Browsing by Subject "Image registration"

Now showing 1 - 4 of 4
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    Active geometric model : multi-compartment model-based segmentation & registration
    (2014-08-26) Mukherjee, Prateep; Tsechpenakis, Gavriil; Raje, Rajeev; Tuceryan, Mihran
    We present a novel, variational and statistical approach for model-based segmentation. Our model generalizes the Chan-Vese model, proposed for concurrent segmentation of multiple objects embedded in the same image domain. We also propose a novel shape descriptor, namely the Multi-Compartment Distance Functions or mcdf. Our proposed framework for segmentation is two-fold: first, several training samples distributed across various classes are registered onto a common frame of reference; then, we use a variational method similar to Active Shape Models (or ASMs) to generate an average shape model and hence use the latter to partition new images. The key advantages of such a framework is: (i) landmark-free automated shape training; (ii) strict shape constrained model to fit test data. Our model can naturally deal with shapes of arbitrary dimension and topology(closed/open curves). We term our model Active Geometric Model, since it focuses on segmentation of geometric shapes. We demonstrate the power of the proposed framework in two important medical applications: one for morphology estimation of 3D Motor Neuron compartments, another for thickness estimation of Henle's Fiber Layer in the retina. We also compare the qualitative and quantitative performance of our method with that of several other state-of-the-art segmentation methods.
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    Digital Image Analysis Tools Developed by the Indiana O’Brien Center
    (Frontiers Media, 2021-12-16) Dunn, Kenneth W.; Medicine, School of Medicine
    The scale and complexity of images collected in biological microscopy have grown enormously over the past 30 years. The development and commercialization of multiphoton microscopy has promoted a renaissance of intravital microscopy, providing a window into cell biology in vivo. New methods of optical sectioning and tissue clearing now enable biologists to characterize entire organs at subcellular resolution. New methods of multiplexed imaging support simultaneous localization of forty or more probes at a time. Exploiting these exciting new techniques has increasingly required biomedical researchers to master procedures of image analysis that were once the specialized province of imaging experts. A primary goal of the Indiana O'Brien Center has been to develop robust and accessible image analysis tools for biomedical researchers. Here we describe biomedical image analysis software developed by the Indiana O'Brien Center over the past 25 years.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images
    (Elsevier, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data. Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume. Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines. Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration. Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images (Publisher’s Note)
    (SPIE, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    The note corrects an error that appeared in Equation 14 of the originally published article.