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“一图胜千言”
为图像处理课程设计(IP)旨在研究生和大四本科生,在任何领域的工程,这本书从第一章概述如何安装摄像头成像雷达的核磁共振成像和猫形态图像,然后继续覆盖广泛的图像处理主题。IP主题包括:图像插值、放大、缩略图和锐化、边缘检测、噪声滤波、模糊图像的去模糊、监督和非监督学习、图像分割等。
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【百度-提取码: nise 】Image Processing For Engineers, Yagle and Ulaby.
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Medical image segmentation have drawn massive attention as it is important in biomedical image analysis. Good segmentation results can assist doctors with their judgement and further improve patients' experience. Among many available pipelines in medical image analysis, Unet is one of the most popular neural networks as it keeps raw features by adding concatenation between encoder and decoder, which makes it still widely used in industrial field. In the mean time, as a popular model which dominates natural language process tasks, transformer is now introduced to computer vision tasks and have seen promising results in object detection, image classification and semantic segmentation tasks. Therefore, the combination of transformer and Unet is supposed to be more efficient than both methods working individually. In this article, we propose Transformer-Unet by adding transformer modules in raw images instead of feature maps in Unet and test our network in CT82 datasets for Pancreas segmentation accordingly. We form an end-to-end network and gain segmentation results better than many previous Unet based algorithms in our experiment. We demonstrate our network and show our experimental results in this paper accordingly.
