76,892 research outputs found
Medical Image Modality Synthesis and Resolution Enhancement Based on Machine Learning Techniques
To achieve satisfactory performance from automatic medical image analysis
algorithms such as registration or segmentation, medical imaging data with
the desired modality/contrast and high isotropic resolution are preferred, yet
they are not always available. We addressed this problem in this thesis using
1) image modality synthesis and 2) resolution enhancement.
The first contribution of this thesis is computed tomography (CT)-tomagnetic
resonance imaging (MRI) image synthesis method, which was developed
to provide MRI when CT is the only modality that is acquired. The
main challenges are that CT has poor contrast as well as high noise in soft
tissues and that the CT-to-MR mapping is highly nonlinear. To overcome these
challenges, we developed a convolutional neural network (CNN) which is a
modified U-net. With this deep network for synthesis, we developed the first
segmentation method that provides detailed grey matter anatomical labels on
CT neuroimages using synthetic MRI.
The second contribution is a method for resolution enhancement for a
common type of acquisition in clinical and research practice, one in which
there is high resolution (HR) in the in-plane directions and low resolution (LR)
in the through-plane direction. The challenge of improving the through-plane resolution for such acquisitions is that the state-of-art convolutional neural
network (CNN)-based super-resolution methods are sometimes not applicable
due to lack of external LR/HR paired training data. To address this challenge,
we developed a self super-resolution algorithm called SMORE and its iterative
version called iSMORE, which are CNN-based yet do not require LR/HR
paired training data other than the subject image itself. SMORE/iSMORE
create training data from the HR in-plane slices of the subject image itself, then
train and apply CNNs to through-plane slices to improve spatial resolution
and remove aliasing. In this thesis, we perform SMORE/iSMORE on multiple
simulated and real datasets to demonstrate their accuracy and generalizability.
Also, SMORE as a preprocessing step is shown to improve segmentation
accuracy.
In summary, CT-to-MR synthesis, SMORE, and iSMORE were demonstrated
in this thesis to be effective preprocessing algorithms for visual quality
and other automatic medical image analysis such as registration or segmentation
Projection image-to-image translation in hybrid X-ray/MR imaging
The potential benefit of hybrid X-ray and MR imaging in the interventional
environment is large due to the combination of fast imaging with high contrast
variety. However, a vast amount of existing image enhancement methods requires
the image information of both modalities to be present in the same domain. To
unlock this potential, we present a solution to image-to-image translation from
MR projections to corresponding X-ray projection images. The approach is based
on a state-of-the-art image generator network that is modified to fit the
specific application. Furthermore, we propose the inclusion of a gradient map
in the loss function to allow the network to emphasize high-frequency details
in image generation. Our approach is capable of creating X-ray projection
images with natural appearance. Additionally, our extensions show clear
improvement compared to the baseline method.Comment: In proceedings of SPIE Medical Imaging 201
Implementation of an Improved Image Enhancement Algorithm on FPGA
Image processing plays very crucial role in this digital human world and has rapidly evolved with the development of computers, mathematics and the real-life demand of variety of applications in wide range of areas. This wide range of areas includes remote sensing, machine/ robot vision, pattern recognition, medical diagnosis, video processing, military, agriculture, television, etc. Image processing has two important components which are image enhancement and information extraction. Since image enhancement works at the front end with the initial raw inputs, it works like a backbone in image processing. When it comes to implementing these image enhancement techniques and developing applications, these tasks are bit demanding in the choice of processing units because the demand of high resolution. This emerges the necessity of a high speed, powerful and cost-effective processing unit. In this thesis we present an improved image enhancement algorithm in terms of performance and its implementation on FPGA as they satiates the necessity of high speed, powerful and cost-effective processing unit by providing flexibility, parallelization, pipelining and reconfigurability. We have performed a high level synthesis by using MATLAB and implemented an improved image enhancement algorithm on Cyclone V by using Quartus Prime. We have considered an X-ray image size of 1000x1920p for implementation and achieved decent PSNR values and hardware resource utilization along with the better visual interpretability by our proposed improvements. For achieving a better execution time and power consumption we also offer the task parallelism for the algorithm
Implementation Of An Improved Image Enhancement Algorithm On FPGA
Image processing plays very crucial role in this digital human world and has rapidly evolved with the development of computers, mathematics and the real-life demand of variety of applications in wide range of areas. This wide range of areas includes remote sensing, machine/ robot vision, pattern recognition, medical diagnosis, video processing, military, agriculture, television, etc. Image processing has two important components which are image enhancement and information extraction. Since image enhancement works at the front end with the initial raw inputs, it works like a backbone in image processing. When it comes to implementing these image enhancement techniques and developing applications, these tasks are bit demanding in the choice of processing units because the demand of high resolution. This emerges the necessity of a high speed, powerful and cost-effective processing unit. In this thesis we present an improved image enhancement algorithm in terms of performance and its implementation on FPGA as they satiates the necessity of high speed, powerful and cost-effective processing unit by providing flexibility, parallelization, pipelining and reconfigurability. We have performed a high level synthesis by using MATLAB and implemented an improved image enhancement algorithm on Cyclone V by using Quartus Prime. We have considered an X-ray image size of 1000x1920p for implementation and achieved decent PSNR values and hardware resource utilization along with the better visual interpretability by our proposed improvements. For achieving a better execution time and power consumption we also offer the task parallelism for the algorithm
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