Method of segmenting single neuron images with high-dynamic-range thresholds and computer readable storage medium thereof

A method of segmenting single neuron images with high-dynamic-range thresholds, comprising:

• preparing a biological tissue sample containing neurons and performing single three-dimensional imaging to said biological tissue sample containing neurons to obtain a single three-dimensional raw neuroimage;
• filtering out voxels in said three-dimensional raw neuroimage whose signal intensities are below a first signal intensity threshold, a second signal intensity threshold, . . . , a n-th signal intensity threshold respectively by a filtering module to obtain a first filtered image, a second filtered image, . . . , a n-th filtered image;
• tracing said first filtered image, said second filtered image, . . . , said n-th filtered image for their skeleton by a tracing module respectively to obtain a first traced image, a second traced image, . . . , a n-th traced image;
• calculating structural importance scores of every voxel in said first traced image, said second traced image, . . . , said n-th traced image by a score calculating module respectively by utilizing an equation to obtain a first structural importance score, a second structural importance score, . . . , a n-th structural importance score of every voxel;
• summing up said first structural importance score, said second structural importance score, . . . , and said n-th structural importance score of every voxel by a summing module to obtain a summed structural importance score of every voxel;
• determining a structural importance score threshold;
• and
• filtering out voxels in said three-dimensional raw neuroimage whose summed structural importance scores are below said structural importance score threshold by said filtering module to obtain a single neuron three-dimensional image, wherein n is a positive integer which is larger than 3.