Medical Image Feature-Point Detection Using Binary Search
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Solution Overview
Problem
Existing image processing methods face inefficiencies in detecting feature points from high-resolution medical images due to increased computational demands.
Innovation Solution
A binary search method is employed to efficiently detect feature points by setting a first point on a medical image and performing a predetermined number of searches based on a second point, using a detection model and prediction model to refine the location of the feature point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point detection is performed on high-resolution medical images, then detection accuracy is improved, but computational load increases
Solution Approach 1:
The image processing is divided into two stages: first processing a low-resolution overview image to obtain a rough feature point location, then processing only a specific region of interest from the high-resolution image based on the rough location. This segmentation approach maintains detection accuracy while significantly reducing the computational load by limiting expensive high-resolution processing to a small region.
Solution Approach 2:
The rough feature point location is determined in advance using a low-resolution image before performing detailed detection on the high-resolution image. This preliminary action guides the subsequent high-resolution processing to focus only on the relevant region, avoiding unnecessary computation across the entire high-resolution image while ensuring accurate feature point detection.
2Measurement precision
If feature point detection is performed on high-resolution medical images, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The processing is segmented into a quick preliminary stage using low-resolution images to identify the rough feature point location, followed by a detailed stage using high-resolution images only for the identified region. This time-efficient segmentation reduces overall processing time while maintaining accurate feature point detection through targeted high-resolution analysis.
Solution Approach 2:
The rough location determination is performed as a preliminary step using computationally inexpensive low-resolution image processing. This preliminary action establishes a search region that guides subsequent high-resolution processing, dramatically reducing the time required for accurate feature point detection by avoiding exhaustive search across the entire high-resolution image.
Data Source
AI summary
An image processing device sets a first point on a medical image including a detection target, and detects a feature point of the detection target from the medical image by performing, a predetermined number of times, a binary search based on the first point and a second point different from the first point on the medical image.