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The manual monitoring process of cancer cells development is a subjective, time consuming process, as it typically relies on the visual recognition and experience level of the pathologists. An automated nuclei segmentation and quantification in Immunohistologically stained images have remained a challenging task. Previous methods used have shown an oversight in the segmentation and counting of the...
Edge detection, especially from colour images, plays very important roles in many applications for image analysis, segmentation and recognition. Most existing methods extract colour edges via fusing edges detected from each colour components or detecting from the intensity image where inter-component information is ignored. In this study, an improved method on colour edge detection is proposed in...
Optical coherence tomography (OCT) is a rapidly emerging imaging modality that can provide non-invasive, cross-sectional, high-resolution images of tissue morphology in situ and in real-time. Previous studies have demonstrated that OCT can non-invasively visualize the pathological changes in the living kidney in real-time using the Munich-Wistar rat model. In this work, we demonstrate, for the first...
We present the application of optical coherence tomography (OCT) for non-destructive imaging and quantitative characterization of the macroporous cyclic acetal hydrogel cell scaffolds. High-resolution OCT visualizes the microstructures of the tissue engineering scaffolds in three-dimension. The porosity, pore size, and the interconnectivity of the hydrogel scaffolds were characterized quantitatively...
It remains a challenge to automatically segment video objects consistent with human visual perception and content understanding. Based on extensive literature survey on existing video object segmentation algorithms, we propose in this paper an integrated approach for semantic video object segmentation, where shape cues in successive frames is taken into account in exploiting the principle of change...
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