Chinese medicinal material microscopic image feature extraction and recognition retrieval method

Abstract

The invention discloses a Chinese medicinal material microscopic image feature extraction and recognition retrieval method. A PCNN time matrix information processing method is introduced, image feature information in a transform domain is extracted through combination of PCNN and image Fourier transform and decimal power exponent filtering phase, and a Chinese medicinal material microscopic image information rapid storage and recognition retrieval method is put forward on the basis of mahalanobis distance with combination of comprehensive similarity measure of a Pearson product moment correlation method so that a holographic Chinese medicinal material microstructure image and pollen image feature extraction and recognition retrieval system is constructed, objectivity, accuracy, repeatability and intelligent degree of Chinese medicinal material quality evaluation can be further enhanced, and a new approach is provided for modernization of Chinese medicinal material detection and analysis.

Description

technical field [0001] The invention belongs to the field of biomedical information processing, and in particular relates to a method for feature extraction, recognition and retrieval of microscopic images of Chinese herbal medicines. Background technique [0002] Pulse-coupled neural network (PCNN) is proposed based on the synchronous pulse emission phenomenon in the visual cortex of mammals such as cats and monkeys. It has a good biological background. The model has dynamic variable threshold, nonlinear modulation coupling, synchronous pulse The characteristics of pulse emission and space-time summation make PCNN show great advantages in signal processing applications, especially in image processing applications. However, the traditional PCNN model still has the following theoretical and technical shortcomings: [0003] (1) In terms of nonlinear modulation coupling and threshold exponential decay, the threshold decay of this model is repeatedly changed. This threshold cha...

AI Technical Summary

Problems solved by technology

[0003] (1) In terms of nonlinear modulation coupling and threshold exponential decay, the threshold decay of this model is repeatedly changed. This threshold change cannot well meet the nonlinear exponential requirements of the human eye's response to brightness, and through this threshold law A large amount of information in the processed image (or other signal) is contained in the activation period (frequency) or activation phase of neurons, but the output image does not contain all available information;

[0004] (2) The existence of a large number of leakage integrators and some feedback connections in the PCNN model improves the approximation of the model bionics and the authenticity of biological processing information, but this not only increases the complexity of the model, but also increases the accuracy of the model. Signal processing time overhead;

[0005] (3) There are too many parameters in the traditional PCNN model, and many difficulties will be added to the (automatic) setting and optimization of parameters;

[0006] (4) Due to the complexity and particularity of microscopic images of Chinese medicinal materials, the traditional PCNN model is not suitable for processing microscopic tissue images

Images