Course detail
Analysis of Biomedical Images
FEKT-FABOAcad. year: 2011/2012
The subject is oriented towards providing an overview of the methods of biomedical image analysis, and a good insight into their concepts, as related to the properties of the medical image data obtained by individual imaging modalities used in medicine.
Language of instruction
Czech
Number of ECTS credits
5
Mode of study
Not applicable.
Guarantor
Learning outcomes of the course unit
Theoretical and practical knowledge of the medical image analysis area, togehter with experience in realising these methods in a suitable software platform.
Prerequisites
The subject knowledge on the Bachelor´s degree level is requested.
Co-requisites
Not applicable.
Planned learning activities and teaching methods
Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.
Assesment methods and criteria linked to learning outcomes
Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.
Course curriculum
Two-dimensional signal as image representation, digital image representation, basic image properties, data properties in planar X-ray imaging, computed tomography, data properties in magnetic resonance imaging, in ultrasonography, electron microscopy, infrared imaging, electric impedance tomography, pre-processing of medical image data, medical image registration and fusion, tomographic data reconstruction, texture analysis, image segmentation, medical image processing environment.
Work placements
Not applicable.
Aims
Gaining an overview of, and insight into, the methods of medical image analysis; acquiring practical experience in software realisation of the methods.
Specification of controlled education, way of implementation and compensation for absences
Limitations of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year.
Recommended optional programme components
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
J.Jan: Medical Image Processing,Reconstruction and Restoration, CRC Taylor and Francis 2006
Recommended reading
A.K.Jain: Fundamentals of Digital Image Processing. Prentice Hall, 1989
Classification of course in study plans
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
Syllabus
1. Two-dimensional signal as image representation, 2D Fourier transform and 2D spectra, spatial 3D images, also with temporal development (4D), profiles and slices
2. Digital image representation, basic image properties, 2D DFT and other 2D transforms, discrete spectra, temporal sequences of 2D and 3D images - 4D data
3. Data properties in planar X-ray imaging, and in X-Ray computed tomography (CT)
4. Data properties in Magnetic Resonance Imaging (MRI) and nuclear imaging
5. Data properties in ultrasonography, electron microscopy, infrared imaging, electric impedance tomography
6. Pre-processing of medical image data: contrast and colour transforms, mask operations, denoising, field homogenisation, distortion restitution - geometric transforms, frequency domain processing
7. Medical image registration and fusion: similarity criteria, registration via optimisation, methods for monomodal and multimodal registration, fusion of image information
8. Tomographic data reconstruction: reconstruction from X-ray CT projections - algebraic and frequency-domain methods, filtered back projection, modifications needed in nuclear imaging, principles of image reconstruction in MRI
9. Local features, statistical and frequency-domain parameters, parametric images; edge-, line- and corner detection, raw- and modified edge representation
10. Texture analysis: original domain and frequency domain texture descriptors, feature based and syntactic texture analysis, textural parametric images, textural gradient
11. Image segmentation 1: edge based segmentation and Hough transform, segmentation based on parametric and textural images, region-based segmentation (region growing, splitting and merging, watershed-based segmentation)
12. Image segmentation 2: flexible contour segmentation - parametric flexible contours, level-set contours, active shape contours; pattern-recognition based segmentation
13. Medical image processing environment, hardware and software requirements, medical image data formats, compatibility of image data, trends in analysis of medical images and multidimensional image data
2. Digital image representation, basic image properties, 2D DFT and other 2D transforms, discrete spectra, temporal sequences of 2D and 3D images - 4D data
3. Data properties in planar X-ray imaging, and in X-Ray computed tomography (CT)
4. Data properties in Magnetic Resonance Imaging (MRI) and nuclear imaging
5. Data properties in ultrasonography, electron microscopy, infrared imaging, electric impedance tomography
6. Pre-processing of medical image data: contrast and colour transforms, mask operations, denoising, field homogenisation, distortion restitution - geometric transforms, frequency domain processing
7. Medical image registration and fusion: similarity criteria, registration via optimisation, methods for monomodal and multimodal registration, fusion of image information
8. Tomographic data reconstruction: reconstruction from X-ray CT projections - algebraic and frequency-domain methods, filtered back projection, modifications needed in nuclear imaging, principles of image reconstruction in MRI
9. Local features, statistical and frequency-domain parameters, parametric images; edge-, line- and corner detection, raw- and modified edge representation
10. Texture analysis: original domain and frequency domain texture descriptors, feature based and syntactic texture analysis, textural parametric images, textural gradient
11. Image segmentation 1: edge based segmentation and Hough transform, segmentation based on parametric and textural images, region-based segmentation (region growing, splitting and merging, watershed-based segmentation)
12. Image segmentation 2: flexible contour segmentation - parametric flexible contours, level-set contours, active shape contours; pattern-recognition based segmentation
13. Medical image processing environment, hardware and software requirements, medical image data formats, compatibility of image data, trends in analysis of medical images and multidimensional image data
Exercise in computer lab
26 hod., compulsory
Teacher / Lecturer
Syllabus
1. PC demonstrations and simulations: Two-dimensional signal as image representation, 2D Fourier transform and 2D spectra, spatial 3D images, also with temporal development (4D), profiles and slices
2. PC demonstrations and simulations: Digital image representation, basic image properties, 2D DFT and other 2D transforms, discrete spectra, temporal sequences of 2D and 3D images - 4D data
3. Working with clinical data and visits to clinics: Data properties in planar X-ray imaging, and in X-Ray computed tomography (CT)
4. Working with clinical data and visits to clinics: Data properties in Magnetic Resonance Imaging (MRI) and nuclear imaging
5. Working with clinical data and visits to clinics: Data properties in ultrasonography, electron microscopy, infrared imaging, electric impedance tomography
6. PC demonstrations and simulations: Pre-processing of medical image data: contrast and colour transforms, mask operations, denoising, field homogenisation, distortion restitution - geometric transforms, frequency domain processing
7. PC demonstrations and simulations: Medical image registration and fusion: similarity criteria, registration via optimisation, methods for monomodal and multimodal registration, fusion of image information
8. PC demonstrations and simulations: Tomographic data reconstruction: reconstruction from X-ray CT projections - algebraic and frequency-domain methods, filtered back projection, modifications needed in nuclear imaging, principles of image reconstruction in MRI
9. PC demonstrations and simulations: Local features, statistical and frequency-domain parameters, parametric images; edge-, line- and corner detection, raw- and modified edge representation
10. PC demonstrations and simulations: Texture analysis: original domain and frequency domain texture descriptors, feature based and syntactic texture analysis, textural parametric images, textural gradient
11. PC demonstrations and simulations: Image segmentation 1: edge based segmentation and Hough transform, segmentation based on parametric and textural images, region-based segmentation (region growing, splitting and merging, watershed-based segmentation)
12. PC demonstrations and simulations: Image segmentation 2: flexible contour segmentation - parametric flexible contours, level-set contours, active shape contours; pattern-recognition based segmentation
13. PC demonstrations and simulations: Medical image processing environment, hardware and software requirements, medical image data formats, compatibility of image data, trends in analysis of medical images and multidimensional image data
2. PC demonstrations and simulations: Digital image representation, basic image properties, 2D DFT and other 2D transforms, discrete spectra, temporal sequences of 2D and 3D images - 4D data
3. Working with clinical data and visits to clinics: Data properties in planar X-ray imaging, and in X-Ray computed tomography (CT)
4. Working with clinical data and visits to clinics: Data properties in Magnetic Resonance Imaging (MRI) and nuclear imaging
5. Working with clinical data and visits to clinics: Data properties in ultrasonography, electron microscopy, infrared imaging, electric impedance tomography
6. PC demonstrations and simulations: Pre-processing of medical image data: contrast and colour transforms, mask operations, denoising, field homogenisation, distortion restitution - geometric transforms, frequency domain processing
7. PC demonstrations and simulations: Medical image registration and fusion: similarity criteria, registration via optimisation, methods for monomodal and multimodal registration, fusion of image information
8. PC demonstrations and simulations: Tomographic data reconstruction: reconstruction from X-ray CT projections - algebraic and frequency-domain methods, filtered back projection, modifications needed in nuclear imaging, principles of image reconstruction in MRI
9. PC demonstrations and simulations: Local features, statistical and frequency-domain parameters, parametric images; edge-, line- and corner detection, raw- and modified edge representation
10. PC demonstrations and simulations: Texture analysis: original domain and frequency domain texture descriptors, feature based and syntactic texture analysis, textural parametric images, textural gradient
11. PC demonstrations and simulations: Image segmentation 1: edge based segmentation and Hough transform, segmentation based on parametric and textural images, region-based segmentation (region growing, splitting and merging, watershed-based segmentation)
12. PC demonstrations and simulations: Image segmentation 2: flexible contour segmentation - parametric flexible contours, level-set contours, active shape contours; pattern-recognition based segmentation
13. PC demonstrations and simulations: Medical image processing environment, hardware and software requirements, medical image data formats, compatibility of image data, trends in analysis of medical images and multidimensional image data