Medical Image Fusion Based on Feature Extraction and Sparse Representation

Yin, Fei; Gao, Wei; Song, Zongxi

doi:10.1155/2017/3020461

Cited by 26 publications

(18 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, the B-spline and mutual information algorithm can 't achieve the manual registration of images [27].Our future research will also try to combine the mutual information metric with user intervention of the anatomical landmark de nition to allow doctors to control the registration accuracy of local ne-scale structures. Function optimization technology can be used to solve the problem of feature extraction and registration in addition to the transformation of model parameters and optimization of the algorithm to improve the accuracy of automatic registration [28][29].…”

Section: Limitationmentioning

confidence: 99%

Construction of a registration and fusion of unreformed thin-sectional high-resolution sectional anatomical image (Chinese Visible Human images) and CT and MRI images based on B-spline and mutual information and its application in segmenting nasopharyngeal structures in Treatment Planning System (TPS)

Yang

Zhang

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

Background To help radiotherapy doctors recognize and segment the nasopharyngeal organs in risk of Nasopharyngeal carcinoma (NPC) and make radiotherapy plan. Materials/Methods: Based on the continuous thin-layer, high-precision, high-resolution and true-color sectional anatomical data (Chinese Visible Human (CVH) images),we used B-spline and mutual information to transform, register and fuse the CVH images with the patient's personalized CT images, and integrated them into the Treatment Planning System (TPS). Consequently, Three-Dimensional Visualization Treatment Planning System (3DV + TPS) was created. To verify it, 3DV + TPS was deployed to identify and segment the nasopharyngeal organs in risk of NPC, and a questionnaire was filled out by radiotherapy doctors. Results Result shows that 3DV + TPS can finish registration and fusion of 4 sets of sectional anatomical images and individual CT images of patients in approximately 3 minutes and 50 seconds. Conclusion The registered and fused images can accurately reflect the position, outline and adjacent space of the nasopharyngeal structure which is not clear in the CT images. Thus, it is helpful for recognizing and segmenting neural, muscular and glandular structures. Through automatically registering and fusing of color images and CT gray images, 3DV + TPS improves the accuracy and efficiency of recognizing nasopharyngeal structures in making radiotherapy plan, and it is useful to improve the teaching quality of tumor radiotherapy for medical students and interns as well.

show abstract

Section: Limitationmentioning

confidence: 99%

Construction of a registration and fusion of unreformed thin-sectional high-resolution sectional anatomical image (Chinese Visible Human images) and CT and MRI images based on B-spline and mutual information and its application in segmenting nasopharyngeal structures in Treatment Planning System (TPS)

Yang

Zhang

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Mr Ling N. et al (2016) [6], in this research used Fusion for Medical Images based on Shearlet Transform and Compressive Sensing Model. Yin Fei et al (2017) [7] a new combination 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 to include structure information map (SIM) and energy information map (EIM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information.…”

Section: Issn: 0067-2904mentioning

confidence: 99%

“…These techniques make on fused the different information between different medical images. Which includes is the High Frequency Intensity Modulation Technique (HIFM) [10,11].…”

Section: Combination Techniquesmentioning

confidence: 99%

Analytical Study of Medical Image Combination Techniques

Abbas

H.Mahdy

Al-Zuky

et al. 2018

ijs

View full text Add to dashboard Cite

Image combination is a technique that fuses two or more medical images taken with different conditions or imaging devices into a single image contain complete information. In this study relied on mathematical, statistical and spatial techniques, to fuse MRI images that captured horizontal and vertical times (T1, T2), and applied a method of supervised classification based on the minimum distance before and after combination process, then examine the quality of the resulting image based on the statistical standards resulting from the analysis of edge analysis, showing the results to identify the best techniques adopted in combination process, determine the exact details in each class and between classes.

show abstract

“…Many of the existing algorithm has been used for IF process such as Electrical Capacitance Tomography (ECT) algorithm [8], Non-Subsample Contour let Transform (NSCT) [9], sparse representation and decision [10], Curvelet transform [11], hybrid Entropy concept [12], hybrid Dual tree complex wavelet transform [13], and hybrid IF and image registration [14]. The main problem with these methods is information loss.…”

Section: Introductionmentioning

confidence: 99%

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Borra¹,

Panakala²,

Kumar³

2019

IJIES

View full text Add to dashboard Cite

Nowadays, all the medical diagnosis is achieved by using the Digital Image Processing (DIP). Because, the usage of DIP is more important in the medical field to identify the activities of the patients related to various diseases. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) scan images are used to perform the fusion process. In brain medical image, MRI scan is used to show the brain structural information without functional data. But, CT scan image is included the functional data with brain activity. To improve the low dose CT scan, Hybrid algorithm is introduced in this paper which is implemented in FPGA. The main objective of this work is to optimize performances of the hardware. This work is implemented in FPGA. The combination of Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) is known as hybrid algorithm. The Maximum Selection Rule (MSR) is used to select the high frequency component from DWT. These three algorithms have RTL architecture which is implemented by Verilog code. Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) performances analysed for the different methods. In 180nm technology, DWT-PCA-IF architecture achieved 5.145mm2 area, 298.25mW power, and 124ms delay. From the fused medical image, Mean, Standard Deviation (SD), Entropy, and Mutual information (MI) performances are evaluated for DWT-PCA method which has better performance than conventional methods.

show abstract

Medical Image Fusion Based on Feature Extraction and Sparse Representation

Cited by 26 publications

References 38 publications

Analytical Study of Medical Image Combination Techniques

VLSI Implementation of Image Fusion Using DWT- PCA Algorithm with Maximum Selection Rule

Contact Info

Product

Resources

About