Martin Christlieb

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The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package(More)
Segmenting transparent phase objects, such as biological cells from brightfield microscope images, is a difficult problem due to the lack of observable intensity contrast and noise. Previous image analysis solutions have used excessive de-focusing or physical models to obtain the underlying phase properties. Here, an improved cell boundary detection(More)
Segmentation of transparent cells in brightfield microscopy images could facilitate the quantitative analysis of corresponding fluorescence images. However, this presents a challenge due to irregular morphology and weak intensity variation, particularly in ultra-thin regions. A boundary detection technique is applied to a series of variable focus images(More)
A novel signal processing-oriented approach to solving problems involving inverse Laplacians is introduced. The Monogenic Signal is a powerful method of computing the phase of discrete signals in image data, however it is typically used with band-pass filters in the capacity of a feature detector. Substituting low-pass filters allows the Monogenic Signal to(More)
The increased resistance of hypoxic cells to all forms of cancer therapy presents a major barrier to the successful treatment of most solid tumors. Inhibition of the essential kinase Checkpoint kinase 1 (Chk1) has been described as a promising cancer therapy for tumors with high levels of hypoxia-induced replication stress. However, as inhibition of Chk1(More)
The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in bright-field images. The package(More)
Performing segmentation and registration on images of transparent phase objects, such as biological cells from brightfield microscope images, is a difficult problem due to the lack of observable intensity contrast and noise. Previous image analysis solutions have used excessive defocusing or physical models to obtain the underlying phase properties. Here,(More)
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