Python for Medical Imaging for Beginners

In this lecture the students will have an introduction of medical imaging and modality.

Lecture 3 focusses on image formats such as bmp, png and jpeg that are commonly used in medical imaging.

Lecture 4 describes the DICOM file structure that includes the header and the pixel data.

Lecture 5 focusses on the NifTI file format which is designed for neuroimaging data.

Lecture 6 describes the TIFF image format that includes single and multi-page image format. It also deals with header details of the TIFF file.

This lecture gives the introduction of this section. It highlights the importance of choosing python for medical image processing and the python libraries that support different types of medical images learnt in Section 2.

Lecture 9 focusses on Matplotlib which is a powerful Python library for creating static, interactive, and dynamic visualizations.

Lecture 10 focusses on OpenCV which is an open-source computer vision library that provides tools for image and video processing, real-time object detection, and feature extraction.

Lecture 11 describes Image IO that helps to the process reading, writing, and handling image files in various formats. This lectures focusses on normal and TIFF file.

Lecture 12 focusses on the ImageIO functions to handle 2D and 3D DICOM Images.

Lecture 13 focusses on the ImageIO functions to handle DICOM data elements of the header.

Lecture 14 describes Pillow which is a Python Imaging Library (PIL) fork that adds image processing capabilities to Python. It supports opening, manipulating, and saving various image formats, providing tools for tasks like resizing, cropping, rotating, and enhancing images. It’s widely used for handling and processing image data in Python projects.

Lecture 15 focusses on Pydicom which is a Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. It allows you to read, write, and modify DICOM files, which are commonly used for storing medical images such as X-rays and MRIs. Pydicom makes it easy to access and manipulate metadata and pixel data in DICOM files, enabling seamless integration with medical image processing workflows. Part 1 describes the PyDICOM functions for handling DICOM header elements.

Lecture 16 describes the PyDICOM functions for handling DICOM image i.e., pixel data.

Lecture 17 focusses on Nibabel which is a Python library used for reading and writing neuroimaging data, particularly in formats like NIfTI, Analyze, and others. It provides tools for handling 3D and 4D brain imaging data, making it essential for research and analysis in neuroscience and medical imaging, such as working with MRI and fMRI scans.

Lecture 18 focusses on Tifffile which is a Python library for reading and writing TIFF (Tagged Image File Format) files. It supports a wide range of TIFF file variations, including multi-page and multi-dimensional images, making it ideal for handling high-resolution images, medical imaging data, and scientific data in the TIFF format.

Lecture 19 focusses on Vedo which is a Python library for 3D visualization and analysis of scientific data. It simplifies the process of creating interactive 3D plots, visualizing meshes, point clouds, and volumetric data, and handling tasks like 3D rendering and animation, making it ideal for applications in fields like medical imaging, computational geometry, and scientific research.

Lecture 20 focusses on Image Enhancement that involves techniques to improve the visual quality of digital images by adjusting aspects such as brightness, contrast, sharpness, and noise reduction, making them more suitable for analysis or display.

In medical imaging, contrast and brightness adjustments enhance tissue visibility and highlight differences, improving the clarity and accuracy of images for better diagnosis. Lecture 21 deals with how to adjust these parameters for an image with python.

In medical imaging, smoothing reduces noise and sharpens details by averaging pixel values, helping to clarify images and improve the detection of structures or abnormalities without losing important information. Lecture 22 deals with how smoothing can be performed on an image with python.

In medical imaging, sharpening improves image clarity by enhancing edges and fine details, making it easier to identify subtle structures or abnormalities, which aids in more accurate diagnosis and evaluation. Lecture 23 focusses on sharpening an image with python.

Lecture 24 deals with image transformation that includes linear, logarithmic and power law (gamma) transformations with python.