18 research outputs found
An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision
The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature
4D X-Ray CT Reconstruction using Multi-Slice Fusion
There is an increasing need to reconstruct objects in four or more dimensions
corresponding to space, time and other independent parameters. The best 4D
reconstruction algorithms use regularized iterative reconstruction approaches
such as model based iterative reconstruction (MBIR), which depends critically
on the quality of the prior modeling. Recently, Plug-and-Play methods have been
shown to be an effective way to incorporate advanced prior models using
state-of-the-art denoising algorithms designed to remove additive white
Gaussian noise (AWGN). However, state-of-the-art denoising algorithms such as
BM4D and deep convolutional neural networks (CNNs) are primarily available for
2D and sometimes 3D images. In particular, CNNs are difficult and
computationally expensive to implement in four or more dimensions, and training
may be impossible if there is no associated high-dimensional training data.
In this paper, we present Multi-Slice Fusion, a novel algorithm for 4D and
higher-dimensional reconstruction, based on the fusion of multiple
low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium
(MACE), an extension of Plug-and-Play, as a framework for integrating the
multiple lower-dimensional prior models. We apply our method to the problem of
4D cone-beam X-ray CT reconstruction for Non Destructive Evaluation (NDE) of
moving parts. This is done by solving the MACE equations using
lower-dimensional CNN denoisers implemented in parallel on a heterogeneous
cluster. Results on experimental CT data demonstrate that Multi-Slice Fusion
can substantially improve the quality of reconstructions relative to
traditional 4D priors, while also being practical to implement and train.Comment: 8 pages, 8 figures, IEEE International Conference on Computational
Photography 2019, Toky
Joint Frequency and Image Space Learning for Fourier Imaging
We demonstrate that neural network layers that explicitly combine frequency
and image feature representations are a versatile building block for analysis
of imaging data acquired in the frequency space. Our work is motivated by the
challenges arising in MRI acquisition where the signal is a corrupted Fourier
transform of the desired image. The joint learning schemes proposed and
analyzed in this paper enable both correction of artifacts native to the
frequency space and manipulation of image space representations to reconstruct
coherent image structures. This is in contrast to most current deep learning
approaches for image reconstruction that apply learned data manipulations
solely in the frequency space or solely in the image space. We demonstrate the
advantages of joint convolutional learning on three diverse tasks: image
reconstruction from undersampled acquisitions, motion correction, and image
denoising in brain and knee MRI. We further demonstrate advantages of the joint
learning approaches across training schemes using a wide variety of loss
functions. Unlike purely image based and purely frequency based architectures,
the joint models produce consistently high quality output images across all
tasks and datasets. Joint image and frequency space feature representations
promise to significantly improve modeling and reconstruction of images acquired
in the frequency space. Our code is available at
https://github.com/nalinimsingh/interlacer.Comment: 16 pages, 13 figures, image reconstruction, motion correction,
denoising, magnetic resonance imaging, deep learnin