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We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell(More)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field(More)
Some cell detection approaches which deal with bright-field microscope images utilize defocussing to increase the image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a modified monogenic signal framework.(More)
Defocusing is used in bright-field image processing in order to increase image contrast. Moreover, defocused images can be used to solve the transport of intensity equation (TIE) and obtain physical light phase. Recently, it was shown that the monogenic local features of an axial intensity derivative passed through a specific low-pass filter can be used to(More)
PURPOSE Several cell detection approaches which deal with bright-field microscope images utilize defocusing to increase image contrast. The latter is related to the physical light phase through the transport of intensity equation (TIE). Recently, it was shown that it is possible to approximate the solution of the TIE using a low-pass monogenic signal(More)
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