LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 5 of total 5

Search options

  1. Article ; Online: COMPUTATIONAL 2D and 3D MEDICAL IMAGE DATA COMPRESSION MODELS.

    Boopathiraja, S / Punitha, V / Kalavathi, P / Surya Prasath, V B

    Archives of computational methods in engineering : state of the art reviews

    2021  Volume 29, Issue 2, Page(s) 975–1007

    Abstract: In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission ... ...

    Abstract In this world of big data, the development and exploitation of medical technology is vastly increasing and especially in big biomedical imaging modalities available across medicine. At the same instant, acquisition, processing, storing and transmission of such huge medical data requires efficient and robust data compression models. Over the last two decades, numerous compression mechanisms, techniques and algorithms were proposed by many researchers. This work provides a detailed status of these existing computational compression methods for medical imaging data. Appropriate classification, performance metrics, practical issues and challenges in enhancing the two dimensional (2D) and three dimensional (3D) medical image compression arena are reviewed in detail.
    Language English
    Publishing date 2021-05-07
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2276736-8
    ISSN 1886-1784 ; 1134-3060
    ISSN (online) 1886-1784
    ISSN 1134-3060
    DOI 10.1007/s11831-021-09602-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Decision level integration of unimodal and multimodal single cell data with scTriangulate.

    Li, Guangyuan / Song, Baobao / Singh, Harinder / Surya Prasath, V B / Leighton Grimes, H / Salomonis, Nathan

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 406

    Abstract: Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple ... ...

    Abstract Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Rather than ensemble approaches which select the "consensus", scTriangulate picks the most stable solution through coalitional iteration. When evaluated on diverse multimodal technologies, scTriangulate outperforms alternative approaches to identify high-confidence cell-populations and modality-specific subtypes. Unlike existing integration strategies that rely on modality-specific joint embedding or geometric graphs, scTriangulate makes no assumption about the distributions of raw underlying values. As a result, this approach can solve unprecedented integration challenges, including the ability to automate reference cell-atlas construction, resolve clonal architecture within molecularly defined cell-populations and subdivide clusters to discover splicing-defined disease subtypes. scTriangulate is a flexible strategy for unified integration of single-cell or multimodal clustering solutions, from nearly unlimited sources.
    MeSH term(s) Cluster Analysis ; Algorithms
    Language English
    Publishing date 2023-01-25
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-36016-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article: Confocal Vessel Structure Segmentation with Optimized Feature Bank and Random Forests.

    Kassim, Yasmin M / Surya Prasath, V B / Glinskii, Olga V / Glinsky, Vladislav V / Huxley, Virginia H / Palaniappan, Kannappan

    IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop

    2017  Volume 2016

    Abstract: In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the ... ...

    Abstract In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better segmentation results in challenging imaging conditions. We obtain binary segmentations using random forest classifier trained on physiologists marked ground-truth. Experimental results on mice dura mater confocal microscopy vessel segmentations indicate that we obtain better results compared to global segmentation approaches.
    Language English
    Publishing date 2017-08-17
    Publishing country United States
    Document type Journal Article
    ISSN 2164-2516
    ISSN 2164-2516
    DOI 10.1109/AIPR.2016.8010580
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article ; Online: Multiquadric Spline-Based Interactive Segmentation of Vascular Networks.

    Meena, Sachin / Surya Prasath, V B / Kassim, Yasmin M / Maude, Richard J / Glinskii, Olga V / Glinsky, Vladislav V / Huxley, Virginia H / Palaniappan, Kannappan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2015  Volume 2016, Page(s) 5913–5916

    Abstract: Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or ... ...

    Abstract Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.
    MeSH term(s) Algorithms ; Animals ; Dura Mater/blood supply ; Image Processing, Computer-Assisted/methods ; Mice ; Microvessels/anatomy & histology ; Microvessels/diagnostic imaging ; Optical Imaging/methods
    Language English
    Publishing date 2015-12-22
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2016.7592074
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images.

    Kassim, Yasmin M / Surya Prasath, V B / Pelapur, Rengarajan / Glinskii, Olga V / Maude, Richard J / Glinsky, Vladislav V / Huxley, Virginia H / Palaniappan, Kannappan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2015  Volume 2016, Page(s) 2901–2904

    Abstract: Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for ... ...

    Abstract Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.
    MeSH term(s) Algorithms ; Animals ; Dura Mater/anatomy & histology ; Dura Mater/blood supply ; Image Processing, Computer-Assisted/methods ; Mice ; Microvessels/anatomy & histology ; Microvessels/physiology ; Optical Imaging/methods ; Optical Imaging/mortality ; Vascular Remodeling
    Language English
    Publishing date 2015-12-22
    Publishing country United States
    Document type Journal Article
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC.2016.7591336
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top