VascuEye - A Real-Time Blood Flow Monitoring System

About VascuEye

Problem Definition

In microsurgical procedures, especially in oral and maxillofacial surgeries, monitoring blood flow and flap viability is critical. Traditional methods are either invasive, require expensive equipment, or lack real-time monitoring capabilities, increasing the risk of flap failure and complications.

Proposed Solution

VascuEye is a non-invasive, real-time blood flow monitoring system using near-infrared imaging and temperature sensing. It helps surgeons monitor tissue perfusion remotely, using an embedded system connected to the cloud and a mobile interface for continuous observation and timely interventions.

Key Features

📡 Real-Time Monitoring

Continuously tracks blood flow and tissue temperature during surgical procedures using NIR imaging and thermal sensors.

🩺 Non-Invasive

Eliminates the need for invasive equipment, reducing patient risk and improving comfort.

☁️ Cloud Integration

Stores data securely in the cloud for remote access, allowing doctors to monitor flap health anytime, anywhere.

📱 User-Friendly Interface

Intuitive mobile and web applications for doctors and admins to easily monitor and interpret patient data.

High Level Architecture

Components Used

Single Board Computer: Raspberry Pi 3B

Camera: Pi NOIR Camera

IR LEDs: 840nm / 950nm Infrared LEDs

Temperature Sensor: MLX90614

Cloud Platform: AWS (Amazon Web Services)

Software Components

OpenCV

Used for real-time image processing, contrast enhancement, and vein visualization techniques.

Node.js

Handles server-side operations, manages API endpoints, and communicates with the database and cloud.

MongoDB

Stores patient data, temperature records, and processed image metadata in a scalable NoSQL database.

AWS IoT Core

Securely connects the Raspberry Pi hardware to the cloud, enabling real-time data upload and monitoring.

React Native

Develops a cross-platform mobile application for doctors and users to access real-time monitoring data.

Initial Hardware Setup

We initially built the circuit on a breadboard using fifteen 940nm IR LEDs since 850nm variants were not available at the time. To power the LEDs, we used an external power supply and incorporated a 5V voltage regulator to ensure a constant 5V input. Initial testing was performed on a hand, and the output results are shown below.

Hand Test Output

Enhanced Vein Visualization

To improve the clarity of vein structures captured by the camera, we implemented several image enhancement techniques using OpenCV. One of the most effective methods was CLAHE (Contrast Limited Adaptive Histogram Equalization), which helps enhance local contrast in infrared images without amplifying noise.

Additionally, we performed grayscale conversion, followed by contrast stretching and Gaussian blurring to suppress noise and improve edge definition. These preprocessing steps significantly enhanced the visibility of veins, especially under uneven lighting conditions.

This improved visualization played a crucial role in enabling real-time monitoring and assessment of blood flow using our near-infrared imaging system.

Enhanced Image Sample

Project Budget

Item	Quantity	Unit Cost	Total
Temperature Sensor (MLX90614)	1	Rs. 4500.00	Rs. 4500.00
Raspberry Pi 3 Model B	1	Rs. 20400.00	Rs. 20400.00
Raspberry Pi NoIR Camera Sony IMX219	1	Rs. 7200.00	Rs. 7200.00
IR LEDs (850nm)	10	Rs. 200.00	Rs. 2000.00
IR LEDs (940nm)	10	Rs. 200.00	Rs. 2000.00
5 Inch LCD Display	1	Rs. 10500.00	Rs. 10500.00
Wires and Other Electronic Components	-	Rs. 1000.00	Rs. 1000.00
Total Price	-	-	Rs. 47400.00

Meet the Team

Dr. Isuru Nawinne

Project Supervisor

T.L.B Mapagedara

Team Member

J.G.C Jananga

Team Member

R.J Yogesh

Team Member

H.A.M.T Prasadinie

Team Member