Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis
The diagnosis of brain cancer relies heavily on medical imaging techniques, with MRI being the most commonly used. It is necessary to perform automatic segmentation of brain tumors on MRI images. This project intends to build an MRI algorithm based on U-Net. The residual network and the module used...
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| creator | Hu, Yuxiang Yang, Haowei Xu, Ting He, Shuyao Yuan, Jiajie Deng, Haozhang |
| description | The diagnosis of brain cancer relies heavily on medical imaging techniques,
with MRI being the most commonly used. It is necessary to perform automatic
segmentation of brain tumors on MRI images. This project intends to build an
MRI algorithm based on U-Net. The residual network and the module used to
enhance the context information are combined, and the void space convolution
pooling pyramid is added to the network for processing. The brain glioma MRI
image dataset provided by cancer imaging archives was experimentally verified.
A multi-scale segmentation method based on a weighted least squares filter was
used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of
three-dimensional reconstruction is further improved. Experiments show that the
local texture features obtained by the proposed algorithm are similar to those
obtained by laser scanning. The algorithm is improved by using the U-Net method
and an accuracy of 0.9851 is obtained. This approach significantly enhances the
precision of image segmentation and boosts the efficiency of image
classification. |
| doi_str_mv | 10.48550/arxiv.2406.18548 |
| format | Article |
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with MRI being the most commonly used. It is necessary to perform automatic
segmentation of brain tumors on MRI images. This project intends to build an
MRI algorithm based on U-Net. The residual network and the module used to
enhance the context information are combined, and the void space convolution
pooling pyramid is added to the network for processing. The brain glioma MRI
image dataset provided by cancer imaging archives was experimentally verified.
A multi-scale segmentation method based on a weighted least squares filter was
used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of
three-dimensional reconstruction is further improved. Experiments show that the
local texture features obtained by the proposed algorithm are similar to those
obtained by laser scanning. The algorithm is improved by using the U-Net method
and an accuracy of 0.9851 is obtained. This approach significantly enhances the
precision of image segmentation and boosts the efficiency of image
classification.</description><identifier>DOI: 10.48550/arxiv.2406.18548</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2024-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2406.18548$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2406.18548$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Yuxiang</creatorcontrib><creatorcontrib>Yang, Haowei</creatorcontrib><creatorcontrib>Xu, Ting</creatorcontrib><creatorcontrib>He, Shuyao</creatorcontrib><creatorcontrib>Yuan, Jiajie</creatorcontrib><creatorcontrib>Deng, Haozhang</creatorcontrib><title>Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis</title><description>The diagnosis of brain cancer relies heavily on medical imaging techniques,
with MRI being the most commonly used. It is necessary to perform automatic
segmentation of brain tumors on MRI images. This project intends to build an
MRI algorithm based on U-Net. The residual network and the module used to
enhance the context information are combined, and the void space convolution
pooling pyramid is added to the network for processing. The brain glioma MRI
image dataset provided by cancer imaging archives was experimentally verified.
A multi-scale segmentation method based on a weighted least squares filter was
used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of
three-dimensional reconstruction is further improved. Experiments show that the
local texture features obtained by the proposed algorithm are similar to those
obtained by laser scanning. The algorithm is improved by using the U-Net method
and an accuracy of 0.9851 is obtained. This approach significantly enhances the
precision of image segmentation and boosts the efficiency of image
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with MRI being the most commonly used. It is necessary to perform automatic
segmentation of brain tumors on MRI images. This project intends to build an
MRI algorithm based on U-Net. The residual network and the module used to
enhance the context information are combined, and the void space convolution
pooling pyramid is added to the network for processing. The brain glioma MRI
image dataset provided by cancer imaging archives was experimentally verified.
A multi-scale segmentation method based on a weighted least squares filter was
used to complete the 3D reconstruction of brain tumors. Thus, the accuracy of
three-dimensional reconstruction is further improved. Experiments show that the
local texture features obtained by the proposed algorithm are similar to those
obtained by laser scanning. The algorithm is improved by using the U-Net method
and an accuracy of 0.9851 is obtained. This approach significantly enhances the
precision of image segmentation and boosts the efficiency of image
classification.</abstract><doi>10.48550/arxiv.2406.18548</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis |
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