Introduction to Image Processing, Segmentation, and Deep Learning

Author

Published

March 6, 2026

Welcome

This textbook provides a comprehensive introduction to image segmentation, deep learning, and quantitative analysis, and deep learning through the lens of tensor operations and NumPy.

About This Book

Modern medical imaging and quantitative analysis require a solid foundation in multi-dimensional data structures. This book systematically introduces tensors—from one-dimensional vectors through four-dimensional image batches—while building practical skills in image processing and machine learning. The course was designed to be accessible to students with a basic understanding of Python, and it emphasizes hands-on learning through code examples and exercises.

AI Campus

This book is part of the AI Campus initiative, which aims to provide accessible, high-quality educational resources in artificial intelligence and data science. By following this textbook, you’ll gain the skills needed to analyze and process complex image data, preparing you for advanced studies or careers in AI and machine learning. The AI Campus initiative also offers a variety of courses, workshops, and resources to support your learning journey in artificial intelligence and data science. You can explore more about AI Campus and its offerings at AI Campus.

What You’ll Learn

Tensor fundamentals: Understanding 1D, 2D, 3D, and 4D arrays
Data visualization: Choosing and creating effective plots
Image processing: Interpolation, color mapping, and transformations
Practical applications: Medical image segmentation and quantitative analysis

Prerequisites

Basic Python programming
Familiarity with NumPy (helpful but not required)
Interest in image analysis or machine learning

How to Use This Book

Each chapter builds on previous concepts:

Introduction: Overview of the book’s aims, structure, and the Urothelial Cell Segmentation Challenge.
Chapter 1: Introduction to Tensors (1D, 2D, 3D)
Chapter 2: 4D Tensors, Image Interpolation, and Color Maps
Chapter 3: Morphological Processing and Segmentation
Chapter 4: Building an image preprocessing pipeline
Chapter 5: Connected Components and Partitioning Databases
Chapter 6: Bayesian Optimization of Segmentation Thresholds
Chapter 7: Feature Engineering (UNDER CONSTRUCTION)
Chapter 8: Machine Learning Approaches to Segmentation (UNDER CONSTRUCTION)
Chapter 9: Introduction to Neural Networks (UNDER CONSTRUCTION)
Chapter 10: Convolutional Neural Networks for Image Segmentation (UNDER CONSTRUCTION)
Appendix A: Fundamentals of Python and NumPy
Appendix B: Introduction to Bayesian Optimization

All code examples are provided in full and can be run in Google Colab or Jupyter notebooks.

Saving Your Progress on the Exercises

If you’d like your progress in the course exercises to be tracked, please email me at sargsye2 at the domain lamission.edu and I’ll provide you with an account. You can also enroll at the VOC Ed course offered at Los Angeles Mission College. Of course, you can also follow along with the code examples in this book without enrolling.

Copyright and License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

You are free to share (copy and redistribute) this material in any medium or format for non-commercial purposes, provided you give appropriate credit, provide a link to the license, and indicate if changes were made.

Citation: > Sargsyan, E. (2026). Introduction to Image Processing, Segmentation, and Deep Learning. Licensed under CC BY-NC-ND 4.0.

For full license details, visit: https://creativecommons.org/licenses/by-nc-nd/4.0/