Medical image segmentation algorithm based on feedback mechanism convolutional neural network

Abstract

The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors and textures in the image. It not only consumes a lot of time and effort, but also requires certain expertise to obtain useful feature information which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, Convolutional Neural Networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Therefore, inspired by the feedback mechanism of human visual cortex, we give a deep research on how to build a computational model of feedback mechanism in deep convolutional neural networks. And effective feedback mechanism calculation models and operation frameworks are proposed. In this paper, to solve the feedback optimization problem, we propose two new algorithms based on the greedy strategy. We analyze the functional differences of these two algorithms, and propose a new feedback convolutional neural network algorithm based on the neuron pathway pruning and pattern information recovering algorithms. For the problem that it is difficult to find and extract effective features in medical image segmentation, we propose a medical image segmentation algorithm based on feedback mechanism convolutional neural network. The basic idea is as follows. Firstly, using the unlabeled image block sample training, learning and extracting the deep features of the image to construct feedback mechanism convolutional neural network models. Then, using the model to classify the pixel block samples in the medical image to be segmented, and the initial regions of the image is obtained. Finally, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method not only has high segmentation accuracy, but also has extremely high adaptive segmentation ability for various medical images.