Medical Image Analysis in Python_468 x 60 copy 2.png

medical.tar.gz

medical

In this course, you’ll learn the basics of medical image analysis using Python. You will learn to display and interpret X-ray and CT scans. This course uses relevant Python libraries and commands on medical images for format conversion, segmentation, and analyzing metadata. You can successfully complete this course with only a basic understanding of Python and no background in medical imaging.

Medical Image Analysis in Python

# Image segmentation basics
**Image segmentation** is the process of partitioning an image into multiple segments or sets of pixels. Image segmentation is typically used to locate images’ objects and boundaries (lines, curves, and so on). More precisely, image segmentation is the process that assigns a label to every pixel in an image. That way, pixels with the same label share specific characteristics. Applications of image segmentation include the following:

- Machine vision
- Medical imaging, including volume-rendered images from computed
tomography and magnetic resonance imaging
    - Locating tumors and other pathologies  
    - Measuring tissue volumes
    - Diagnosis
    - The study of anatomical structure  
    - Surgery planning
    - Virtual surgery simulation
    - Intra-surgery navigation
- Traffic control systems





Learn about the segmentation of a medical image using the Felzenszwalb algorithm.

__default

Segmentation of Medical Images