Engineering Tripos Part IIA, 3G4: Medical Imaging & 3D Computer Graphics, 2023-24
Module Leader
Lecturers
Prof Andrew Gee, Prof Graham Treece
Timing and Structure
Lent term. 10 flipped classroom interactive seminars and 6 traditional lectures. Lectures (but not seminars) will be recorded.
Aims
The aims of the course are to:
- Introduce state-of-the-art techniques for the acquisition, representation and visualisation of structured 3D data.
Objectives
As specific objectives, by the end of the course students should be able to:
- Explain the principles of operation of CT, nuclear medicine and diagnostic ultrasound and magnetic resonance imaging.
- Be aware of the advantages and risks associated with these techniques and understand the types of diagnostic problems that each can address.
- Be aware of other types of data to which segmentation and visualisation algorithms can be applied (eg. CAD models).
- Understand the different ways to represent 3D data and appreciate the advantages and disadvantages of each technique.
- Know how to extract surfaces from volumetric data.
- Be aware of the range of computer graphics algorithms and hardware used to visualise 3D data.
- Understand how surfaces can be rendered using suitable illumination and reflection models.
- Know how to visualise voxel arrays directly using volume rendering techniques.
Content
The main application area considered in the module is diagnostic medical imaging: 3D data is acquired using one of the clinical imaging modalities (e.g. CT), represented as a voxel array or segmented into surfaces, then visualised using computer graphics techniques. While medical imaging is the focus of the course, many of the techniques used to segment, represent and visualise the 3D data sets are generic and can equally be applied to other types of data, such as CAD models.
Medical Image Acquisition (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- X-rays and the Radon transform
- Tomographic reconstruction algorithms in both the spatial and frequency domains
- Emission computed tomography
- SPECT and PET
- Iterative reconstruction algorithms
- 2D and 3D ultrasound
- Introduction to Magnetic Resonance Imaging
Extracting information from 3D data (6 lectures, Prof Graham Treece)
Polygonal representations and efficient storage
- Parametric curves and surfaces
- Subdivision and display of parametric surfaces
Surfaces from sampled data
- Thresholding, morphological operators and contours
- Surface extraction - marching cubes
Interpolating sampled data
- Interpolation of isotropic data
- Distance transforms and interpolation of non-isotropic data
- Unstructured data - RBFs and Delaunay triangulation
Direct surface capture
- Laser stripe scanners
- Space encoding: the cubicscope
3D Graphical Rendering (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- Viewing systems: viewpoints and projection
- Reflection and illumination models: the Phong reflection model
- Surface rendering: incremental shading techniques, hidden surface removal using Z-buffers
- Shadows and textures
- Ray tracing
- Volume rendering
- Computer graphics hardware
Coursework
A computer-based laboratory exploring the visualization and analysis of CT data. Students write algorithms to generate slices through the 3D data set, observing the differences between linear and nearest-neighbour interpolation. They go on to fit surfaces to the data and analyse some basic geometric properties of the surfaces. Finally, they use Vulkan to visualize the surfaces from different viewpoints and under different lighting conditions, including a "fly-through" visualization mode.
Learning objectives:
- To appreciate the 3D nature of the data acquired by many medical imaging devices.
- To investigate how such data can be stored and resliced in a C++ software framework.
- To consider techniques for extracting surfaces from such data.
- To understand how surfaces can be represented by triangular meshes and stored in suitable C++ data structures.
- To analyse properties of such surfaces using basic computational geometry algorithms.
- To experiment with graphical rendering in a Vulkan framework.
Practical information:
- Sessions will take place in the DPO, during weeks 1-8.
- This activity involves preliminary work (reading the handout, around one hour).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 30/05/2023 15:22
Engineering Tripos Part IIA, 3G4: Medical Imaging & 3D Computer Graphics, 2025-26
Module Leader
Lecturers
Prof Andrew Gee, Prof Graham Treece
Timing and Structure
Lent term. 10 flipped classroom interactive seminars and 6 traditional lectures. Lectures (but not seminars) will be recorded.
Aims
The aims of the course are to:
- Introduce state-of-the-art techniques for the acquisition, representation and visualisation of structured 3D data.
Objectives
As specific objectives, by the end of the course students should be able to:
- Explain the principles of operation of CT, nuclear medicine and diagnostic ultrasound and magnetic resonance imaging.
- Be aware of the advantages and risks associated with these techniques and understand the types of diagnostic problems that each can address.
- Be aware of other types of data to which segmentation and visualisation algorithms can be applied (eg. CAD models).
- Understand the different ways to represent 3D data and appreciate the advantages and disadvantages of each technique.
- Know how to extract surfaces from volumetric data.
- Be aware of the range of computer graphics algorithms and hardware used to visualise 3D data.
- Understand how surfaces can be rendered using suitable illumination and reflection models.
- Know how to visualise voxel arrays directly using volume rendering techniques.
Content
The main application area considered in the module is diagnostic medical imaging: 3D data is acquired using one of the clinical imaging modalities (e.g. CT), represented as a voxel array or segmented into surfaces, then visualised using computer graphics techniques. While medical imaging is the focus of the course, many of the techniques used to segment, represent and visualise the 3D data sets are generic and can equally be applied to other types of data, such as CAD models.
Medical Image Acquisition (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- X-rays and the Radon transform
- Tomographic reconstruction algorithms in both the spatial and frequency domains
- Emission computed tomography
- SPECT and PET
- Iterative reconstruction algorithms
- 2D and 3D ultrasound
- Introduction to Magnetic Resonance Imaging
Extracting information from 3D data (6 lectures, Prof Graham Treece)
Polygonal representations and efficient storage
- Parametric curves and surfaces
- Subdivision and display of parametric surfaces
Surfaces from sampled data
- Thresholding, morphological operators and contours
- Surface extraction - marching cubes
Interpolating sampled data
- Interpolation of isotropic data
- Distance transforms and interpolation of non-isotropic data
- Unstructured data - RBFs and Delaunay triangulation
Direct surface capture
- Laser stripe scanners
- Space encoding: the cubicscope
3D Graphical Rendering (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- Viewing systems: viewpoints and projection
- Reflection and illumination models: the Phong reflection model
- Surface rendering: incremental shading techniques, hidden surface removal using Z-buffers
- Shadows and textures
- Ray tracing
- Volume rendering
- Computer graphics hardware
Coursework
A computer-based laboratory exploring the visualization and analysis of CT data. Students write algorithms to generate slices through the 3D data set, observing the differences between linear and nearest-neighbour interpolation. They go on to fit surfaces to the data and analyse some basic geometric properties of the surfaces. Finally, they use Vulkan to visualize the surfaces from different viewpoints and under different lighting conditions, including a "fly-through" visualization mode.
Learning objectives:
- To appreciate the 3D nature of the data acquired by many medical imaging devices.
- To investigate how such data can be stored and resliced in a C++ software framework.
- To consider techniques for extracting surfaces from such data.
- To understand how surfaces can be represented by triangular meshes and stored in suitable C++ data structures.
- To analyse properties of such surfaces using basic computational geometry algorithms.
- To experiment with graphical rendering in a Vulkan framework.
Practical information:
- Sessions will take place in the DPO, during weeks 1-8.
- This activity involves preliminary work (reading the handout, around one hour).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 04/06/2025 13:22
Engineering Tripos Part IIA, 3G4: Medical Imaging & 3D Computer Graphics, 2024-25
Module Leader
Lecturers
Prof Andrew Gee, Prof Graham Treece
Timing and Structure
Lent term. 10 flipped classroom interactive seminars and 6 traditional lectures. Lectures (but not seminars) will be recorded.
Aims
The aims of the course are to:
- Introduce state-of-the-art techniques for the acquisition, representation and visualisation of structured 3D data.
Objectives
As specific objectives, by the end of the course students should be able to:
- Explain the principles of operation of CT, nuclear medicine and diagnostic ultrasound and magnetic resonance imaging.
- Be aware of the advantages and risks associated with these techniques and understand the types of diagnostic problems that each can address.
- Be aware of other types of data to which segmentation and visualisation algorithms can be applied (eg. CAD models).
- Understand the different ways to represent 3D data and appreciate the advantages and disadvantages of each technique.
- Know how to extract surfaces from volumetric data.
- Be aware of the range of computer graphics algorithms and hardware used to visualise 3D data.
- Understand how surfaces can be rendered using suitable illumination and reflection models.
- Know how to visualise voxel arrays directly using volume rendering techniques.
Content
The main application area considered in the module is diagnostic medical imaging: 3D data is acquired using one of the clinical imaging modalities (e.g. CT), represented as a voxel array or segmented into surfaces, then visualised using computer graphics techniques. While medical imaging is the focus of the course, many of the techniques used to segment, represent and visualise the 3D data sets are generic and can equally be applied to other types of data, such as CAD models.
Medical Image Acquisition (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- X-rays and the Radon transform
- Tomographic reconstruction algorithms in both the spatial and frequency domains
- Emission computed tomography
- SPECT and PET
- Iterative reconstruction algorithms
- 2D and 3D ultrasound
- Introduction to Magnetic Resonance Imaging
Extracting information from 3D data (6 lectures, Prof Graham Treece)
Polygonal representations and efficient storage
- Parametric curves and surfaces
- Subdivision and display of parametric surfaces
Surfaces from sampled data
- Thresholding, morphological operators and contours
- Surface extraction - marching cubes
Interpolating sampled data
- Interpolation of isotropic data
- Distance transforms and interpolation of non-isotropic data
- Unstructured data - RBFs and Delaunay triangulation
Direct surface capture
- Laser stripe scanners
- Space encoding: the cubicscope
3D Graphical Rendering (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- Viewing systems: viewpoints and projection
- Reflection and illumination models: the Phong reflection model
- Surface rendering: incremental shading techniques, hidden surface removal using Z-buffers
- Shadows and textures
- Ray tracing
- Volume rendering
- Computer graphics hardware
Coursework
A computer-based laboratory exploring the visualization and analysis of CT data. Students write algorithms to generate slices through the 3D data set, observing the differences between linear and nearest-neighbour interpolation. They go on to fit surfaces to the data and analyse some basic geometric properties of the surfaces. Finally, they use Vulkan to visualize the surfaces from different viewpoints and under different lighting conditions, including a "fly-through" visualization mode.
Learning objectives:
- To appreciate the 3D nature of the data acquired by many medical imaging devices.
- To investigate how such data can be stored and resliced in a C++ software framework.
- To consider techniques for extracting surfaces from such data.
- To understand how surfaces can be represented by triangular meshes and stored in suitable C++ data structures.
- To analyse properties of such surfaces using basic computational geometry algorithms.
- To experiment with graphical rendering in a Vulkan framework.
Practical information:
- Sessions will take place in the DPO, during weeks 1-8.
- This activity involves preliminary work (reading the handout, around one hour).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 31/05/2024 09:55
Engineering Tripos Part IIA, 3G4: Medical Imaging & 3D Computer Graphics, 2022-23
Module Leader
Lecturers
Prof Andrew Gee and Dr Flavia Mancini
Timing and Structure
Lent term. 10 flipped classroom interactive seminars and 6 traditional lectures. Lectures (but not seminars) will be recorded.
Aims
The aims of the course are to:
- Introduce state-of-the-art techniques for the acquisition, representation and visualisation of structured 3D data.
Objectives
As specific objectives, by the end of the course students should be able to:
- Explain the principles of operation of CT, nuclear medicine and diagnostic ultrasound and magnetic resonance imaging.
- Be aware of the advantages and risks associated with these techniques and understand the types of diagnostic problems that each can address.
- Be aware of other types of data to which segmentation and visualisation algorithms can be applied (eg. CAD models).
- Understand the different ways to represent 3D data and appreciate the advantages and disadvantages of each technique.
- Know how to extract surfaces from volumetric data.
- Be aware of the range of computer graphics algorithms and hardware used to visualise 3D data.
- Understand how surfaces can be rendered using suitable illumination and reflection models.
- Know how to visualise voxel arrays directly using volume rendering techniques.
Content
The main application area considered in the module is diagnostic medical imaging: 3D data is acquired using one of the clinical imaging modalities (e.g. CT), represented as a voxel array or segmented into surfaces, then visualised using computer graphics techniques. While medical imaging is the focus of the course, many of the techniques used to segment, represent and visualise the 3D data sets are generic and can equally be applied to other types of data, such as CAD models.
Medical Image Acquisition (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- X-rays and the Radon transform
- Tomographic reconstruction algorithms in both the spatial and frequency domains
- Emission computed tomography
- SPECT and PET
- Iterative reconstruction algorithms
- 2D and 3D ultrasound
- Introduction to Magnetic Resonance Imaging
Extracting information from 3D data (6 lectures, Dr Flavia Mancini)
Polygonal representations and efficient storage
- Parametric curves and surfaces
- Subdivision and display of parametric surfaces
Surfaces from sampled data
- Thresholding, morphological operators and contours
- Surface extraction - marching cubes
Interpolating sampled data
- Interpolation of isotropic data
- Distance transforms and interpolation of non-isotropic data
- Unstructured data - RBFs and Delaunay triangulation
Direct surface capture
- Laser stripe scanners
- Space encoding: the cubicscope
3D Graphical Rendering (flipped classroom, 5 interactive seminars, Prof Andrew Gee)
- Viewing systems: viewpoints and projection
- Reflection and illumination models: the Phong reflection model
- Surface rendering: incremental shading techniques, hidden surface removal using Z-buffers
- Shadows and textures
- Ray tracing
- Volume rendering
- Computer graphics hardware
Coursework
A computer-based laboratory exploring the visualization and analysis of CT data. Students write algorithms to generate slices through the 3D data set, observing the differences between linear and nearest-neighbour interpolation. They go on to fit surfaces to the data, writing algorithms to calculate the volumes enclosed by the surfaces. Finally, they use OpenGL to visualize the surfaces from different viewpoints and under different lighting conditions, including a "fly-through" visualization mode.
Learning objectives:
- To appreciate the 3D nature of the data acquired by many medical imaging devices.
- To investigate how such data can be stored and resliced in a C++ software framework.
- To consider techniques for extracting surfaces from such data.
- To understand how surfaces can be represented by triangular meshes and stored in suitable C++ data structures.
- To analyse properties of such surfaces using basic computational geometry algorithms.
- To experiment with graphical rendering in an OpenGL framework.
Practical information:
- Sessions will take place in the DPO, during weeks 1-8.
- This activity involves preliminary work (reading the handout, around one hour).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 18/07/2022 17:47
Engineering Tripos Part IIA, 3G4: Medical Imaging & 3D Computer Graphics, 2018-19
Module Leader
Lecturers
Dr A Gee, Dr G Treece and Prof R Prager
Lab Leader
Dr G Treece
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce state-of-the-art techniques for the acquisition, representation and visualisation of structured 3D data.
Objectives
As specific objectives, by the end of the course students should be able to:
- Explain the principles of operation of CT, nuclear medicine and diagnostic ultrasound and magnetic resonance imaging.
- Be aware of the advantages and risks associated with these techniques and understand the types of diagnostic problems that each can address.
- Be aware of other types of data to which segmentation and visualisation algorithms can be applied (eg. CAD models).
- Understand the different ways to represent 3D data and appreciate the advantages and disadvantages of each technique.
- Know how to extract surfaces from volumetric data.
- Be aware of the range of computer graphics algorithms and hardware used to visualise 3D data.
- Understand how surfaces can be rendered using suitable illumination and reflection models.
- Know how to visualise voxel arrays directly using volume rendering techniques.
Content
The main application area considered in the module is diagnostic medical imaging: 3D data is acquired using one of the popular imaging modalities (e.g. CT), represented as a voxel array or segmented into surfaces, then visualised using advanced computer graphic techniques. While medical imaging is the focus of the course, many of the techniques used to segment, represent and visualise the 3D data sets are generic and can equally be applied to other types of data, such as CAD models.
Medical Image Acquisition (5L, Prof Richard Prager)
- X-rays and the Radon transform
- Tomographic reconstruction algorithms in both the spatial and frequency domains
- Emission computed tomography
- SPECT and PET
- Iterative reconstruction algorithms
- 2D and 3D ultrasound
- Introduction to Magnetic Resonance Imaging
Extracting information from 3D data (6L, Dr Graham Treece)
Polygonal representations and efficient storage
- Parametric curves and surfaces
- Subdivision and display of parametric surfaces
Surfaces from sampled data
- Thresholding, morphological operators and contours
- Surface extraction - marching cubes
Interpolating sampled data
- Interpolation of isotropic data
- Distance transforms and interpolation of non-isotropic data
- Unstructured data - RBFs and Delaunay triangulation
Direct surface capture
- Laser stripe scanners
- Space encoding: the cubicscope
3D Graphical Rendering (5L, Dr Andrew Gee)
- Viewing systems: viewpoints and projection
- Reflection and illumination models: the Phong reflection model
- Surface rendering: incremental shading techniques, hidden surface removal using Z-buffers
- Shadows and textures
- Ray tracing
- Volume rendering
- Computer graphics hardware
Coursework
A computer-based laboratory exploring the visualization and analysis of CT data. Students write algorithms to generate slices through the 3D data set, observing the differences between linear and nearest-neighbour interpolation. They go on to fit surfaces to the data, writing algorithms to calculate the volumes enclosed by the surfaces. Finally, they use OpenGL to visualize the surfaces from different viewpoints and under different lighting conditions, including a "fly-through" visualization mode.
Learning objectives:
- To appreciate the 3D nature of the data acquired by many medical imaging devices.
- To investigate how such data can be stored and resliced in a C++ software framework.
- To consider techniques for extracting surfaces from such data.
- To understand how surfaces can be represented by triangular meshes and stored in suitable C++ data structures.
- To analyse properties of such surfaces using basic computational geometry algorithms.
- To experiment with graphical rendering in an OpenGL framework.
Practical information:
- Sessions will take place in the DPO, during weeks 1-8.
- This activity involves preliminary work (reading the handout, around one hour).
Full Technical Report:
Students will have the option to submit a Full Technical Report.
Booklists
Please see the Booklist for Group G Courses for references to this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E1
Ability to use fundamental knowledge to investigate new and emerging technologies.
E2
Ability to extract data pertinent to an unfamiliar problem, and apply its solution using computer based engineering tools when appropriate.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P1
A thorough understanding of current practice and its limitations and some appreciation of likely new developments.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
P8
Ability to apply engineering techniques taking account of a range of commercial and industrial constraints.
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
US4
An awareness of developing technologies related to own specialisation.
Last modified: 16/05/2018 13:46
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2024-25
Module Leader
Lecturers
Prof G Hennequin, Prof M Lengyel
Lab Leader
Prof G Hennequin
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (8L) (Prof G Hennequin)
- Neurons and synapses
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
- Perception as Bayesian inference
- Decision making
Learning and memory (8L) (Prof M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Python
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions take place in the DPO.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 2 papers addressing efficient coding of sensory information in the brain.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
Last modified: 31/05/2024 09:55
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2019-20
Module Leader
Lecturers
Dr G Hennequin, Dr M Lengyel, Dr T O'Leary
Lab Leader
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please see the Booklist for Part IIA Courses for references to this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
Last modified: 03/09/2019 12:11
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2020-21
Module Leader
Lecturers
Prof M Lengyel, Dr T O'Leary, Dr Y Ahmadian
Lab Leader
Dr Y Ahmadian
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
Last modified: 28/08/2020 11:08
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2018-19
Module Leader
Lecturers
Dr G Hennequin, Dr M Lengyel, Dr T O'Leary
Lab Leader
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr T O'Leary)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr M Lengyel)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Matlab
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions will take place in the DPO during week 2 (3 sessions: Tuesday 30/01 from 11am-1pm and from 2-4pm; Wednesday 31/01 from 2-4pm).
- This activity involves primary work (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 3 papers addressing efficient coding of sensory information in the brain.
Booklists
Please see the Booklist for Part IIA Courses for references to this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
Last modified: 16/05/2018 13:46
Engineering Tripos Part IIA, 3G3: Introduction to Neuroscience, 2021-22
Module Leader
Lecturers
Dr G Hennequin, Dr Y Ahmadian
Lab Leader
Dr G Hennequin
Timing and Structure
Lent term. 16 lectures.
Aims
The aims of the course are to:
- Introduce students to how the brain processes sensory information, controls our actions, learns through experience and lays down memories.
- Elucidate the computational and engineering principles of brain function.
Objectives
As specific objectives, by the end of the course students should be able to:
- Have a basic grasp of neuroscience that can act as foundation for further study.
- Understand the basic principles of sensory processing, decision making, learning and memory and how engineering concepts can be applied to them.
Content
Perception and action (6L) (Dr G Hennequin)
- Neurons and synapses
- Perception as Bayesian inference
- Decision making
Dynamics of single neurons (2L) (Dr G Hennequin)
- Introduction to basic cell physiology and ion channels
- How do neurons communicate? The action potential and the Hodgkin-Huxley model
Learning and memory (8L) (Dr Y Ahmadian)
- The cellular basis of learning and memory
- Animal learning
- Memory
Coursework
Simulation of different types of neural coding of natural images. Laboratory report and/or Full Technical Report.
Efficient coding in visual cortex
Learning objectives:
- To apply basic techniques from linear algebra, optimization and statistics to understand how the primary visual cortex might efficiently encode natural scenes
- To learn (or consolidate) how to implement simple algorithms in Python
- To consolidate critical analysis and report-writing skills
Practical information:
- Sessions normally take place in the DPO, but could be done online if required by Covid19-related restrictions.
- This activity involves preliminary homework (estimated 30 min duration), consisting of mathematical derivations (including some basic vector calculus) to be performed before coming to the lab.
Full Technical Report:
Students will have the option to submit a Full Technical Report. This will take the form of a unifying review of 2 papers addressing efficient coding of sensory information in the brain.
Booklists
Please refer to the Booklist for Part IIA Courses for references to this module, this can be found on the associated Moodle course.
Examination Guidelines
Please refer to Form & conduct of the examinations.
UK-SPEC
This syllabus contributes to the following areas of the UK-SPEC standard:
Toggle display of UK-SPEC areas.
GT1
Develop transferable skills that will be of value in a wide range of situations. These are exemplified by the Qualifications and Curriculum Authority Higher Level Key Skills and include problem solving, communication, and working with others, as well as the effective use of general IT facilities and information retrieval skills. They also include planning self-learning and improving performance, as the foundation for lifelong learning/CPD.
IA1
Apply appropriate quantitative science and engineering tools to the analysis of problems.
KU1
Demonstrate knowledge and understanding of essential facts, concepts, theories and principles of their engineering discipline, and its underpinning science and mathematics.
KU2
Have an appreciation of the wider multidisciplinary engineering context and its underlying principles.
E3
Ability to apply mathematical and computer based models for solving problems in engineering, and the ability to assess the limitations of particular cases.
P3
Understanding of contexts in which engineering knowledge can be applied (e.g. operations and management, technology, development, etc).
US1
A comprehensive understanding of the scientific principles of own specialisation and related disciplines.
US2
A comprehensive knowledge and understanding of mathematical and computer models relevant to the engineering discipline, and an appreciation of their limitations.
US3
An understanding of concepts from a range of areas including some outside engineering, and the ability to apply them effectively in engineering projects.
Last modified: 31/05/2021 11:09
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