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Built to explore the application of OpenCV and Computer Vision logic on raw medical datasets, moving beyond theory to build a functional diagnostic tool.

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ShreyasLakshmikanth/PyDICOMImage-Processing-Prototype

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Medical Diagnostic Workstation Prototype

⚠️ DISCLAIMER: Educational Portfolio Project

Non-Commercial / No Affiliation This software is a personal technical prototype developed strictly for educational and portfolio purposes. It demonstrates competency in DICOM image processing, statistical anomaly detection, and software architecture.

  • No Affiliation: This project is not an official product of, nor endorsed by, Siemens Healthineers or any specific medical entity.
  • Trademarks: Any references to specific company names or trademarks in the code or history are used solely for descriptive purposes to contextualize the project's intent (Fair Use).

Overview

This software provides an interactive diagnostic interface for viewing medical images (DICOM/MRI) and performing automated anomaly detection using statistical analysis.

It was built to explore the application of OpenCV and Computer Vision logic on raw medical datasets, moving beyond theory to build a functional diagnostic tool.

Project Structure

  • main.py: The primary GUI application. Features Window/Level adjustment (Brightness/Contrast) and AI sensitivity controls.
  • readscandata.py: Utility to inspect DICOM metadata (Patient ID, Modality, Body Part).
  • tests.py: Quality Assurance (QA) suite to verify the mathematical accuracy of the AI.
  • requirements.txt: List of dependencies.
  • core/: Contains the computer vision logic for noise calculation and anomaly detection.
  • hardware/: Drivers for loading .dcm files and simulated X-ray detectors.
  • data/: Storage directory for medical scan files.

Installation & Workstation

  1. Install the required dependencies:
    pip install -r requirements.txt
    
  2. Strart the workstation
    python3 main.py

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Built to explore the application of OpenCV and Computer Vision logic on raw medical datasets, moving beyond theory to build a functional diagnostic tool.

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