Medical Image Analysis In Python

The field of medical imaging has become very popular in recent years. Therefore, I write book where you will learn the basics of medical image analysis using Python. You will study CT and X-ray scans, segment images, and analyze metadata. Even if you have not used with medical imaging before, you will have all the necessary skills upon completion of the book.

Prerequisites

It assumes you already know how to program in Python. This book is not intended for beginners in Python. Required libraries: matplotlib, numpy, pandas. Knowledge of opencv and skimage would be a plus.

It is also advisable to have experience with the Linux command line.

I wrote the Medical Image Analysis In Python booklet. Now there is an opportunity to purchase it with a $2 discount using the BLOG.

What awaits you in this book?

You will be introduced to medical imaging research. Understand the DICOM and NIfTI-1 Data Format. Learn to analyze meta tags, convert and anonymize data.

Learn what Windowing and Hounsfield units are and how to apply them.

Understand the task of image segmentation. Take a look at third party libraries for medical image segmentation.

Get to know fastai.medical.imaging.

The course is accompanied by several test tasks and a chat in the telegram for discussion.

What exactly will not be in this book?

This course will not include Deep Learning. We will be focusing specifically on the medical imaging format.

I wrote the Medical Image Analysis In Python booklet. Now there is an opportunity to purchase it with a $2 discount using the BLOG.

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