Introduction to Image Processing for Biomedical Engineering: Key Points

Introduction to Image Processing in Biomedical ApplicationsIntroduction of the episode 01

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

2D digital images are matrices and are represented as rectangular arrays of square pixels.
In Python to describe the images we use a left-hand coordinate system, with the origin in the upper left corner, the x-axis running to the right, and the y-axis running down. Some learners may prefer to think in terms of counting down rows for the y-axis and across columns for the x-axis. Thus, we will make an effort to allow for both approaches in our lesson presentation.
Most frequently, digital images use an additive RGB model (Red, Green, Blue), with eight bits for the red, green, and blue channels.
skimage images are stored as multi-dimensional NumPy arrays.
Lossless compression retains all the details in an image, but lossy compression results in loss of some of the original image detail.
Some images, depending on the digital sensor (camera), might give textual information called Metadata. we will see then in Medical Images.