Introduction to Image Processing in Biomedical Applications

Last updated on 2025-08-26 | Edit this page

Overview

Questions

  • Why is image processing an essential learning topic for a biomedical engineer?
  • Which imaging modalities are used in the biomedical fielsd?
  • How does a researcher/engineer/ clinician know if an image is 2D or 3D?
  • Why is automation in image processing necessary?

Objectives

  • Recognise scientific questions that can be solved with Image processing.
  • Explain why learning about image processing is essential for a biomedical engineer.
  • Identify the 2D and 3D imaging modalities used in the biomedical field.
  • opening and visualizing examples from the 2d imaging general dataset
  • identify/extract the data type, size, dimensions, and compression type from an image

Introduction

This is imported from Carpenries

This is a lesson created via The Carpentries Workbench. It is written in Pandoc-flavored Markdown for static files and R Markdown for dynamic files that can render code into output. Please refer to the Introduction to The Carpentries Workbench for full documentation.

What you need to know is that there are three sections required for a valid Carpentries lesson:

  1. questions are displayed at the beginning of the episode to prime the learner for the content.
  2. objectives are the learning objectives for an episode displayed with the questions.
  3. keypoints are displayed at the end of the episode to reinforce the objectives.

Introduction of the episode 01

Digital imaging plays a pivotal role in modern science, medicine, and everyday life. With the proliferation of digital cameras in mobile devices, individuals now capture and store thousands of images annually, often on personal computers or cloud storage systems. Beyond consumer use, high-precision imaging systems—such as telescopes, medical imaging modalities, and research-grade cameras—are engineered to acquire, store, and digitally disseminate high-fidelity representations of physical subjects, often in real-time with no degradation in quality.

High-resolution digital images are essential for both macroscopic and microscopic investigations. They are routinely used in astrophysics for imaging the universe, in molecular biology for imaging sub-cellular structure, and in medical diagnostics to support research findings, clinical decisions, and technological innovation.

Fundamentals of Digital Image Formation

In all digital imaging modalities, data acquisition involves capturing physical properties of a target using sensors. These measurements are then digitised—converted into numerical representations that can be processed and stored computationally. The nature of the measured properties depends on the specific imaging technique: - Visiblen light reflection — Digital photography - X-ray attenuation — Digital radiography, fluoroscopy, and computed tomography (CT) - Gamma ray radiation emission — Nuclear medicine modalities such as scintigraphy, SPECT, and PET - Acoustic wave reflection and transmission — Ultrasonography Each of these systems captures data in a format amenable to further computational processing, manipulation, and analysis.

Digital Image Representation and Processing

Digital image processing encompasses a range of operations including numerical transformations, filtering, compression, storage, visualisation, and printing. Two-dimensional (2D) images are typically represented as matrices or arrays of pixels (picture elements), where each pixel corresponds to a discrete spatial location in the image and holds one or more numerical values representing intensity or colour. These arrays can vary in size from a few hundred to several million pixels and can be processed using a variety of mathematical operations and algorithms. The field of image processing focuses on these manipulations to enhance image quality, extract meaningful features, or prepare data for further analysis.

Common 2D Image Formats: BMP, PNG, TIFF, JPEG With the growing availability of computational resources, digital image processing has evolved into a key area of research, enabling advanced tasks in image analysis, pattern recognition, and machine learning. It is therefore essential for a biomedical engineer to be familiar with the fundamentals of image processing.

3D Imaging and Medical Applications

Digital images are not limited to two dimensions. Many imaging modalities, particularly in medical and biomedical research, capture three-dimensional (3D) volumes. These volumetric images are composed of voxels (volume elements), each representing a measurement within a spatial 3D grid.

Common 3D Medical Imaging Modalities: - Magnetic Resonance Imaging (MRI) - Computed Tomography (CT) - Positron Emission Tomography (PET) These images are often stored using the DICOM (Digital Imaging and Communications in Medicine) standard, which ensures interoperability across devices, systems, and institutions.

Given the substantial volume of image data generated in research and clinical settings, manual analysis is both time-consuming and prone to error. Automation, utilising software tools and custom algorithms, is becoming increasingly necessary for efficient and accurate image analysis. While many free and open-source image processing software packages exist, researchers, engineers, and clinicians may encounter limitations that require bespoke solutions. Writing custom code—particularly in versatile languages like Python—can provide the necessary flexibility and precision for specific experimental or clinical goals.

About this Notebook Series

This notebook series is designed to introduce researchers, engineers, and students in biomedical fields to fundamental concepts in digital image processing using Python and the scikit-image (skimage) library.

I NEED TO IMPORT THE scikit-image

Throughout this episode series, we will:

Examine the structure of 2D images and how they are represented in code. Explore basic image operations (e.g., filtering, thresholding, segmentation). Apply these methods to high-resolution structural MRI scans of the brain. Learn how to use Python scripts to perform reproducible image analysis. With thoughtful experimental design, Python and scikit-image can become powerful tools for answering diverse biomedical research questions.

Challenge

Challenge 1: Can you do it?

Why a biomedical engineer needs to know about image processing? Which imaging modalities will they encounter in their practice?

R 

paste("This", "new", "lesson", "looks", "good")

OUTPUT 

[1] "Use medical images to answer research/clinical questions and aid medical diagnostics"
[2] "Biomedical images in Microscopy, x-rays, PET, CT MRI ultrasounds etc "
Challenge

Challenge 2: Can you do it?

Which medical images are stored in DICOM ?

OUTPUT 

[1] "MRI"
[2] "CT "
[3] "PET "
Challenge

Challenge 2: how do you nest solutions within challenge blocks?

You can add a line with at least three colons and a solution tag.

Figures

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![optional caption that appears below the figure](figure url){alt='alt text for accessibility purposes'}

Blue Carpentries hex person logo with no text.
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Callout

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They are sometimes used to emphasise particularly important points but are also used in some lessons to present “asides”: content that is not central to the narrative of the lesson, e.g. by providing the answer to a commonly-asked question.

Math

One of our episodes contains \(\LaTeX\) equations when describing how to create dynamic reports with {knitr}, so we now use mathjax to describe this:

$\alpha = \dfrac{1}{(1 - \beta)^2}$ becomes: \(\alpha = \dfrac{1}{(1 - \beta)^2}\)

Cool, right?

Key Points
  • Knowledge of Image processing is essential for a Biomedical Engineer.
  • Familiarity with pragramming and automation in medical image processing is a nessasity.