K. Palaniappan, F. Bunyak, S. Nath, and J. Goffeney

High-Throughput Image Reconstruction and Analysis, Artech House Publishers, pgs. 39--85, 2009

parallelization, gpu, cell detection, cell segmentation, cell tracking, machine learning, biomedical

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Abstract

The first step in cell behavior analysis is cell detection. Cell detection returns an initial contour close to actual cell boundaries, that is later refined by the cell seg- mentation process. A wide range of techniques have been applied for detecting and segmenting biological objects of interest in video microscopy imagery includ- ing spatially adaptive thresholding, morphological watershed, mean shift, active contours, graph cuts, clustering, multidimensional classifiers like neural networks, and genetic algorithms. Various factors such as type of the cells, environmental conditions, and imaging characteristics such as zoom factor affect the appearance of cells, and thus choice of detection method. But in general, features used in cell detection can be grouped into three categories: intensity-based, texture-based, and spatio-temporal features. Intensity-based detection approaches (i.e., intensity thresholding [1, 2] or clustering [3]) are suitable for lower resolution or stained images where cells or nuclei appear semi-homogeneous and have distinct intensity patterns compared to the background. For high-resolution unstained images where interior of the cells appear highly heterogeneous or where interior and exterior in- tensity distributions are similar, features based on spatial texture (i.e., ridgeness measures in [4]) or features based on spatiotemporal features or motion are needed. In the following section, we describe the flux tensor framework for accurate detection of moving objects (which in this context correspond to moving cells) in time lapse images that effectively handles accurate detection of nonhomoge- neous cells in the presence of complex biological processes, background noise, and clutter.