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Digital Image Processing for Medical Applications

This repository contains assignments and projects related to various aspects of image processing, from basic operations to advanced techniques like active contours. Examples and case studies focus on applications in medical imaging.

Table of Contents

  1. HW0 - Introduction to Image Analysis with Python
  2. HW1 - Introduction to Operations on Images
  3. HW2 - Intensity-based Operations
  4. HW3 - Spatial Operations
  5. HW4 - Frequency Domain Operations
  6. HW5 - Image Restoration and Morphological Image Processing
  7. HW6 - Segmentation and Active Contours

HW0 - Introduction to Image Analysis with Python

In this section, we introduce the basics of Python programming and data visualization, laying the groundwork for advanced image analysis topics.

Topics Covered

pt-1.ipynb

  • Exploring NumPy functionalities
  • Data Types and Memory Management
  • Array Manipulations

pt-2.ipynb

  • Populating Matrixes Based on Defined Rules

pt-3.ipynb

  • 2D Matrix Generation with Circle Pattern
  • Adding Random Noise to Matrix

pt-4.ipynb

  • Data Distribution Visualization
  • Plotting Histograms with Matplotlib

HW1 - Introduction to Operations on Images

In this section, we delve into basic image operations, including transformations and adjustments. The notebooks cover a variety of techniques such as affine transformations, image interpolation, and contrast & brightness adjustments.

Topics Covered

affine_transformations_and_image_interpolation.ipynb

  • Affine Transformations (Rotation, Scaling, Shearing)
  • Downsampling
  • Resampling & Interpolation (Cubic, Linear, Nearest)

contrast_and_brightness_adjustments.ipynb

  • Images Normalization
  • Linear and Non-linear Transformations
  • Adjusted Contrast & Brightness

HW2 - Intensity-based Operations

This part, explores the basics of intensity-based operations for image enhancement. Techniques ranging from contrast stretching and power law transformations to histogram equalization and CLAHE are covered. Each notebook offers a thorough analysis of histogram techniques and their outcomes, providing a complete understanding of the subject.

Topics Covered

contrast_stretching_and_power_law.ipynb

  • Contrast Stretching
  • Power-Law (Gamma) Transformation
  • Different Gamma Value Experimentation
  • Comparison between Contrast Stretching and Power-Law Along

histogram_equalization_and_CLAHE.ipynb

  • Histogram Equalization
  • Contrast Limited Adaptive Histogram Equalization (CLAHE)
  • Analysis of Histogram Techniques and Their Outcomes

HW3 - Spatial Operations

In this part, the focus shifts to spatial filtering techniques that emphasize on specific features in images. We explore various types of filters like mean, median, and Laplacian, along with edge-detection methods such as Sobel operators.

Topics Covered

mean_median_and_laplacian_isotropic_filters.ipynb

  • Spatial Filters (Mean, Median)
  • Image Blurring Techniques
  • Laplacian Isotropic Filter
  • Image Enhancement

laplacian_sharpening_sobely_sobely.ipynb

  • Laplacian Sharpening
  • Sobel Filters (Sobel-X, Sobel-Y)
  • Edge Detection Techniques
  • Image Enhancement

HW4 - Frequency Domain Operations

In this section, we delve into the realm of frequency domain operations, studying the Fourier Transform and its applications in image processing. From basic Fourier Transform techniques to the implementation of various types of filters such as Ideal, Butterworth, and Gaussian, this section provides a comprehensive look into the manipulation of images in the frequency domain.

Topics Covered

fourier_transform_and_band_reject.ipynb

  • Fourier Transform for Image Analysis
  • Band-Reject Filtering
  • Frequency Domain Techniques

low_and_high_ideal_butterworth_guassian_filters.ipynb

  • Fourier Transform & Inverse Fourier Transform
  • Low- and High-Pass Filters (Ideal, Butterworth, Gaussian)

HW5 - Image Restoration and Morphological Image Processing

In this part, we explore various methods for improving image quality and enhancing features through various restoration and morphological techniques. This section covers a range of topics, from eliminating unwanted artifacts to performing operations like dilation and erosion. We explore the fundamentals of these methods, their applications, and their effects on different types of images.

Topics Covered

restoration.ipynb

  • Noise Distribution Analysis
  • Alpha-Trimmed Mean Filtering
  • Inverse Filtering for Image Restoration
  • High- and Low-Pass Butterworth Filters

morphological_operations.ipynb

  • Dilation and Erosion Functions
  • Boundary Identification through Textural Segmentation
  • Morphologic Opening and Closing

HW6 - Segmentation and Active Contours

The final section focuses on the complex realm of image segmentation and contour detection. We employ a range of algorithms and techniques to identify and isolate specific structures within images. From basic circle detection using the Hough transform to sophisticated active contours known as "snakes". These techniques help us to explore how to extract meaningful information from complex visual scenes.

Topics Covered

non_maximum_suppression_and_hysteresis_thresholding.ipynb

  • Sobel and Prewitt Operators
  • Non-Maximum Suppression
  • Hysteresis Thresholding

hough_circle_detection.py

  • Circle Detection using Hough Transform

active_contours_snakes_method.ipynb

  • User Interface for Gathering Initial Contour Points
  • Calculating Equally Spaced 2D Contour Points
  • Snake External and Internal Energy Calculating
  • Contour Evolution

Contour Evolution

To give a visual summary of the exploration into active contours, below is an image illustrating the evolution of a contour after several iterations:

Contour Evolution