7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.
4650146
A New Coarse-to-Fine Framework for 3D Brain MR Image Registration
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.
15282622
Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions
17432425
Structure analysis for dynamic software architecture
The open and dynamic Internet environment greatly urges software entities that are distributed on different locations to coordinate with each other to accomplish a computing task. Software architecture is applied to abstract the software entities to be components and the coordination between them to be connectors and then a model is extracted as the architecture on which the design, analysis and verification are based. Currently, the notion of dynamic software architectures that can modify their architecture and enact modifications during the system execution has become one of the most active research areas. In this paper, we focus on the dynamic evolution of system structure other than coordination mechanisms (e.g. communication protocols). It is widely recognized that some restrictions should be imposed on the system evolution to ensure that the system structure may remain one style or transform within a scope. These conditions, to a large extent, make the system execute under control as expected.
17774823
Scalable Partitioning and Exploration of Chemical Spaces Using Geometric Hashing
Virtual screening (VS) has become a preferred tool to augment high-throughput screening(1) and determine new leads in the drug discovery process. The core of a VS informatics pipeline includes several data mining algorithms that work on huge databases of chemical compounds containing millions of molecular structures and their associated data. Thus, scaling traditional applications such as classification, partitioning, and outlier detection for huge chemical data sets without a significant loss in accuracy is very important. In this paper, we introduce a data mining framework built on top of a recently developed fast approximate nearest-neighbor-finding algorithm(2) called locality-sensitive hashing (LSH) that can be used to mine huge chemical spaces in a scalable fashion using very modest computational resources. The core LSH algorithm hashes chemical descriptors so that points close to each other in the descriptor space are also close to each other in the hashed space. Using this data structure, one can perform approximate nearest-neighbor searches very quickly, in sublinear time. We validate the accuracy and performance of our framework on three real data sets of sizes ranging from 4337 to 249 071 molecules. Results indicate that the identification of nearest neighbors using the LSH algorithm is at least 2 orders of magnitude faster than the traditional k-nearest-neighbor method and is over 94% accurate for most query parameters. Furthermore, when viewed as a data-partitioning procedure, the LSH algorithm lends itself to easy parallelization of nearest-neighbor classification or regression. We also apply our framework to detect outlying (diverse) compounds in a given chemical space; this algorithm is extremely rapid in determining whether a compound is located in a sparse region of chemical space or not, and it is quite accurate when compared to results obtained using principal-component-analysis-based heuristics.
10160655
A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry
Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.
295636
Agent-Based Forwarding Strategies for Reducing Location Management Cost in Mobile Networks
An important issue in location management for dealing with user mobility in wireless networks is to reduce the cost associated with location updates and searches. The former operation occurs when a mobile user moves to a new location registration area and the network is being informed of the mobile user's current location; the latter operation occurs when there is a call for the mobile user and the network must deliver the call to the mobile user. In this paper, we propose and analyze a class of new agent-based forwarding schemes with the objective to reduce the location management cost in mobile wireless networks. We develop analytical models to compare the performance of the proposed schemes with existing location management schemes to demonstrate their feasibility and also to reveal conditions under which our proposed schemes are superior to existing ones. Our proposed schemes are particularly suitable for mobile networks with switches which can cover a large number of location registration areas.
2057857
Gene-finding via tandem mass spectrometry
We propose a new gene-finding methodology that combines high performance liquid chromatograph (HPLC)-tandem mass spectrometry experiments with a fast computer algorithm to locate coding regions and introns. Proteins are first extracted from cells and digested by enzymes, and then the resulting peptides are separated and analyzed by HPLC-tandem mass spectrometry. We designed an algorithm to find DNA coding sequences, corresponding to open reading frames (ORF), in the genome such that their translated amino acid sequences are optimally correlated with these tandem mass spectra. In this algorithm, we also allow one gap, corresponding to an intron, between two DNA coding sequences, such that their concatenation becomes one coding sequence. Finally, the algorithm assembles these candidate coding sequences and introns into gene structures. Our algorithm was implemented to predict genes on 4 contigs with a total of 123 kbps using two sets of simulated digestion- HPLC-tandem mass spectrometry data of 2523 Caenorhabditis elegans Chromosome IV proteins, digested by trypsin and Asp-N respectively. Among 15 annotated genes in the forward strand, all 98 exons are hit by the predicted no-gap coding sequences, and 60 out of 83 introns are correctly predicted. We also tested gene structure prediction in a contig containing 3 genes. Combining splicing site predictions with predicted coding sequences and introns, we found all 3 gene structures.