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Scilab for Medical Image Analysis

medicimage main

Analyze Medical Images with Scilab

Analysing medical images with Scilab AIVP module, which is complimentary with attending this training. You will learn how to build the GUI for loading and showing the medical images in this training as well.

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 Exploring Useful Image Processing Tools in the Field of Biomedical Engineering

 Course Synopsis


The course begins with an overview of medical imaging, image modalities, and a review on the basic concepts image processing. The image enhancement and image segmentation are introduced in turn, each offering improvement AND enhancement. The derivation of the main algorithms are covered to enable better understanding and to provide insight on the conceptual ideas behind these algorithms. Application examples are provided at the end of each section to help reconcile theory with actual practice.

This course is conducted in a workshop-like manner, with a balance mix of theory and hands-on coding and simulation in Scilab. Extensive exercises are provided throughout the course to cover every angle of algorithm design and implementation using Scilab.

Course Objectives


This course is intended as a practical introduction to medical image processing techniques. As such, there will be a series of hands-on exercises which are generally aimed to help translate the theoretical models to practical medical applications.

Who Must Attend

Engineer, researchers, scientists from academic, medical and engineering or simply anyone who wants to work on medical image processing



Basic knowledge of computer operation and image processing.

Course Outline

Managing DICOM Images
  • What is medical imaging?
  • Dicom format
Image Modalities
  • Computed Tomography
  • Fundamental of Computed Tomography
  • The Formation of CT Image
  • CT Number of Brain Soft Tissues
  • Digital Imaging and Communication in Medicine
  • CT Image Conversion with DICOM
  • CT Images Presentation
  • Window Setting for Ischaemic Stroke Detection
  • General Measurement of Performance
Contrast Enhancement
  • Mathematical Definitions of Contrast
  • Fundamental of Contrast Enhancement
  • Contrast Enhancement of Medical Images
Histogram Equalisation
  • Conventional Histogram Equalisation
  • Pros and Cons of Conventional Histogram Equalisation
Image Quality Measurement
  • Mean Square Error
  • Peak Signal-to-Noise Ratio
  • The Measure of Image Enhancement
Case Studies
These case studies discuss the basic clinical practice by medical doctors, spatial based transformation, contrast and brightness enhancement, image representation, image quality enhancement.
  • Case Studies 1 : Mycobacterium Tuberculosis imaging
  • Case Studies 2 : Breast cancer detection in MRI
  • Case Studies 3 : Gastrointestinal Endoscopy
  • Case Studies 4 : Brain lesion detection in CT

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