167 research outputs found
Computer-Assisted Algorithms for Ultrasound Imaging Systems
Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and
reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging
is considered to be safer, economical and can image the organs in real-time, which makes it widely
used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum
of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc.
Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are
in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of
an ultrasound system are constrained to hospitals and did not translate to its potential in remote
health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low
signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an
objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care
applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic
accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve
the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address
the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in
point-of-care and remote health-care applications
Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm
PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage
Invariant Scattering Transform for Medical Imaging
Invariant scattering transform introduces new area of research that merges
the signal processing with deep learning for computer vision. Nowadays, Deep
Learning algorithms are able to solve a variety of problems in medical sector.
Medical images are used to detect diseases brain cancer or tumor, Alzheimer's
disease, breast cancer, Parkinson's disease and many others. During pandemic
back in 2020, machine learning and deep learning has played a critical role to
detect COVID-19 which included mutation analysis, prediction, diagnosis and
decision making. Medical images like X-ray, MRI known as magnetic resonance
imaging, CT scans are used for detecting diseases. There is another method in
deep learning for medical imaging which is scattering transform. It builds
useful signal representation for image classification. It is a wavelet
technique; which is impactful for medical image classification problems. This
research article discusses scattering transform as the efficient system for
medical image analysis where it's figured by scattering the signal information
implemented in a deep convolutional network. A step by step case study is
manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl
Liver CT enhancement using Fractional Differentiation and Integration
In this paper, a digital image filter is proposed to enhance the Liver CT image for improving the classification of tumors area in an infected Liver. The enhancement process is based on improving the main features within the image by utilizing the Fractional Differential and Integral in the wavelet sub-bands of an image. After enhancement, different features were extracted such as GLCM, GRLM, and LBP, among others. Then, the areas/cells are classified into tumor or non-tumor, using different models of classifiers to compare our proposed model with the original image and various established filters. Each image is divided into 15x15 non-overlapping blocks, to extract the desired features. The SVM, Random Forest, J48 and Simple Cart were trained on a supplied dataset, different from the test dataset. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of enhancement in the proposed technique
A multimodal deep learning framework using local feature representations for face recognition
YesThe most recent face recognition systems are
mainly dependent on feature representations obtained using
either local handcrafted-descriptors, such as local binary patterns
(LBP), or use a deep learning approach, such as deep
belief network (DBN). However, the former usually suffers
from the wide variations in face images, while the latter
usually discards the local facial features, which are proven
to be important for face recognition. In this paper, a novel
framework based on merging the advantages of the local
handcrafted feature descriptors with the DBN is proposed to
address the face recognition problem in unconstrained conditions.
Firstly, a novel multimodal local feature extraction
approach based on merging the advantages of the Curvelet
transform with Fractal dimension is proposed and termed
the Curvelet–Fractal approach. The main motivation of this
approach is that theCurvelet transform, a newanisotropic and
multidirectional transform, can efficiently represent themain
structure of the face (e.g., edges and curves), while the Fractal
dimension is one of the most powerful texture descriptors
for face images. Secondly, a novel framework is proposed,
termed the multimodal deep face recognition (MDFR)framework,
to add feature representations by training aDBNon top
of the local feature representations instead of the pixel intensity
representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary
to those acquired by the Curvelet–Fractal approach.
Finally, the performance of the proposed approaches has
been evaluated by conducting a number of extensive experiments
on four large-scale face datasets: the SDUMLA-HMT,
FERET, CAS-PEAL-R1, and LFW databases. The results
obtained from the proposed approaches outperform other
state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by
achieving new state-of-the-art results on all the employed
datasets
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