PCNN-based method for de-noising wavelet domain ultrasonic medical image

Abstract

The invention discloses a PCNN-based method for de-noising a wavelet domain ultrasonic medical image, which comprises the following steps: firstly, performing logarithmic transformation and wavelet transformation on a noise image and the corresponding pretreatment on a wavelet coefficient; secondly, processing the wavelet coefficient by using a PCNN method and performing the corresponding post-treatment on the wavelet coefficient; and finally, performing the inverse wavelet transformation and exponential transformation on the wavelet coefficient subjected to the post-treatment to obtain a de-noised image. Compared with wavelet de-noise, the method makes the edge of the de-noised image clearer, well retains image details and improves a signal to mean square error ratio; and compared with PCNN, the method overcomes the drawback that during the removal of speckle noises, the PCNN has difficulty in determining model parameters and step length and realizes a higher signal to the mean square error ratio after image de-noise.

Description

technical field [0001] The invention relates to a wavelet domain ultrasonic medical image denoising method, in particular to a PCNN-based wavelet domain ultrasonic medical image denoising method. Background technique [0002] Ultrasound medical imaging has become one of the important means of clinical medical auxiliary diagnosis because of its advantages of no damage to the human body, real-time display of organs, low cost, and convenient use. However, the speckle noise generated in the process of ultrasound imaging reduces the quality of the image and makes it difficult to distinguish the image detail information. Since the detail information of ultrasound medical images plays a key role in clinical auxiliary diagnosis, therefore, while removing speckle noise, Preserving the detailed information of ultrasonic medical images has become an important research topic in the field of ultrasonic medical images. [0003] Pulse Coupled Neural Network (PCNN) is a new type of neural ...

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Problems solved by technology

However, for images polluted by speckle noise, since PCNN cannot accurately determine the exact position of the noise, its denoising effect is not ideal.

[0004] Wavelet transform is considered to be an effective tool for recovering signals. The advantage of wavelet transform is that it can generate coefficients containing significant characteristics of input information and can perform coarse and fine level-by-level multi-resolution analysis on signals. The signal / mean square error ratio of the post image is high, but the existing various wavelet-based denoising algorithms will cause the edge of the image to be blurred to some extent (see literature: [6] Tian Yong, Guo Jianzheng, Ma Yide, etc. Wavelet Comparison and combination of transformation and PCNN in image processing [J] Gansu Science Journal, 2006, 12(4): 53-55)

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