The course provides knowledge on basic and advanced processing algorithms of image, as an important diagnostic tool in medicine. Ability to apply digital image processing techniques in practical problems.

Understanding of theoretical foundations of digital image processing algorithms and ability of their practical implementation. The experience with: image processing for display needs, basic and advanced image processing algorithms, and automatic extraction of image attributes for computer aided diagnostics.

Introduction to digital image processing (application examples, basic components of image processing systems). Basic concepts in image processing (visual perception, image sensing and acquisition, sampling and quantization, relationships between pixels). Image improvement in space domain (intensity transformations, histogram, spatial filtering, smoothing, sharpening). Image improvement in frequency domain (2D Discrete Fourier Transform, properties, filtering in frequency domain). Image restoration (noise models,filtering for noise removal, estimation of the degradation function, inverse filtering, Wiener filter). Color image processing (color models, color transformations, color image processing, pseudo-color image processing, color segmentation). Image compression (redundancy in images, basic lossless compression methods, predictive coding, transformation coding). Image segmentation (point, line, edge detection, thresholding)

Lectures, auditory and computer lab exercises.