Multiresolution multiscale active mask segmentation of fluorescence microscope images

Gowri Srinivasa; Matthew Fickus; Jelena Kovačević

doi:10.1117/12.825776

24 August 2009 Multiresolution multiscale active mask segmentation of fluorescence microscope images

Gowri Srinivasa, Matthew Fickus, Jelena Kovačević

Proceedings Volume 7446, Wavelets XIII; 744603 (2009) https://doi.org/10.1117/12.825776
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images.

Citation Download Citation

Gowri Srinivasa, Matthew Fickus, and Jelena Kovačević "Multiresolution multiscale active mask segmentation of fluorescence microscope images", Proc. SPIE 7446, Wavelets XIII, 744603 (24 August 2009); https://doi.org/10.1117/12.825776

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