BIO-695: Image Processing for Life Science

Lectures in this course (51)

Covers resizing, interpolation, and basic image math operations with practical examples.

Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.

Covers mathematical operations in image processing, including normalization, intensity flattening, and image-to-image operations.

Covers image scaling, interpolation, artifacts, pixel sizes, and bit depth.

Covers the fundamental principles of image filtering and its applications in bio-medical image analysis.

Explores image filtering techniques, including linear and nonlinear filters, for artifact removal and feature enhancement.

Explores Fourier filtering principles, applications in image processing, and efficient FFT implementation.

Covers the fundamental principles of image filtering in biomedical analysis.

Introduces segmentation techniques, from intensities to objects and measurements generation.

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

Covers basic to advanced morphological operations for image analysis workflows.

Covers the process of analyzing particles in images using Fiji software.

Summarizes image segmentation, morphological filters, and converting pixels to objects.

Covers regions of interest, unbiased experiments, results storage, and batch image analysis.

Explores the significance of ROIs in measurements and how to manipulate them.

Emphasizes the need for independent channels when detecting objects in images.

Covers setting measurements, managing results, using macro language functions, getting statistics, and saving results as CSV files.

Covers batch processing images using macros in Fiji software.

Covers Regions of Interest, unbiased measurements, results tables, and batch processing.

Introduces color models and methods to reduce dataset size for display.

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