||This material is based upon work
supported by the National Science Foundation (Principal Investigator: Jing Hua). Any opinions, findings and
conclusions or recommendations expressed in this material are those of
the author(s) and do not necessarily reflect the views of the National
Science Foundation (NSF).
Visual Analytics of Cross-subject, Multi-measure, Multi-dimensional Imaging Data
Graphics and Imaging Lab
Wayne State University
Scientific imaging data is typically collected from individual subjects, and
many consists of sophisticated three-dimensional (3D) geometric structures and
high-dimensional, heterogeneous features. Assessment of similarity and disparity
from the multimodality, heterogeneous 3D imaging data across subjects plays a
central role in modern scientific discovery. In many scenarios, intrinsic
geometric structures embedded in 3D imaging of real-world objects are very
effective in mapping individual objects for interpretation of their similarity
and disparity. A rigorous computational framework that tightly unifies geometric
mapping, diffusion and matching is essential for integrative analysis of a
variety of underlying relationships in features inherited from imaging datasets
of a large number of subjects, i.e., 3D imaging informatics. These research
activities will address the following major themes and
objectives: (1) To design novel geometric diffusion theory and methods to compute
multi-scale representations for imaging data abstraction and then investigate
hierarchical image feature extraction and matching/registration; (2) To design
and develop a coordinated visualization framework for comparative analysis of
cross-subject multi-measure brain images, which includes both data abstraction
and visual abstraction; (3) To validate and apply
the proposed theoretical approaches to real medical imaging informatics
problems, such as Brain Tumor, Epilepsy, Sturge-Weber
Syndrome, and so on. In addition, the PIs' research endeavors have been tightly integrated
with a complementary set of educational objectives, including: (1) the
development of new strategies for truly multi-disciplinary science education;
(2) the enhancement of the existing curricula; (3) the doctoral training of
graduate researchers; (4) the implementation of mentoring activities for
students from underrepresented groups.
Intrinsic Geometric Scale Space (IGSS)
|We have formalized a novel, intrinsic geometric scale space (IGSS)
of 3D surface shapes. The intrinsic geometry of a surface is diffused by
means of the Ricci flow for the generation of a geometric scale space.
We rigorously prove that this multiscale shape representation satisfies
the axiomatic causality property. Within the theoretical framework, we
further present a feature-based multiscale shape representation derived
from IGSS processing, which is shown to be theoretically plausible and
practically effective. By integrating the concept of scale-dependent
saliency into the shape description, this representation is not only
highly descriptive of the local structures, but also exhibits several
desired characteristics of global shape representations, such as being
compact, robust to noise and computationally efficient. We demonstrate
the capabilities of our approach through salient geometric feature
detection and highly discriminative matching of 3D scans.
Parameterization of General Surfaces Using Lie Advection
|Parameterization of complex surfaces constitutes a major
means of visualizing highly convoluted geometric structures as well as
other properties associated with the surface. It also enables users with
the ability to navigate, orient, and focus on regions of interest within
a global view and overcome the occlusions to inner concavities. We
propose a novel area-preserving surface parameterization method which is
rigorous in theory, moderate in computation, yet easily extendable to
surfaces of non-disc and closed-boundary topologies. Starting from the
distortion induced by an initial parameterization, an area restoring
diffeomorphic flow is constructed as a Lie advection of differential
2-forms along the manifold, which yields equality of the area elements
between the domain and the original surface at its final state.
Existence and uniqueness of result are assured through an analytical
derivation. Based upon a triangulated surface representation, we also
present an efficient algorithm in line with discrete differential
modeling. As an exemplar application, the utilization of this method for
the effective visualization of brain cortical imaging modalities is
presented. Compared with conformal methods, our method can reveal more
subtle surface patterns in a quantitative manner. It, therefore,
provides a competitive alternative to the existing parameterization
techniques for better surface-based analysis in various scenarios.
■ Quansi-Conformal Surface
|Quasi-conformal deformation analysis of 3D dynamic models utilizes a
new metric on the quotient space of surfaces which is able to capture
changes of the curvature at each vertex of a simplicial complex during
deformation. Hence, the deformation curve can be obtained by calculating
the geodesic curve connecting two shapes in the shape space manifold. To
facilitate the deformation analysis, the deformation curves are first
transferred to the same location of the shape space. Finally, the
Multi-Dimensional Scaling method is employed to eliminate the redundant
dimensions allowing easy comparison of the deformations. The algorithm
can effectively classify normal and abnormal deformations in shape
Registration with Shape Spectrum
||We develop a fully automatic geometric method for registering
surfaces extracted from images. The algorithm uses shape spectrum to
extract the shape characteristics which are employed as the surface
signature to find the correspondent regions between the surfaces. The
method is simple yet efficient and accurate. The novel surface
registration method based on shape spectrum is of excellent accuracy for
surface registration. Based on our observation, the second eigenvector
of the Laplacian, called the Fiedler vector, has interesting properties,
making it a good permutation vector for numerical computations.
Image Processing for Prepless Virtual Colonography
|A novel segmentation framework for a prepless virtual colonoscopy
(VC) is presented, which reduces the necessity for colon cleansing
before the CT scan. The patient is injected rectally with a
water-soluble iodinated contrast medium using manual insufflators and a
small rectal catheter. Compared to the air-based contrast medium, this
technique can better preserve the color lumen and reduce the partial
volume effect. However, the contrast medium, together with the fecal
materials and air, makes colon wall segmentation challenging.Our
solution makes no assumptions about the shape, size, and location of the
fecalmaterial in the colon. This generality allows us to label the fecal
material accurately and extract the colon wall reliably. The accuracy of
our technique has been verified on 60 human subjects. Compared with
current VC technologies, our method is shown to be better in terms of
both sensitivity and specificity. Further, in our experiments, the
accuracy of the technique was comparable to that of optical colonoscopy
■ Business Model-based Coclustering
|We present a quantitative analysis and modeling tool that is able to
characterize anatomical connectivity patterns based on a newly developed
coclustering algorithm, termed the Business model-based Coclustering
Algorithm (BCA). We apply BCA to Diffusion Tensor Imaging (DTI) data in
order to provide an automated and reproducible assessment of the
connectivity patterns between different cortical areas in human brains.
BCA not only partitions the cortical mantel into well-defined clusters,
but also maximizes the connectivity strength between these clusters.
Moreover, BCA is computationally robust and allows both outlier
detection as well as parameter-independent determination of the number
of clusters. Our coclustering results have showed good performance of
BCA in identifying major white matter fiber bundles in human brains and
facilitated the detection of abnormal connectivity patterns in patients
suffering from various neurological diseases.
Flow-based Spherical Parameterization and Surface Registration
|We present an improved Euclidean Ricci ow method for
spherical parameterization, and subsequently invent a scale space
processing built upon Ricci energy to extract robust surface features
for accurate surface registration. Since our method is based on the
proposed Euclidean Ricci ow, it inherits the properties of Ricci ow such
as conformality, robustness and intrinsicalness, facilitating e cient
and e ective surface mapping. Compared with other surface registration
methods using curvature or sulci pattern, our method demonstrates a
signi cant improvement for surface registration. In addition, Ricci
energy can capture local di erences for surface analysis as shown in the
experiments and applications.
Please register and download the software:
at the following link:
http://22.214.171.124:8080/ClientApp/clientdata. The brief introduction and
manual can be download at BrainSpace_V2.1.pdf.
Selected, Related Publications:
Jiaxi Hu, Guangyu Zou, and Jing Hua, "Volume-Preserving
Mapping and Registration for Collective Data Visualization," IEEE
Transactions on Visualization and Computer Graphics (IEEE VIS '14),
Vol. 20, No. 6, pp. 2664-2673, 2014.
Vahid Taimouri and Jing Hua, "Deformation
Similarity Measurement in Quasi-Conformal Shape Space," Graphical
Models, Vol. 76, No. 2, pp. 57-69, 2014.
Xuejiao Chen, Jiaxi Hu, Huiguang He, and
Jing Hua, "Spherical Volume-Preserving Demons Registration,"
Computer-Aided Design (ACM SPM '14), 2014.
Vahid Taimouri and Jing Hua, "Visualization
of Shape Motions in Shape Space," IEEE Transactions on Visualization and Computer Graphics
(IEEE VIS '13), Vol. 19, No. 12, 2644-2652, 2013.
Xuejiao Chen, Huiguang He, Guangyu Zou, Xiaopeng Zhang, Xianfeng Gu, and
Jing Hua, "Ricci Flow-based Spherical Parameterization and
Surface Registration," Computer Vision and Image Understanding,
Vol. 117, No. 9, pp. 1107-1118, 2013.
Guangyu Zou, Jiaxi Hu, Xianfeng Gu, and Jing Hua, "Authalic
Parameterization of General Surfaces Using Lie Advection," IEEE Transactions on Visualization and Computer Graphics
(VIS), Vol. 17, No. 6, 2011.
Vahid Taimouri, Xin Liu, Zhaoqiang Lai, Chang Liu,
Darshan Pai, and Jing Hua, "Colon Segmentation for Prepless Virtual Colonoscopy," IEEE Transactions on
Information Technology in Biomedicine, Vol. 15, 2011.
Darshan Pai, Hamid Soltanian-Zadeh, and Jing
Hua, "Evaluation of Fiber Bundles across Subjects through
Brain Mapping and Registration of Diffusion Tensor Data," NeuroImage,
Vol. 54, pp. S165-S175, 2011.
Cui Lin, Darshan Pai, Shiyong Lu, Otto Muzik,
and Jing Hua, “Coclustering for Cross-subject Fiber Tract
Analysis through Diffusion Tensor Imaging,” IEEE Transactions on
Information Technology in Biomedicine, Vol. 14, No. 2, pp. 514 - 525,
Guangyu Zou, Jing Hua, Zhaoqiang Lai,
Xianfeng Gu, and Ming Dong, "Intrinsic Geometric Scale Space by Shape
Diffusion," IEEE Transactions on Visualization and Computer Graphics
Vol. 15, No. 6, pp. 1193 - 1200, 2009.
Yanhua Chen, Lijun Wang, Ming Dong, and Jing
Hua, "Exemplar-based Visualization of Large
Document Corpus," IEEE Transactions on Visualization and Computer
Graphics (INFOVIS), Vol. 15, No. 6, pp. 1169 - 1176, 2009.
Guangyu Zou, Jiaxi Hu, Xianfeng Gu, and Jing Hua, "Area-preserving
Surface Flattening Using Lie Advection," In Proceedings of the 14th International Conference on Medical Image Computing
and Computer Assisted Intervention
Zhaoqiang Lai, Jiaxi Hu, Chang Liu, Vahid
Taimouri, Darshan Pai, Jiong Zhu, Jianrong Xu, and Jing Hua, "Intra-patient Supine-Prone
Colon Registration in CT Colonography Using Shape Spectrum," In Proceedings of the 13th International Conference on Medical Image Computing
and Computer Assisted Intervention
(MICCAI), 2010 (Oral; Acceptance Rate: 5.7%)
Vahid Taimouri, Huiguang He, and Jing Hua, "Comparative
Analysis of Quasi-Conformal Deformations in Shape Space," In Proceedings of the 13th International Conference on Medical Image Computing
and Computer Assisted Intervention
Wei Zeng, Lok Ming Lui, Lin Shi, Defeng
Wang, Winnie C.W. Chu, Jack C.Y. Cheng, Jing Hua, Shing-Tung Yau, and
Xianfeng Gu, "Shape
Analysis of Vestibular Systems in Adolescent Idiopathic Scoliosis Using
Geodesic Spectra," In Proceedings of the 13th International Conference on Medical Image Computing
and Computer Assisted Intervention
Point of Contact: PI Jing
Date of Last Update:
June 10, 2014.