Computer Vision Basics

This course is designed to provide a solid foundation in computer vision techniques and their applications in the Healthcare & Life Sciences industries. Participants will learn the fundamental concepts and algorithms used in image processing, object detection, and image segmentation. The course covers various computer vision libraries and tools, enabling participants to develop practical skills in analyzing and interpreting visual data relevant to the healthcare and life sciences domains, such as medical imaging, microscopy, and digital pathology.

• Understand the fundamental concepts and techniques in computer vision

• Implement basic image processing operations, such as filtering, edge detection, and morphological transformations

• Utilize computer vision libraries, such as OpenCV and scikit-image, for efficient image processing and analysis

• Develop computer vision applications for various Healthcare & Life Sciences use cases, such as medical image analysis and cell segmentation

1. Introduction to Computer Vision

• Overview of computer vision and its applications in the Healthcare & Life Sciences industries

• Digital image fundamentals (pixels, color spaces, image formats)

• Image processing basics (reading, writing, and displaying images)

• Hands-on exercises: Basic image manipulation using Python and OpenCV

2. Image Processing Techniques

• Image filtering (smoothing, sharpening, and noise reduction)

• Edge detection (Sobel, Canny, and Laplacian operators)

• Morphological transformations (erosion, dilation, opening, and closing)

• Hands-on exercises: Implementing image processing techniques on medical and biological images

3. Object Detection

• Template matching and its limitations

• Feature-based object detection (SIFT, SURF, and ORB)

• Cascade classifiers and Haar-like features for detecting anatomical structures

• Deep learning-based object detection (YOLO, SSD, and Faster R-CNN) for medical applications

• Hands-on exercises: Detecting objects of interest in medical images and microscopy data

4. Image Segmentation

• Thresholding techniques (global, adaptive, and Otsu's method)

• Region-based segmentation (region growing and split-and-merge)

• Edge-based segmentation (watershed and graph-based methods)

• Semantic segmentation using deep learning (FCN, U-Net, and DeepLab) for medical image segmentation

• Hands-on exercises: Segmenting cells, tissues, and organs in medical and biological images

5. Applications and Advanced Topics

• Case studies of computer vision applications in the Healthcare & Life Sciences industries (e.g., disease diagnosis, drug discovery, precision medicine)

• Introduction to 3D medical imaging and volume rendering

• Challenges and future directions in computer vision for healthcare and life sciences

• Hands-on exercises: Developing a computer vision application for a specific Healthcare or Life Sciences use case

• Strong understanding of linear algebra and calculus

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques