Interactive edge detection and tracing in cellular nuclei images using B-splines and neural network

Barrios, V.; Reigosa, Aldo; Montilla, G.; Hernández, Luis Alberto Morales; Torres, J.; Bonsjak, A.

doi:10.1109/iembs.1995.575224

Cited by 1 publication

(2 citation statements)

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“…A number of image analysis methods have been developed for nuclei segmentation [1][2][3][4][5][6][7][8][9]. For example, a statistical model, based on Compact Hough Transform, likelihood function and global grey-level histogram information, have been proposed in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Tsapatsoulis, et al, proposed an image analysis system for the automated detection of breast cancer nuclei using block-based processing followed by a singular value decomposition of each block [6]. Barrios et al used B-splines and neural network for interactive edge detection and tracing in cellular nuclei images [7]. A machine learning method, which is based on Haar Wavelets was proposed for detection of the molecular particles in live cells in [8].…”

Section: Introductionmentioning

confidence: 99%

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Neuronal nuclei localization in 3D using level set and watershed segmentation from laser scanning microscopy images

et al. 2008

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Abnormalities of the number and location of cells are hallmarks of both developmental and degenerative neurological diseases. However, standard stereological methods are impractical for assigning each cell's nucleus position within a large volume of brain tissue. We propose an automated approach for segmentation and localization of the brain cell nuclei in laser scanning microscopy (LSM) embryonic mouse brain images. The nuclei in these images are first segmented by using the level set (LS) and watershed methods in each optical plane. The segmentation results are further refined by application of information from adjacent optical planes and prior knowledge of nuclear shape. Segmentation is then followed with an algorithm for 3D localization of the centroid of nucleus (CN). Each volume of tissue is thus represented by a collection of centroids leading to an approximate 10,000-fold reduction in the data set size, as compared to the original image series. Our method has been tested on LSM images obtained from an embryonic mouse brain, and compared to the segmentation and CN localization performed by an expert. The average Euclidian distance between locations of CNs obtained using our method and those obtained by an expert is 1.58±1.24 µm, a value well within the ~5 µm average radius of each nucleus. We conclude that our approach accurately segments and localizes CNs within cell dense embryonic tissue.

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