125 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Liver segmentation using 3D CT scans.
Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2018.Abstract available in PDF file
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Structure analysis and lesion detection from retinal fundus images
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening.
Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features.
For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection.
We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape).
This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.Mexican National Council of Science and Technolog
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
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