{"title":"A new variational shape-from-orientation approach to correcting intensity inhomogeneities in MR images","authors":"S. Lai, M. Fang","doi":"10.1109/BIA.1998.692393","DOIUrl":"https://doi.org/10.1109/BIA.1998.692393","url":null,"abstract":"A new algorithm based on shape from orientation formulation in a regularization framework is proposed for correcting intensity inhomogeneities in MR images. Unlike most previous methods, this algorithm is fully automatic and very efficient. In addition, it can be applied to a wide variety of images since no prior classification knowledge is assumed. In this algorithm, the authors use a finite element basis to represent the bias field function. Orientation constraints are computed at the nodes of the finite element discretization away from the boundary between different regions. The selection of reliable orientation constraints is facilitated by the goodness of fitting a first-order polynomial model to the neighborhood of each nodal location. The selected orientation constraints are integrated in a regularization framework, which leads to the minimization of a convex and quadratic energy function. This energy minimization is achieved by solving a linear system with a large, sparse, symmetric and positive semi-definite stiffness matrix. The authors employ an adaptive preconditioned conjugate gradient algorithm to solve the linear system efficiently. Experimental results on a variety of MR images are given to demonstrate the effectiveness and efficiency of the proposed algorithm.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127330612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Forward deformation of PET volumes using material constraints","authors":"G. Klein","doi":"10.1109/BIA.1998.692394","DOIUrl":"https://doi.org/10.1109/BIA.1998.692394","url":null,"abstract":"A method for non-rigidly deforming 3D PET datasets is described. The method uses a Lagrangian motion field description and a forward deformation mapping which conserves total voxel intensities. To regularize the deformation, a large-deformation isotropic strain energy function is used that models the material properties of cardiac tissue. The method is applied to motion compensation in PET to combine different time frames, or gates, of a cardiac sequence.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129561274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Joint reconstruction of 2-D left ventricular displacement and contours from tagged magnetic resonance images using Markov random field edge prior","authors":"L. Yan, T. Denney","doi":"10.1109/BIA.1998.692408","DOIUrl":"https://doi.org/10.1109/BIA.1998.692408","url":null,"abstract":"Magnetic Resonance (MR) tagging has been shown to be a useful method for non-invasively measuring the deformation of the left ventricle (LV), during the cardiac cycle. By reconstructing a displacement field based on the movement of the tag lines, one can compute myocardial contraction measures such as strain. Existing methods depend on user-defined LV contours, which require human intervention and are therefore the biggest bottleneck in the reconstruction process. Here, the authors present a method for reconstructing 2-D LV deformation without user-defined contours. They use a compound Gauss-Markov random field to model the 2-D vector displacement field, which is parameterized by two closed and smooth contours. By iteratively optimizing the contours, the displacement field, and the parameters, the authors obtain an estimate of the displacement field and the contours. Experimental results on in vivo human data are presented that demonstrate the accuracy of the authors' algorithm.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Singularities and the features of deformation grids [brain images analysis]","authors":"F. Bookstein","doi":"10.1109/BIA.1998.692392","DOIUrl":"https://doi.org/10.1109/BIA.1998.692392","url":null,"abstract":"Biological shape differences often are represented as diffeomorphisms of a Cartesian coordinate grid. The problem addressed here is the extraction of spatially discrete, localized features of such transformation grids, which often help to identify underlying developmental or pathological processes. This paper shows how some such features can be identified with variants of the singularity (x,y)/spl rarr/(x,x/sup 2/y+y/sup 3/) that are visually evident as creases in the grid. The crease is a nongeneric singularity at which a pair of cusps appears as a function of a parameter for extrapolation. The paper shows how this representation extracts informative discrete feature sets from deformations characterizing two different brain diseases, schizophrenia and Fetal Alcohol Syndrome, in the mid-sagittal plane (plane of symmetry). Creases appear to be robust under relaxation of bending energy against Euclidean distance, one analogue to multiscale analysis for discrete punctate data. The author suggests that they comprise the simplest words in an eventual grammar of grids.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122440484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Real-time tracking of contrast bolus propagation in X-ray peripheral angiography","authors":"Zhenyu Wu, J. Qian","doi":"10.1109/BIA.1998.692440","DOIUrl":"https://doi.org/10.1109/BIA.1998.692440","url":null,"abstract":"This paper describes a robust and fast algorithm for real-time tracking of contrast bolus propagation in vessels during X-ray peripheral angiography studies, in which the authors are interested in imaging the arterial structures in the legs. A bolus of contrast medium is injected to the patient and a time-sequence of X-ray images records the bolus propagation. Since the field of view of the imaging device is small compared to the object length, the device has to step through an number of preset stations to follow the bolus. Currently it requires a physician to manually issue stepping commands according to his/her visual assessment of bolus propagation observed in a monitor. A smart data acquisition technology is being developed to replace this error-prone manual process in which the bolus tracking algorithm plays a key role. Novel feature normalization circumvents the need for estimating vessel sizes and enables one to estimate contrast density and trade bolus using only features that can be extracted efficiently from acquired image sequences. The use of a parametric model, developed to characterize extracted features, makes the algorithm resistant to noise and feature extraction errors. Extensive experiments on real and simulated angiographic sequences have demonstrated the robustness, accuracy and efficiency of the tracking algorithm.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124590676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Preprocessing of fMR datasets","authors":"F. Kruggel, X. Descombes, D. Yves von Cramon","doi":"10.1109/BIA.1998.692518","DOIUrl":"https://doi.org/10.1109/BIA.1998.692518","url":null,"abstract":"When studying complex cognitive tasks using functional magnetic resonance (fMR) imaging one often encounters weak signal responses. These weak responses are corrupted by noise and artifacts of various sources. Preprocessing of the raw data before the application of test statistics helps to extract the signal and thus can vastly improve signal detection. The authors discuss artifact sources and algorithms to handle them. Experiments with simulated and real data underline the usefulness of this preprocessing sequence.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133303604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Zhang, Sven J. Dickinson, S. Sclaroff, Jacob Feldman, Stanley Dunn
{"title":"Shape-based indexing in a medical image database","authors":"Wei Zhang, Sven J. Dickinson, S. Sclaroff, Jacob Feldman, Stanley Dunn","doi":"10.1109/BIA.1998.692519","DOIUrl":"https://doi.org/10.1109/BIA.1998.692519","url":null,"abstract":"Reports on a prototype of a clinical radiograph image database to be indexed by image content. The underlying content-based search engine is based on the modal shape description method for characterizing the shape of a 2-D image region (S.E. Sclaroff, 1995). Using a similarity metric defined in a modal vector space, the authors' system successfully classified radiographic images according to the dental pathologies they depicted. This successful classification demonstrates that the proposed similarity metric effectively captures clinical similarity between images in the database. The prototype is implemented in a Web-based environment, allowing remote users in the field to search a central repository of images. Examples of classification performance and typical queries are provided.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127155882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The integration of automatic segmentation and motion tracking for 4D reconstruction and visualization of musculoskeletal structures","authors":"Jose Gerardo Tamez-Peña, K. Parker, S. Totterman","doi":"10.1109/BIA.1998.692437","DOIUrl":"https://doi.org/10.1109/BIA.1998.692437","url":null,"abstract":"The 3D reconstruction of high contrast anatomical structures has been widely explored. However, a class of important clinical problems involves the motion of very complex musculoskeletal structures including the joints, hence a 4D reconstruction is desired. Practical difficulties with 4D reconstruction with MRI include the time required for data acquisition, the resolution required for visualization of small but critical structures, the gross inhomogeneities of field coil response, the degree of noise present with the signal and the extreme low-contrast details between some distinct anatomical structures. The authors present a comprehensive approach to 4D musculoskeletal imagery that address the above challenges. Specific MRI imaging protocols; segmentation, motion estimation and motion tracking algorithms are developed and applied to render complex 4D musculoskeletal systems. Applications of the approach include the analysis of the rotation of the upper arm and the knee extension.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127321612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automatic motion analysis of the tongue surface from ultrasound image sequences","authors":"Y. Sinan Akgul, C. Kambhamettu, M. Stone","doi":"10.1109/BIA.1998.692417","DOIUrl":"https://doi.org/10.1109/BIA.1998.692417","url":null,"abstract":"The authors present a system for automatic 2D analysis of the tongue movement from digital ultrasound image sequences. The system focuses on extraction, tracking and analysis of the tongue surface during speech production and swallowing. The input to the system is provided by a Head and Transducer Support System (HATS), which is developed for use in ultrasound imaging of tongue movement. The authors developed a novel active contour (snakes) model that uses several adjacent images during the extraction of the tongue surface contour for an image frame. The user supplies an initial contour model for a single image frame in the sequence. This initial contour is a form of expert knowledge input to the system, which is used to find the candidate contour points in the adjacent images. Subsequently, the new snake mechanism is applied to estimate optimal contours for each image frame using these candidate points. Finally, the system uses a postprocessing method to refine the positions of the contours by utilizing more spatiotemporal information. The authors extended their previous work by applying the system to different speech and swallowing sequences using various constraints. The extended system can also extract qualitative local deformations with only a minimal computational overhead which may be useful for the diagnosis of Cerebellar Ataxic disorder. The authors tested the system on several different speech and swallowing sequences produced by HATS. During the tests, the authors saw that the system is flexible enough to be used in a wide variety of cases. In addition, visual inspection of the detected contours by the speech experts confirms that the results are very promising and this system can be effectively employed in speech and swallowing research.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sulcal basins and sulcal strings as new concepts for describing the human cortical topography","authors":"G. Lohmann, D. V. Cramon","doi":"10.1109/BIA.1998.692384","DOIUrl":"https://doi.org/10.1109/BIA.1998.692384","url":null,"abstract":"Human brain mapping aims at establishing correspondences between brain function and brain anatomy. One of the most intriguing problems in this field is the high inter-personal variability of human neuroanatomy which makes studies across many subjects very difficult. The cortical folds (\"sulci\") often serve as landmarks that help to establish correspondences between subjects. Here, the authors present a method that both automatically detects and attributes neuroanatomical names to the cortical folds using image analysis methods applied to magnetic resonance data of human brains. The authors claim that the cortical folds can be subdivided into a number of substructures which they call sulcal basins. In addition to the concept of sulcal basins, the authors introduce the concept of sulcal strings which are groups of sulcal basins arranged as strings that are aligned with the Sylvian fissure and the inter-hemispheric cleft. The concept of sulcal basins allows to establish a complete parcellation of the cortical surface into separate regions. These regions are neuroanatomically meaningful and can be identified from MR data sets across many subjects. At the same time, the parcellation is detailed enough to be useful for brain mapping purposes.","PeriodicalId":261632,"journal":{"name":"Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128948715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}