X-ray Image Processing

about project

The project focused on the development and verification of an image processing module for an X-ray imaging system. The primary goal was to develop a solution that could seamlessly integrate into a larger X-ray installation while offering advanced tools for adjusting and fine-tuning imaging algorithms. This involved creating an application capable of stitching multiple X-ray images of varying quality into one cohesive and accurate image for a comprehensive patient view

Technologies

C++
OpenCV
C#
WPF
MATLAB
AI Neural Networks
DICOM
Gauss Filter
Fiji Method
Poisson Noise Reduction
FFV1

C++
OpenCV
C#
WPF
MATLAB
AI Neural Networks
DICOM
Gauss Filter
Fiji Method
Poisson Noise Reduction
FFV1

Standarts

Domain knowledge

Tomosynthesis
X-ray Image Processing
AI Image Analysis

Tomosynthesis
X-ray Image Processing
AI Image Analysis

tasks

Create a software solution for processing X-ray images, including stitching and image enhancement, that meets the client's requirements.

Design the solution for smooth integration into a larger X-ray system.

Develop tools within the software to enable easy configuration and fine-tuning of visualization algorithms to ensure high image quality, even with images of different qualities.

Create an intuitive touchpad application that displays the stitched X-ray image and integrates with hospital systems.

Results

SEAMLESS INTEGRATION

Developed and integrated an image processing module into the existing X-ray system, enabling high-quality image processing

ADVANCED VISUALIZATION TOOLS

Provided effective tools for fine-tuning and adjusting visualization algorithms, resulting in improved image quality

ON-TIME DELIVERY

The project was completed within 3 months, meeting all specified requirements, including strict quality and accuracy standards

HIGH-QUALITY APPLICATION

Delivered an application that meets the standards of Class 2B medical devices, allowing easy integration into hospital workflows

process

Requirement Analysis and Gathering:

The project started with a detailed analysis of the client's needs and the challenges they faced with their initial attempt at developing the image processing algorithm. The requirements were gathered through close collaboration with the client, focusing on the functionality needed for seamless integration, image quality, and user experience

Solution Design and Architecture

Based on the gathered requirements, the solution architecture was designed, including the selection of technologies, algorithms, and integration methods. The design phase focused on ensuring the software would be scalable, efficient, and compatible with a Windows-based touchpad interface for hospital use

Development and Implementation

The development phase involved implementing the image stitching algorithms using C++, OpenCV, and MATLAB. The touchpad application was developed in C# and WPF, ensuring it met the usability and performance standards required in a clinical setting

Testing and Quality Assurance

Comprehensive testing was conducted, including functional tests, integration tests, and performance tests. The focus was on ensuring high image accuracy, smooth application operation, and compatibility with varying image qualities

Validation

The final solution underwent validation to ensure it met all medical device regulatory standards (including DICOM) and performed reliably in real-world scenarios. The validation also included client reviews and feedback loops to ensure the solution was fully aligned with their expectations

review

The highly motivated team at Seaberry Technologies demonstrated professionalism, met all our requirements, and consistently delivered high-quality work within the agreed timelines

Avshalom Mizrahi

R&D Director, CMT Medical Technologies, Thales Group

contacts