Automatic Segmentation Algorithm of Ultrasound Heart Image Based on convolutional neural network and Image Saliency

The emergence of 4D heart images makes the data volume of the images multiply. It is more urgent to require an effective and fast segmentation algorithm. Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary. Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence. Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image. Convolutional neural network is a special deep neural network model of artificial intelligence. Its connections between neurons are not fully connected. The weights of connections between certain neurons in the same layer are shared, and the network model is reduced. The complexity reduces the number of weights. The use of visual saliency techniques to achieve cardiac segmentation based on CT images. An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique. In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved GrabCut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image. At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect. The convolutional neural network is constructed to realize the positioning function of the heart in the image. The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed. A stacking noise reduction self-coding network is constructed, and the network is manually segmented. Training, realize the classification and recognition of the pixels belonging to the heart tissue in the CT image of the heart, and finally realize the segmentation of the heart image based on the classification result. The results of the above segmentation algorithm are quantitatively evaluated and analyzed with the artificial segmentation results, and the segmentation results are visually reconstructed by surface rendering and volume rendering. The algorithm has better accuracy, reliability and higher. The segmentation efficiency is more simplified for user operations.