The no-reference image quality assessment is receiving more and more attention. With no need for clear images as a reference during the evaluation process, it has a wide range of application scenarios. By analyzing th...
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Brain extraction from Magnetic resonance imaging (MRI) is the separation of brain tissue from non-brain tissue in MRI brain images and removal of extra brain tissue. Automatic extraction of brain tissue is an importan...
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The no-reference image quality assessment is receiving more and more attention. With no need for clear images as a reference during the evaluation process, it has a wide range of application scenarios. By analyzing th...
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ISBN:
(数字)9781728123455
ISBN:
(纸本)9781728123462
The no-reference image quality assessment is receiving more and more attention. With no need for clear images as a reference during the evaluation process, it has a wide range of application scenarios. By analyzing the characteristics of the defocused image and combining with the Human Visual System (HVS), this paper proposed a No-Reference Visual System Index (NRVSI) approach for the image quality assessment of defoused images. Both the qualitative and quantitative assessments demonstrated the performance of the proposed method.
Brain extraction from Magnetic resonance imaging (MRI) is the separation of brain tissue from non-brain tissue in MRI brain images and removal of extra brain tissue. Automatic extraction of brain tissue is an importan...
ISBN:
(数字)9781728123455
ISBN:
(纸本)9781728123462
Brain extraction from Magnetic resonance imaging (MRI) is the separation of brain tissue from non-brain tissue in MRI brain images and removal of extra brain tissue. Automatic extraction of brain tissue is an important step in neuroimage processing for clinical applications and scientific research. Accurate delineation of the region can be challenged due to the weak contrast between the brain and non-brain tissue. To overcome this issue, this paper proposed a novel brain extraction approach integrating the boundary information under the deep neural network framework. The boundary of the brain tissues was first detected by an edge detection neural network. The inception module was then utilized in the convolutional stage to capture multi-scale features. A dense decoder shortcut connection structure was finally introduced to leverage the multi-level features in the upsampling stage. The proposed method can achieve Dice overlap coefficients of 98.15% and 98.09%, for the OASIS and LPBA40 datasets respectively. The proposed method outperforms the commonly used brain extraction tools by qualitative and quantitative assessments on two publicly available benchmark datasets.
One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize...
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