Clustering algorithms by minimizing an object function share a clear drawback that the number of clusters need to be set manually. Although density peak clustering is able to seek the number of clusters, it suffers from memory overflow when it is used for image segmentation because a moderate-size image usually includes a large number of pixels leading to a huge similarity matrix. To address the issue, here we proposed an automatic fuzzy clustering framework (AFCF) for image segmentation. The proposed framework has threefold contributions. Firstly, the idea of superpixel is used for the density peak (DP) algorithm, which efficiently reduces the size of the similarity matrix and thus improves the computational efficiency of the DP algorithm. Secondly, we employ a density balance algorithm to obtain a more robust decision-graph that helps the DP algorithm to achieve fully automatic clustering. Finally, a fuzzy c-means clustering based on prior entropy is used in the framework to improve image segmentation results. Because the spatial neighboring information of both the pixels and membership are considered, the final segmentation result is improved effectively. Experiments show that the proposed framework is not only able to achieve automatic image segmentation, it also provides better segmentation results than state-of-the-art algorithms.Index Terms-Fuzzy clustering, image segmentation, superpixel, density peak (DP) algorithm Tao Lei (M'17) received the Ph.D degree in Information and Communication Engineering from Northwestern
Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. On the other hand, the fittest hyperparameters that can adapt to source data properties (e.g., sparsity, noisy features) are not able to be quickly identified for target data properties. For instance, the realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in applying existing methods directly and creating effective denoising neural networks easily. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures automatically. We expedite the evolutionary process through an experiencebased greedy exploration strategy and transfer learning. Our evolutionary algorithm procedure has flexibility, which allows taking advantage of current state-of-the-art modules (e.g., residual blocks) to search for promising neural networks. We evaluate our framework on a classic medical image analysis task: denosing. The experimental results on computed tomography perfusion (CTP) image denoising demonstrates the capability of the method to select the fittest genes for building highperformance networks, named EvoNets. Our results outperform state-of-the-art methods consistently at various noise levels.
The deep convolutional neural networks and residual networks have shown great success and high-quality reconstruction for single image super-resolution. It is clearly seen that among the bestknown super-resolution models, deep learning-based methods demonstrate state-of-the-art performance. In this paper, we propose a deep differential convolutional network (DCN) for single image superresolution (SRDCN). The proposed DCN is a novel convolutional network, which is composed of convolutional layers, parametric rectified linear units (PReLU), and the identity skip connection. Different from other deep learning-based methods which complete the reconstruction by learning the mapping function between low-resolution and high-resolution images, the proposed algorithm makes changes to the way of reconstruction. In the proposed network, we use DCN to obtain the reconstructed images and the differences between the low-resolution and reconstructed images in the reconstruction process. Then the differences combined with the original low-resolution image and the reconstructed image that from the last DCN are used for final reconstruction. In addition, the loss function is more rationally designed and optimized in this paper. The proposed loss function contains three parts of loss: feature loss, style loss, and mean squared error (MSE) loss. These losses will be used to supervise the structure and content of the reconstructed image. The experimental results prove that the proposed model is superior to many state-of-the-art super-resolution methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM).INDEX TERMS Deep convolutional neural networks, differential convolutional network, single image super-resolution, peak signal-to-noise ratio, structure similarity index metrics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.