Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints

DOI10.1155/2019/4625371zbMath1435.94028OpenAlexW2945275406WikidataQ127938776 ScholiaQ127938776MaRDI QIDQ2298452

Jiawei Lai, Hongqing Zhu, Xiaofeng Ling

Publication date: 20 February 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2019/4625371

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• TensorFlow
• GitHub
• U-Net
• PRMLT

Cites Work

• Unnamed Item
• Unnamed Item
• Robust clustering by deterministic agglomeration EM of mixtures of multivariate \(t\)-distributions
• Image segmentation using a trimmed likelihood estimator in the asymmetric mixture model based on generalized gamma and Gaussian distributions
• A tutorial on the cross-entropy method
• Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval
• A Bayesian Framework for Image Segmentation With Spatially Varying Mixtures

This page was built for publication: Segmentation of brain MR images by using fully convolutional network and Gaussian mixture model with spatial constraints