Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions

Real-time content-aware video retargeting on the Android platform for tunnel vision assistance

As mobile devices continue to rise in popularity, advances in overall mobile device processing power lead to further expansion of their capabilities. This, coupled with the fact that many people suffer from low vision, leaves substantial room for advancing mobile development for low vision assistance. Computer vision is capable of assisting and accommodating individuals with blind spots or tunnel vision by extracting the necessary information and presenting it to the user in a manner they are able to visualize. Such a system would enable individuals with low vision to function with greater ease. Additionally, offering assistance on a mobile platform allows greater access. The objective of this thesis is to develop a computer vision application for low vision assistance on the Android mobile device platform. Specifically, the goal of the application is to reduce the effects tunnel vision inflicts on individuals. This is accomplished by providing an in-depth real-time video retargeting model that builds upon previous works and applications. Seam carving is a content-aware retargeting operator which defines 8-connected paths, or seams, of pixels. The optimality of these seams is based on a specific energy function. Discrete removal of these seams permits changes in the aspect ratio while simultaneously preserving important regions. The video retargeting model incorporates spatial and temporal considerations to provide effective image and video retargeting. Data reduction techniques are utilized in order to generate an efficient model. Additionally, a minimalistic multi-operator approach is constructed to diminish the disadvantages experienced by individual operators. In the event automated techniques fail, interactive options are provided that allow for user intervention. Evaluation of the application and its video retargeting model is based on its comparison to existing standard algorithms and its ability to extend itself to real-time. Performance metrics are obtained for both PC environments and mobile device platforms for comparison

Exploration, Registration, and Analysis of High-Throughput 3D Microscopy Data from the Knife-Edge Scanning Microscope

Advances in high-throughput, high-volume microscopy techniques have enabled the acquisition of extremely detailed anatomical structures on human or animal organs. The Knife-Edge Scanning Microscope (KESM) is one of the first instruments to produce sub-micrometer resolution ( ~1 µm^(3)) data from whole small animal brains. We successfully imaged, using the KESM, entire mouse brains stained with Golgi (neuronal morphology), India ink (vascular network), and Nissl (soma distribution). Our data sets fill the gap of most existing data sets which have only partial organ coverage or have orders of magnitude lower resolution. However, even though we have such unprecedented data sets, we still do not have a suitable informatics platform to visualize and quantitatively analyze the data sets. This dissertation is designed to address three key gaps: (1) due to the large volume (several tera voxels) and the multiscale nature, visualization alone is a huge challenge, let alone quantitative connectivity analysis; (2) the size of the uncompressed KESM data exceeds a few terabytes and to compare and combine with other data sets from different imaging modalities, the KESM data must be registered to a standard coordinate space; and (3) quantitative analysis that seeks to count every neuron in our massive, growing, and sparsely labeled data is a serious challenge. The goals of my dissertation are as follows: (1) develop an online neuro-informatics framework for efficient visualization and analysis of the multiscale KESM data sets, (2) develop a robust landmark-based 3D registration method for mapping the KESM Nissl-stained entire mouse data into the Waxholm Space (a canonical coordinate system for the mouse brain), and (3) develop a scalable, incremental learning algorithm for cell detection in high-resolution KESM Nissl data. For the web-based neuroinformatics framework, I prepared multi-scale data sets at different zoom levels from the original data sets. And then I extended Google Maps API to develop atlas features such as scale bars, panel browsing, and transparent overlay for 3D rendering. Next, I adapted the OpenLayers API, which is a free mapping and layering API supporting similar functionality as the Google Maps API. Furthermore, I prepared multi-scale data sets in vector-graphics to improve page loading time by reducing the file size. To better appreciate the full 3D morphology of the objects embedded in the data volumes, I developed a WebGL-based approach that complements the web-based framework for interactive viewing. For the registration work, I adapted and customized a stable 2D rigid deformation method to map our data sets to the Waxholm Space. For the analysis of neuronal distribution, I designed and implemented a scalable, effective quantitative analysis method using supervised learning. I utilized Principal Components Analysis (PCA) in a supervised manner and implemented the algorithm using MapReduce parallelization. I expect my frameworks to enable effective exploration and analysis of our KESM data sets. In addition, I expect my approaches to be broadly applicable to the analysis of other high-throughput medical imaging data

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Metodologias para caracterização de tráfego em redes de comunicações

Tese de doutoramento em Metodologias para caracterização de tráfego em redes de comunicaçõesInternet Tra c, Internet Applications, Internet Attacks, Tra c Pro ling, Multi-Scale Analysis abstract Nowadays, the Internet can be seen as an ever-changing platform where new and di erent types of services and applications are constantly emerging. In fact, many of the existing dominant applications, such as social networks, have appeared recently, being rapidly adopted by the user community. All these new applications required the implementation of novel communication protocols that present di erent network requirements, according to the service they deploy. All this diversity and novelty has lead to an increasing need of accurately pro ling Internet users, by mapping their tra c to the originating application, in order to improve many network management tasks such as resources optimization, network performance, service personalization and security. However, accurately mapping tra c to its originating application is a di cult task due to the inherent complexity of existing network protocols and to several restrictions that prevent the analysis of the contents of the generated tra c. In fact, many technologies, such as tra c encryption, are widely deployed to assure and protect the con dentiality and integrity of communications over the Internet. On the other hand, many legal constraints also forbid the analysis of the clients' tra c in order to protect their con dentiality and privacy. Consequently, novel tra c discrimination methodologies are necessary for an accurate tra c classi cation and user pro ling. This thesis proposes several identi cation methodologies for an accurate Internet tra c pro ling while coping with the di erent mentioned restrictions and with the existing encryption techniques. By analyzing the several frequency components present in the captured tra c and inferring the presence of the di erent network and user related events, the proposed approaches are able to create a pro le for each one of the analyzed Internet applications. The use of several probabilistic models will allow the accurate association of the analyzed tra c to the corresponding application. Several enhancements will also be proposed in order to allow the identi cation of hidden illicit patterns and the real-time classi cation of captured tra c. In addition, a new network management paradigm for wired and wireless networks will be proposed. The analysis of the layer 2 tra c metrics and the di erent frequency components that are present in the captured tra c allows an e cient user pro ling in terms of the used web-application. Finally, some usage scenarios for these methodologies will be presented and discussed