Segmentation of Breast Mass and Diagnosis of Benign and Malignant Breast Tumors Based on Edge Constraint in Pulse Coupled Neural Network

In order to segment breast tumor accurately, an improved Unit-Linking Pulse-Coupled Neural Networks based mammography image segmentation method is proposed. Firstly, the link input and coupled parameter in the original model are improved according to the relationship between this neuron and its neighbors. Then, the improved model is used to segment the breast tumor image to obtain multiple output images. Finally, the gradient algorithm is used to calculate the edges of the original image and each output image respectively, and the minimum mean square error (MMSE) of the two edge images is calculated to find the best output image. The final experimental results indicate that the improved method can accurately segment breast tumor images in different environments. In addition, based on the segmentation results, we use the SVM method to diagnose the type of tumor, and its classification accuracy is much higher than the existing deep classification algorithm.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.