Automatic Segmentation of Breast Tumor in Ultrasound Image with Simplified PCNN and Improved Fuzzy Mutual Information Image segmentation is very important in the field of image processing. The pulse coupled neural network (PCNN) has been efficiently applied to image processing, especially for image segmentation. In this study, a simplified PCNN (SPCNN) model is proposed, the fuzzy mutual information (FMI) is improved as optimization criterion for SPCNN, and then the SPCNN and improved FMI (IFMI) based segmentation algorithm is proposed and applied for the segmentation of breast tumor in ultrasound image. To validate the proposed algorithm, a comparative experiment is implemented to segment breast images not only by our proposed algorithm, but also by the improved CV algorithm, the maxentropybased PCNN algorithm, the MIbased PCNN algorithm, and the IFMIbased PCNN algorithm. The results show that the breast lesions are well segmented by the proposed algorithm without image preprocessing, with the mean Hausdorff of distance of 5.631xc2xb10.822, mean average minimum Euclidean distance of 0.554xc2xb10.049, mean Tanimoto coefficient of 0.961xc2xb10.019, and mean misclassified error of 0.038xc2xb10.004. These values of evaluation indices are better than those of other segmentation algorithms. The results indicate that the proposed algorithm has excellent segmentation accuracy and strong robustness against noise, and it has the potential for breast ultrasound computeraided diagnosis (CAD).

Research of Multimodal Medical Image Fusion Based on ParameterAdaptive PulseCoupled Neural Network and Convolutional Sparse Representation Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion results due to the loss of detailed feature information during fusion. To deal with it, this paper proposes a new multimodal medical image fusion method based on the imaging characteristics of medical images. In the proposed method, the nonsubsampled shearlet transform (NSST) decomposition is first performed on the source images to obtain highfrequency and lowfrequency coefficients. The highfrequency coefficients are fused by a parameterxe2x80x90adaptive pulsecoupled neural network (PAPCNN) model. The method is based on parameter adaptive and optimized connection strength       adopted to promote the performance. The lowfrequency coefficients are merged by the convolutional sparse representation (CSR) model. The experimental results show that the proposed method solves the problems of difficult parameter setting and poor detail preservation of sparse representation during image fusion in traditional PCNN algorithms, and it has significant advantages in visual effect and objective indices compared with the existing mainstream fusion algorithms.