Abstract
Image segmentation in medical imaging has long been a problem in radiological image processing. Most of the image segmentation methods in traditional vision algorithms are difficult to achieve high-resolution image segmentation due to the complexity of the algorithm. This article proposes an image segmentation method based on an optimized cellular neural network. This method introduces a non-linear template and data quantization on the basis of a basic network model, which greatly reduces the computational complexity while maintaining the accuracy of image segmentation. We then applied the method to a computer-aided system to classify tumor lesions in mammograms. Finally, we propose an FPGA-based multilevel optimization architecture for energy-efficient cellular neural networks. The optimization scheme includes three levels: system level, module level, and design space. This solution improves computing performance by increasing system parallelism, using data reuse technology to fully utilize loading bandwidth, and using data quantization to reduce computational redundancy. It also introduces pipeline and dual cache structures to optimize memory access, and analyzes the limited resources through the Roofline model. System for best performance. The experimental results show that the FPGA accelerator in this article can improve unit performance by 34% compared with other existing research work. The nonlinear quantified cellular neural network proposed in this article can reduce LUT resource consumption by 74% and energy of 48.2%. Compared with the original network, in the two projection position segmentation results of the mammogram, only 1.5% and 0.6% of the accuracy loss, respectively.
Highlights
Robots can partially replace humans or cooperate with humans to accomplish many tasks [1]
There are two linear voltage-controlled current sources I xy and I xu in linear cellular neural networks, which are defined as A(ij, kl)vykl and B(ij; kl)vukl respectively
FPGA-BASED IMAGE SEGMENTATION METHOD ACCELERATES IMPLEMENTATION Based on the basic solution of FPGA hardware implementation, we optimize the system level, module level and design space using FPGA respectively, run the cellular neural network with the same structure, and analyze the performance under the same hardware resource constraints
Summary
Robots can partially replace humans or cooperate with humans to accomplish many tasks [1]. With the development of science and technology, robots have been widely used in industrial, agricultural, national defense, medical, disaster relief, entertainment and other fields. Robots have been widely used in medical fields and have achieved success [2]. Computer-aided diagnostic system refers to an automatic or semi-automatic system that uses computer technology to help radiologists detect abnormal symptoms. The system usually uses X-ray imaging to make early clinical preventive diagnosis of suspicious conditions [3]. Computer-assisted diagnostic systems can help doctors detect early symptoms, such as detecting and analyzing suspicious masses in X-ray images, and classifying malignant breast tumors from a large number of benign masses [4]. The current computer-aided diagnosis system cannot completely replace manual
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