Micro Image Cell Recognition Based On Wavelet Transform

The computer medical image has become more and more powerful in clinic diagnosis and therapy. How to analyze the micro image automatically is a very important research field in medical image processing and analysis, which can win the diagnosis time for the doctor and increase the accuracy of the diagnosis.Because the wavelet transform has different focus for different scale layer, people can set different threshold value for the cell edge and background noise by using the self adaptive time-frequency feature of the wavelet according to characters of the micro images. Then the cell edge can be effectively separated by separating the modulo angle. So the cell identification and quantitative analysis for the micro image is realized by performing circular arc algorithm to the cell edge.1. Multiscale analysis and wavelet transformThe key to construct a wavelet base in a space is to find its corresponding generatorof the multiscale analysis. The third degree B-spline is more suitable for the micro image processing which has been proved in practice. To use the analysis algorithm, the discrete sample value data for M+1 scale must be given and the data on the M scale layer must be computed. The process and formulas of computing the analysis coefficients are as follows: = + + +The corresponding reconstruction algorithm is as follows: ＝ ＋＋＋In the orthonormal wavelet analysis process, it is not convenient to find the original data at the price of complicated computation. So here we simplify the question by let ＝, That is let the sample value of the fine scale layer be the original data. The hypothesis has been proved to be feasible and reasonable in the practice and theory.The images for different scales usually have been soothed. We consider the gradient vector :＝Because the direction of the gradient vector atindicates the maximum absolute value of the image in this direction, the multiscale edge of the image is the local extreme value of the 2-d wavelet transform and the edge points are the inflexions of the curve .In scale, define the amplitude of the gradient vector as and the angle as .angle is equal to the angle of the gradient in the horizontal direction. We compute the extreme value of the gradient vector in the angle direction, which is the edge point of the image. 2. Noise removal of the micro imageThis paper put forward a method to select the threshold value of the weighted combination. For the low frequency in the signal wavelet analysis, we first search for the corresponding wavelet transform modulo value in every scale layer of the mutant point, then after finding the modulo value of each mutant point, compute the modulo values in four directions: 0,45,90,135, by the formulawhere is the output value,is the modulo value in four direction,is the difference between the modulo value in the ith direction and the modulo value of the point. We set a threshold value for the new modulo value. If the modulo point is greater than the threshold value, keep the transform value of this point and its neighborhood as its value, while the modulo point is zero if it is less than the threshold value. That is the detail part in the signal analysis which only detains the wavelet transform value corresponding with the position of the mutant points while the other wavelet transform values are substituted by zero. This method is different from the ones which use different threshold value for different scale layer, which lead to better representation of the mutant part in the signal, elimination of the noise behavior and better feedback to the feedback signal for each scale layer and the effect of the noise removal is obvious.3. Edge extraction of the micro imageBecause the edge is the discontinuous point of the image gray grade which has singularity, image edge can be detected by searching for the maximum of the wavelet coefficient modulo. The modulo of the image 2-d wavelet transform is in dire...