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IP = Image processing part
AI = Artificial intelligence part
Steve Mitchell
| Image Data | IVUS sequences the Fusion Project Pig IVUS and IVUS sequences provide by Dijkstra at the LUMC |
| Goals | Develop a methodology for the segmentation of lumen and lamina borders of IVUS frames |
| Methods I, IP | Dynamic Programming and Mahalanobis edge weighting for probable borders. |
| Methods II, AI | Active Shape Model extended in to include adjacent frames for prior knowledge about plausible IVUS border shapes |
| Validation | In the IVUS sequences provided by the LUMC, the data was hand segmented by an expert. Training will be applied to one half of the series and validation against manually traced borders will be applied to the other half. |
Jianchun He
| Image Data | CT images of human inner ear |
| Goals | To automatically (semi-automatically) detect the major objects of interest, including cochlea, IAC(inner auditory canal), Ossicles, semi-circular canals and utricle etc. |
| Methods I, IP | Volume growing approach possiblyusing 3D edges for better delineatiuon of borders |
| Methods II, AI | 3D location of individual objects, their shapes, and similar used as a priori knowledge |
| Validation | 6 volumes with manually traced objects of interest. Assess segmentation accuracy in comparison to manualy defined independent standard. If some learning is used, the leave-one-out validation approach will be used. |
Kyoung-Mi Lee
| Image Data | Breast Cancer Cell (cytological) images |
| Goals | Develop a methodology for cell detection, segmentation and classification based on shape features |
| Methods I, IP | Memory-efficient template matching (iterative GHT) for detection and for segmentation |
| Methods II, AI | Neural network for pattern classification - benign/malignant, especially suitable in on-line modeling of non-stationary process |
| Validation | 800 training cells and 300 testing cells AND using leave-100-out approach. |
John Dill
| Image Data | 3D segmentations of the uterus |
| Goals | Develop a method for registering the uterus |
| Methods I, IP | Generation of a point based model from the surface of the segmentation |
| Methods II, AI | Principal Component Analysis for generation of surface model Validation : Use a known deformation with a known correspondence of the segmentations and see how well the registration matches the transformation |
| Validation |
Tim Irwin
| Image Data | 4D ultrasound data sets of the left ventricle, 6 normals and 7 or so stenotic aortic valve patients taken preop, postop, and 6 months postop; 6 or so balloon data sets used in volume validation. |
| Goals | To develop an automated method to segment the epicardium of the left ventricle |
| Methods I, IP | Suboptimal 3D graph searching |
| Methods II I, AI | Use the position of the endocardium as priori information |
| Validation | Compare the epicardial models of those found by the above method to those modeled by an expert. Calculate the border positioning errors, and possibly volume errors, to give a quantitative error measurement. |
Seonho Shin
| Image Data | CT images of heart(3D heart image) |
| Goals | Define Coronary Artery of heart |
| Methods I, IP | ?? |
| Methods II, AI | ?? |
| Validation | ?? |
Xiaoxu Han
| Image Data | Cardiac MR images and image sequences |
| Goals | Compare PCA and ICA performance |
| Methods I, IP |
Incorporate ICA in the existing AAM/AAMM framework. |
| Methods II, AI | Implement ICA approach to replace the currently used PCA approach in AAM and AAMM modeling |
| Validation | Compare 2D and 2D+T AAM - results between PCA/ICA/manual using standard comparison indices used in the past. |
Junghyun Byun
| Image Data | CT, MRI, and SPECT images |
| Goals | Improve a manual and an automated coregistrations |
| Methods I, IP | Rigid body transformation rotation method for manual coregistration. |
| Methods II, AI | Mutual information method for automated coregistration |
| Validation | Using multimodality brain images, compare the coregistration routine by the above methods to commercially used one. |
Ryan Long
| Image Data | CT volumes of lungs |
| Goals | Develop a reliable 3D tree representation |
| Methods I, IP | Identify primary airway trees from volumetric CT images together with some approximate bifurcation points |
| Methods II, AI | develop tree-structure-based representation allowing node identification, tree traversal, basic voxel-based measurements |
| Validation | Comparison with manual tracings using quantitative indices |
Igor Okulist
| Image Data | 4D ultrasound data sets of the left ventricle, 6 normals and 7 or so stenotic aortic valve patients taken preop, postop, and 6 months postop; |
| Goals | To develop automated method to detect heart twisting motion. Find out the "normal" twisting pattern and compare patient data against normal. |
| Methods I, IP |
Suboptimal 3D graph searching, pattern matching. |
| Methods II, AI | Use the gray level values, position of the endocardium and valve location as priori information. |
| Validation | Compare the heart twisting pattern to the one found in MR studies. |
Mark Olszewski
| Image Data |
3D IVUS data (2D sequence) |
| Goals | Develop a rudimentary 3D approach to IVUS segmentation |
| Methods I, IP | 3D - considering temporal sequence context |
| Methods II, AI | training from examples |
| Validation | against manually defined independent standard |
David Rodriguez
| Image Data |
Sequences of MR Images of arteries that (may) have plaque |
| Goals | Segmentation of vesssel lumen, plaque and wall |
| Methods I, IP | Edge based segmentation, generalized hough transform |
| Methods II, AI | Graph search dynamic programming with automated cost functions |
| Validation | Provided images, manually traced. Some of the images will be used for training and some for validation |
Last Modified: 2001