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Segmentation and reconstruction on 3D microscopic images is an important yet challenging problem in biomedical imaging, and many approaches have been proposed for different imaging settings (e.g., [3, 4]).
The recognition of acute leukemia blast cells in colored microscopic images is a challenging task.
The result, compared to the manual way of diagnosing microscopic images, is simple for diagnostic process and provides better decision support for malaria and blood cell counting, as well as speed up the diagnosis process.
The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.
However, it is practically difficult to determine a feasible threshold that gives proper segmentation all over the image because the background level of typical microscopic images is not uniform due to light scattering and non-uniform illumination.
Nevertheless, complete correspondence between endocytoscopic images and microscopic images is still limited [ 13].
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Immunostained sections of the mice were observed under a Nikon Eclipse E800 microscope, and light microscopic images were acquired with a digital camera (Nikon DXM1200C).
In two other studies, actual microscopic images were investigated, using microscope navigation data instead of eye movements to study visual processing.
At ten days after explanting, all wells were visualised using the Nikon microscope and camera and light microscopic images were taken of migrating cells from the explants.
Fluorescent microscopic images were visualized on an inverted microscope (Axio Imager M1 microscope, Zeiss, Göttingen, Germany).
Microscopic images were acquired using an inverted microscope (IX 73, Olympus) with a charge-coupled device (CCD) camera (DP80, Olympus).
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