Digital 2D Biosignal Processing

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Biomedical Informatics

Synopsis:

The basic topics of the subject are techniques of 2D bio-signal processing,

discrete 2D transforms, linear filtering, image reconstruction from projection,

3D reconstruction, 2D signal analysis (distortion and noise identification,

wavelet decomposition, edge detection, segmentation, texture analysis), lossless

and lossy compression, quality classification, 2D bio-signal processing in

medicine and ecology (US, mammography, microscopic imaging)

Requirements:

Syllabus of lectures:

1. Linear 2D systems, 2D spectra, digital representation of images, 2D discrete

operators

2. 2D discrete transformations, DFT, cosine and sine, Hadamard, Haar

transformation

3. Image restoration, distortion and noise identification, Wiener filtering in

frequency domain

4. Image segmentation. Texture analysis, brightness calibration, image

segmentation based on histogram, optimal tresholding

5. Spline curves, image segmentation based on deformable models

6. Wavelet decomposition and its utilization in 2D bio-signal analysis and

compression

7. Noise suppression, nonlinear filtering, Gauss, median, mode, rank-order

filtering and their properties

8. Morphological image analysis, erosion, dilatation, morphological opening and

closing, morphological filtering and its properties

9. 3D reconstruction of object position and orientation based on its 2D

projection, manual transport analysis

10. Image registration, geometrical transformation, image registration

11. Image compression, Lossless and lossy compression JPEG, JPEG2000

12. Line detection, Hough transformation, circle detection, generalized Hough

transform

13. 2D objects representation based on their contours, object classification

based on contour descriptors

14. 2D bio-signal processing in medicine and ecology

Syllabus of tutorials:

Study Objective:

The basic topics of the subject are techniques of 2D bio-signal processing,

discrete 2D transforms, linear filtering, image reconstruction from projection,

3D reconstruction, 2D signal analysis (distortion and noise identification,

wavelet decomposition, edge detection, segmentation, texture analysis), lossless

and lossy compression, quality classification, 2D bio-signal processing in

medicine and ecology (US, mammography, microscopic imaging)

Study materials:

[1]Rafael C.Gonzales, Paul Wintz: Digital Image Processing, 2002.

[2]Al Bovik: Handbook of Image & Video Processing. Academic Press, 2000.

[3]Gonzales, C.R., Woods, E.R., Eddins, L.S.: Digital Image Processing Using

MATLAB. Prentice Hall, 2004.

[4]Sonka, M., Fitzpatrick, J.M.: Handbook of Medical Imaging, Volume 2. Medical

Image Processing and Analysis, SPIE Press, 2000.

[5]Davies, E.R.: Machina Vision, Theory, Algorithms, Practicalities, 3rd

edition, Elsevier Inc. 2005.

[6]Fontoura Costa, L., Marcondes Cesar, R.Jr.: Shape Analysis and

Classification, Tudory and Praktice, CRC Press, 2000.

[7]Soille, P.: Morphological Image Analysis, Principles and Applications, 2nd

edition, Springer-Verlag, 2003.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: