levels. Each resolution level has a low frequency component and several high frequency components. The low frequency portions at each resolution level may be stored in their entirety. The segmentation list is used to select the regions in the high frequency portions of the multi-resolution image data that correspond to the regions of interest and those regions that do not. The regions in the high frequency portions of the multi-resolution image data that correspond to the region of interest are stored. Those regions in the high frequency portions of the multi-resolution image data that do not correspond to a region of interest are discarded.