基于特征提取和稀疏表示的医学图像融合。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2017-01-01 Epub Date: 2017-02-21 DOI:10.1155/2017/3020461
Yin Fei, Gao Wei, Song Zongxi
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引用次数: 28

摘要

作为一种新型的多尺度几何分析工具,稀疏表示与传统的图像表示方法相比具有许多优点。然而,标准稀疏表示没有考虑固有结构及其时间复杂度。本文提出了一种基于稀疏表示和决策映射的多模态医学图像融合机制来同时处理这些问题。设计了结构信息图(SM)和能量信息图(EM)以及结构和能量图(SEM)三种决策图,使结果保留更多的能量和边缘信息。SM包含由拉普拉斯高斯函数(LOG)捕获的局部结构特征,EM包含由均方差检测到的能量和能量分布特征。在基于正态稀疏表示的方法中加入决策映射,提高了算法的速度。该方法通过增强对比度和保留更多源图像的结构和能量信息,提高了融合结果的质量。36组CT/MR、MR- t1 /MR- t2和CT/PET图像的实验结果表明,基于SR和SEM的方法优于5种最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Medical Image Fusion Based on Feature Extraction and Sparse Representation.

Medical Image Fusion Based on Feature Extraction and Sparse Representation.

Medical Image Fusion Based on Feature Extraction and Sparse Representation.

Medical Image Fusion Based on Feature Extraction and Sparse Representation.

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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