Biomedical subjects

Kaleem Siddiqi

Publications and source records attributed to Kaleem Siddiqi.

8 recordsLinked to original sources

PubMed2006

3D curve inference for diffusion MRI regularization and fibre tractography.

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibres as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [Parent, P., Zucker, S., 1989. Trace inference, curvature consistency, and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 823-839] by using a notion of co-helicity to indicate compatibility between fibre orientations at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain. We also demonstrate the use of the technique to improve the performance of a fibre tracking algorithm.

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PubMed2006

Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery.

The simulation of pituitary gland surgery requires a precise classification of soft tissues, vessels and bones. Bone structures tend to be thin and have diffuse edges in CT data, and thus the common method of thresholding can produce incomplete segmentations. In this paper, we present a novel multi-scale sheet enhancement measure and apply it to paranasal sinus bone segmentation. The measure uses local shape information obtained from an eigenvalue decomposition of the Hessian matrix. It attains a maximum in the middle of a sheet, and also provides local estimates of its width and orientation. These estimates are used to create a vector field orthogonal to bone boundaries, so that a flux maximizing flow algorithm can be applied to recover them. Hence, the sheetness measure has the essential properties to be incorporated into the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We validate the approach quantitatively on synthetic examples, and provide comparisons with existing segmentation techniques on paranasal sinus CT data.

Algorithms↗

PubMed2005

Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques.

In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.

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PubMed2005

Hippocampal shape analysis using medial surfaces.

In magnetic resonance imaging (MRI) research, significant attention has been paid to the analysis of the hippocampus (HC) within the medial temporal lobe because of its importance in memory and learning, and its role in neurodegenerative diseases. Manual segmentation protocols have established a volume decline in the HC in conjunction with Alzheimer's disease, epilepsy, post-traumatic stress disorder, and depression. Furthermore, recent studies have investigated age-related changes of HC volume which show an interaction with gender; in early adulthood, volume reduction of the HC is found in men but not in women. In this paper, we investigated gender differences in normal subjects in young adulthood by employing a shape analysis of the HC using medial surfaces. For each subject, the most prominent medial manifold of the HC was extracted and flattened. The flattened sheets were then registered using both a rigid and a non-rigid alignment technique, and the medial surface radius was expressed as a height function over them. This allowed for an investigation of the association between subject variables and the local width of the HC. With regard to the effects of age and gender, it could be shown that the previously observed gender differences were mostly due to volume loss in males in the lateral areas of the HC head and tail. We suggest that the analysis of HC shape using medial surfaces might thus serve as a complimentary technique to investigate group differences to the established segmentation protocols for volume quantification in MRI.

Adolescent↗

PubMed2005

3D curve inference for diffusion MRI regularization.

We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [8] by using a notion of co-helicity to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain.

Algorithms↗

PubMed2005

Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery.

We present a novel multi-scale bone enhancement measure that can be used to drive a geometric flow to segment bone structures. This measure has the essential properties to be incorporated in the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We present synthetic examples that validate our approach and show a comparison between existing segmentation techniques of paranasal sinus CT data.

Algorithms↗

PubMed2005

Flux driven automatic centerline extraction.

We present a fast, robust and automatic method for computing centerline paths through tubular structures for application to virtual endoscopy. The key idea is to utilize a skeletonization algorithm which exploits properties of the average outward flux of the gradient vector field of a Euclidean distance function from the boundary of the structure. The algorithm is modified to yield a collection of 3D curves, each of which is locally centered. The approach requires no user interaction, is virtually parameter free and has low computational complexity. We validate the method quantitatively on a number of synthetic data sets with known centerlines and qualitatively on colon and vessel data segmented from CT and CRA images.

Algorithms↗

PubMed2005

Indexing hierarchical structures using graph spectra.

Hierarchical image structures are abundant in computer vision and have been used to encode part structure, scale spaces, and a variety of multiresolution features. In this paper, we describe a framework for indexing such representations that embeds the topological structure of a directed acyclic graph (DAG) into a low-dimensional vector space. Based on a novel spectral characterization of a DAG, this topological signature allows us to efficiently retrieve a promising set of candidates from a database of models using a simple nearest-neighbor search. We establish the insensitivity of the signature to minor perturbation of graph structure due to noise, occlusion, or node split/merge. To accommodate large-scale occlusion, the DAG rooted at each nonleaf node of the query "votes" for model objects that share that "part," effectively accumulating local evidence in a model DAG's topological subspaces. We demonstrate the approach with a series of indexing experiments in the domain of view-based 3D object recognition using shock graphs.

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