Cell tracking using a distributed algorithm for 3‐D image segmentation

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

“…To select low-entropy regions by using this spatial and temporal information, we used an object-tracking algorithm [18-20] (Figure 4). The tracking algorithm was composed of the following two recursive processes.…”

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

“…To select low-entropy regions by using this spatial and temporal information, we used an object-tracking algorithm [18-20] (Figure 4). The tracking algorithm was composed of the following two recursive processes.…”

3D culture model and computer-assisted videomicroscopy to analyze migratory behavior of noninvasive and invasive bronchial epithelial cells

“…Automatic tracking of objects in 4D data sets has been the subject of several studies (9). Good results can be expected only when the number of objects is small and the trajectories of these objects do not interfere (2,8,23). When the number of objects in the analyzed volume is greater than several tens and the trajectories of these objects can come close to each other, we do not believe that the fully automatic tracking approaches can be used, because the risk of making errors becomes nonnegligible.…”

Tumor cell identification using features rules

“…There have been a number of attempts to automate the process of tumor cell counting [1][2][3][4][5][6][7][8][9][10]. Many of these attempts focus on using pure image processing techniques to identify the tumor cells.…”

“…They are categorized into statistical classification methods [6,7,9], region growing methods [3,4], and boundary methods [5,8,10].…”

“…Awasthi et al [3] used a combination of multiple thresholding, dilation morphology operation and region growing methods to perform cell segmentation. Berns et al [4] used a combination of median filter, local histogram, and morphology filter with watershed method to achieve the same goal of cell segmentation.…”

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

“…To select low-entropy regions by using this spatial and temporal information, we used an object-tracking algorithm [18-20] (Figure 4). The tracking algorithm was composed of the following two recursive processes.…”

3D culture model and computer-assisted videomicroscopy to analyze migratory behavior of noninvasive and invasive bronchial epithelial cells

“…Automatic tracking of objects in 4D data sets has been the subject of several studies (9). Good results can be expected only when the number of objects is small and the trajectories of these objects do not interfere (2,8,23). When the number of objects in the analyzed volume is greater than several tens and the trajectories of these objects can come close to each other, we do not believe that the fully automatic tracking approaches can be used, because the risk of making errors becomes nonnegligible.…”

Tumor cell identification using features rules

“…There have been a number of attempts to automate the process of tumor cell counting [1][2][3][4][5][6][7][8][9][10]. Many of these attempts focus on using pure image processing techniques to identify the tumor cells.…”

“…They are categorized into statistical classification methods [6,7,9], region growing methods [3,4], and boundary methods [5,8,10].…”

“…Awasthi et al [3] used a combination of multiple thresholding, dilation morphology operation and region growing methods to perform cell segmentation. Berns et al [4] used a combination of median filter, local histogram, and morphology filter with watershed method to achieve the same goal of cell segmentation.…”

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

“…To select low-entropy regions by using this spatial and temporal information, we used an object-tracking algorithm [18-20] (Figure 4). The tracking algorithm was composed of the following two recursive processes.…”

3D culture model and computer-assisted videomicroscopy to analyze migratory behavior of noninvasive and invasive bronchial epithelial cells

“…Automatic tracking of objects in 4D data sets has been the subject of several studies (9). Good results can be expected only when the number of objects is small and the trajectories of these objects do not interfere (2,8,23). When the number of objects in the analyzed volume is greater than several tens and the trajectories of these objects can come close to each other, we do not believe that the fully automatic tracking approaches can be used, because the risk of making errors becomes nonnegligible.…”

Tumor cell identification using features rules

“…There have been a number of attempts to automate the process of tumor cell counting [1][2][3][4][5][6][7][8][9][10]. Many of these attempts focus on using pure image processing techniques to identify the tumor cells.…”

“…They are categorized into statistical classification methods [6,7,9], region growing methods [3,4], and boundary methods [5,8,10].…”

“…Awasthi et al [3] used a combination of multiple thresholding, dilation morphology operation and region growing methods to perform cell segmentation. Berns et al [4] used a combination of median filter, local histogram, and morphology filter with watershed method to achieve the same goal of cell segmentation.…”