PACS Viewer Lung Orientation via Active Contour Model
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Solution Overview
Problem
Existing methods for determining patient orientation in medical images, such as those from CT, MRI, or radiography, often rely on manual designations or predefined protocols, which can lead to inaccuracies if not properly executed, potentially resulting in incorrect treatment plans.
Innovation Solution
A PACS viewer and associated method that uses processing circuitry to register images onto a template, apply an active contour model, and utilize a support vector machine classifier to reliably identify the left and right lungs, thereby predicting patient orientation without reliance on manual designations or protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual designations (L or R) are placed on or burned into the image, then patient orientation can be identified, but technicians may forget to place or burn the designation, leading to inaccurate orientation identification
Solution Approach 1:
The system automatically identifies patient orientation by analyzing anatomical features in the medical image itself, without requiring external manual designations. The algorithm detects lung fields, heart position, and other anatomical landmarks to determine left-right orientation autonomously, making the system self-sufficient and eliminating reliance on technician actions.
Solution Approach 2:
The manual mechanical process of placing or burning designations is replaced with an automated image processing system that uses computer vision and pattern recognition algorithms to identify patient orientation directly from the image data, substituting human action with computational analysis.
2Measurement precision
If an indication of orientation is inserted into the header based on a predefined protocol, then orientation can be identified, but the patient may not be oriented according to the protocol, resulting in inaccurate orientation indication
Solution Approach 1:
Instead of assuming a predefined protocol and hoping the patient is positioned correctly, the system inverts the approach by analyzing the actual anatomical features in the image to determine orientation. Rather than protocol→orientation, the system uses image analysis→actual orientation, reversing the logical flow to directly observe rather than assume.
Solution Approach 2:
The system changes from using fixed protocol parameters to dynamically analyzing image parameters such as lung field asymmetry, heart position, and anatomical landmark locations to determine orientation. This allows the system to adapt to any patient positioning while maintaining accurate orientation identification.
3Reliability
If automated image analysis is used to identify patient orientation, then reliability and accuracy are improved, but device complexity increases
Solution Approach 1:
The complex image analysis task is segmented into distinct processing stages: pre-processing to enhance image quality, feature detection to identify anatomical landmarks, pattern recognition to analyze spatial relationships, and orientation determination to produce the final result. This modular segmentation manages complexity while maintaining high reliability.
Data Source
AI summary
A PACS viewer and an associated method and computer program product are provided for identifying patient orientation. With respect to a PACS viewer, the PACS viewer includes processing circuitry configured to register an image onto a template. The processing circuitry is also configured to determine a representative seed point within each of the left and right lungs as represented by the image. The processing circuitry is further configured to apply an active contour model to each of the left and right lungs to generate a binary image of the left and right lungs. In this regard, the processing circuitry is configured to apply the active contour model by initializing the active contour model with a mask constructed using the representative seed points. Further, the processing circuitry is configured to detect at least one feature from the binary image to permit identification of the left and right lungs.