• Trity Course RPi CV Scilab

    Computer Vision with Raspberry Pi & Scilab by Examples

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  • Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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  • Python Data Science

    Python for Data Science Fundamentals

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  • Python for IPCV

    Python for Image Processing and Computer Vision

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

More about Scilab Courses

 

Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

More about CG Courses


Scilab for Medical Image Analysis

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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

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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

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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

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Prerequisites

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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