CPU Image Processing via Optimized Computation Units
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Solution Overview
Problem
The increasing demand for high-resolution medical images poses challenges in efficient processing due to the high computational requirements, which are typically addressed by graphics processing units (GPUs) that occupy space and are costly, necessitating more efficient processing methods.
Innovation Solution
The use of optimized computation units supported by central processing units (CPUs), such as those utilizing instruction sets like AVX, AVX2, SSE, and MMX, to process medical images, potentially allowing for multi-thread processing to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high-resolution medical images are processed using conventional GPUs, then processing speed and accuracy are improved, but device cost and space occupation increase
Solution Approach 1:
The patent creates optimized computation units that replicate and adapt GPU-like processing capabilities within the CPU architecture. By copying essential parallel processing functions into CPU instruction sets (AVX, SSE, MMX), the system achieves GPU-level performance without requiring separate GPU hardware, thereby reducing device complexity and cost while maintaining high processing speed for medical images
Solution Approach 2:
The patent makes the CPU multi-functional by enabling it to perform both traditional sequential processing and parallel image processing operations. Through optimized computation units that support multiple instruction sets, the CPU can handle diverse medical imaging tasks (CT, MRI, PET) with varying resolution requirements, replacing the need for dedicated GPU hardware and reducing overall system complexity
2Measurement precision
If high-resolution medical images are processed, then diagnostic accuracy is improved, but computational requirements and processing difficulty increase
Solution Approach 1:
The patent segments the complex processing of high-resolution medical images into multiple manageable computation units, each handling specific processing tasks. By dividing the image processing workload across multiple optimized CPU cores and instruction sets, the system maintains high diagnostic accuracy while reducing the complexity of individual processing operations and enabling parallel execution
Solution Approach 2:
The patent changes the computational parameters by implementing optimized instruction sets (AVX, SSE, MMX) that are specifically tuned for medical image processing operations. These parameter optimizations enable the CPU to handle high-resolution images efficiently, maintaining diagnostic accuracy while reducing processing difficulty through optimized computational approaches
3Productivity
If conventional image processing methods are used, then implementation is simple, but processing efficiency and speed are insufficient
Solution Approach 1:
The patent performs preliminary action by pre-optimizing computation units with specific instruction sets (AVX, SSE, MMX) before actual image processing. These pre-configured computation units are ready to execute medical image processing tasks efficiently, eliminating the need for complex runtime optimizations and enabling straightforward implementation while achieving high processing efficiency
Data Source
AI summary
A method for processing a medical image is provided. The method may include obtaining the medical image, and processing the medical image using a processing program. The processing program may include one or more optimized computation units. The one or more optimized computation units may be optimized by an instruction set supported by the at least one CPU. The instruction set may be configured to optimize at least one of an operation time of the processing program, a resource of the at least one CPU occupied by the processing program, and a count of instructions included in the processing program.