Abstract: An auxiliary prediction system is provided to predict reliability of an object after a specific operation is applied to the target object. The auxiliary prediction system includes an image correction module and an analysis module. The image correction module performs an image correction procedure to convert an original image of the target object into a first correction image. The analysis module performs a feature analysis on the first correction image through an artificial intelligence model that has been trained, so as to predict whether the target object has a defect or not after the specific operation.

Abstract: An auxiliary prediction system is provided to predict reliability of an object after a specific operation is applied to the target object. The auxiliary prediction system includes an image correction module and an analysis module. The image correction module performs an image correction procedure to convert an original image of the target object into a first correction image. The analysis module performs a feature analysis on the first correction image through an artificial intelligence model that has been trained, so as to predict whether the target object has a defect or not after the specific operation.

Abstract: The method of segmenting single neuron images with high-dynamic-range thresholds of the present invention includes (a) preparing a biological tissue sample containing neurons and performing imaging to this sample to obtain a three-dimensional raw neuroimage; (b) deleting voxels in the three-dimensional raw neuroimage with signal intensities below a first signal intensity threshold to obtain a first thresholded image; (c) tracing the first thresholded image to obtain a first traced image; (d) calculating a structural importance score of every voxel in the first traced image to obtain a first structural importance score of every voxel; (e) gradually increasing the signal intensity threshold and repeating (b), (c) and (d) n?1 times; (f) summing up all the n structural importance scores of every voxel; (g) deleting voxels with summed structural importance score smaller than a pre-determined value from the raw image to obtain the segmented single neuron.

Abstract: The method of segmenting single neuron images with high-dynamic-range thresholds of the present invention includes (a) preparing a biological tissue sample containing neurons and performing imaging to this sample to obtain a three-dimensional raw neuroimage; (b) deleting voxels in the three-dimensional raw neuroimage with signal intensities below a first signal intensity threshold to obtain a first thresholded image; (c) tracing the first thresholded image to obtain a first traced image; (d) calculating a structural importance score of every voxel in the first traced image to obtain a first structural importance score of every voxel; (e) gradually increasing the signal intensity threshold and repeating (b), (c) and (d) n?1 times; (f) summing up all the n structural importance scores of every voxel; (g) deleting voxels with summed structural importance score smaller than a pre-determined value from the raw image to obtain the segmented single neuron.

Abstract: This invention provides an automatic tracing algorithm for quantitative analysis of continuous structures, such as the images of tree-like or network-like structures. The algorithm includes the steps of encoding 3-D image voxels by using a source field encoding methodology followed by a defined image threshold, tracing the codelets along encoded voxels such that the characteristic element of a 3-D image such as the center line of fiber, fiber branch, loop, and end point can be determined systematically, and achieving the automatic analysis without manual intervention. In addition, quantitative measurements are exquisitely calculated by the location and distance of these characteristic elements between coded voxels. The algorithm is more suitable to automatically analyze the 2D/3D images of complex neurons, blood vessels, collagens in skin tissue, and fibril morphology in polymeric materials.

Abstract: This invention provides an automatic tracing algorithm for quantitative analysis of continuous structures, such as the images of tree-like or network-like structures. The algorithm includes the steps of encoding 3-D image voxels by using a source field encoding methodology followed by a defined image threshold, tracing the codelets along encoded voxels such that the characteristic element of a 3-D image such as the center line of fiber, fiber branch, loop, and end point can be determined systematically, and achieving the automatic analysis without manual intervention. In addition, quantitative measurements are exquisitely calculated by the location and distance of these characteristic elements between coded voxels. The algorithm is more suitable to automatically analyze the 2D/3D images of complex neurons, blood vessels, collagens in skin tissue, and fibril morphology in polymeric materials.