Neuron positioning abnormity layer processing method and system based on histogram and medium

Abstract

The invention discloses a neuron positioning abnormity layer processing method and system based on a histogram and a medium. According to the number of layers and the number of calculation nodes, input data is grouped. Each computing node allocates and initializes variables and storage space, independently executes anomaly detection and processing tasks based on the block histogram of the first three layers within the belonging range, and then independently executes the anomaly detection and processing tasks based on the block histogram of the subsequent layer data until all the layers are processed. The invention can effectively detect the abnormal layer in the neuron positioning input data, has the characteristics of high detection speed and high expandability, can be deployed on a distributed computing platform, realizes flexibility and supports parameter configuration, and is easy to transplant and popularize.

Description

technical field [0001] The present invention relates to the image preprocessing of the analysis method of the fine structure of the neural circuit, in particular to a method, system and medium for processing abnormal layers of neuron positioning based on histograms, which are used to realize the abnormal positioning of neurons on a distributed multi-core computing platform Layer detection and processing. Background technique [0002] Neural circuit information is the key to understanding brain function and the mechanism of brain diseases. How to realize the automatic tracking of neural circuit big data is one of the key scientific problems faced by research in the field of neurology such as brain science. Neuron localization is the key to the analysis of neural circuit data. Accurate location of neuron cell bodies is obtained through the analysis of neural circuit image data, which is the basis for subsequent quantitative analysis. [0003] With the continuous improvement o...

AI Technical Summary

Problems solved by technology

In the process of imaging image acquisition and storage, due to certain errors in the system, there is a certain probability that some wrong layers will be generated, which directly affects the completeness and accuracy of neuron positioning in the later stage

How to efficiently detect and process abnormal layers from hundreds of thousands of layers and hundreds of terabytes of image data is a huge challenge in image processing, and it has become a bottleneck that restricts the transformation of acquired data into knowledge.

Images