1,631 research outputs found
A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification
This paper represents the first survey on the application of AI techniques for the analysis
of biomedical images with forensic human identification purposes. Human identification is of
great relevance in today’s society and, in particular, in medico-legal contexts. As consequence,
all technological advances that are introduced in this field can contribute to the increasing necessity
for accurate and robust tools that allow for establishing and verifying human identity. We first
describe the importance and applicability of forensic anthropology in many identification scenarios.
Later, we present the main trends related to the application of computer vision, machine learning
and soft computing techniques to the estimation of the biological profile, the identification through
comparative radiography and craniofacial superimposition, traumatism and pathology analysis,
as well as facial reconstruction. The potentialities and limitations of the employed approaches are
described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU)
PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI
P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU)
DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship
746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019
EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia
ED431G 2019/01European Union (EU)
RTI2018-095894-B-I0
Improving biomedical image quality with computers
Computerized image enhancement techniques used on biomedical radiographs and photomicrograph
Computer-aided diagnosis tool for the detection of cancerous nodules in X-ray images
This thesis involves development of a computer-aided diagnosis (CAD) tool for the detection of cancerous nodules in X-ray images. Both cancerous and non-cancerous regions appear with little distinction on an X-ray image. For accurate detection of cancerous nodules, we need to differentiate the cancerous nodules from the non-cancerous. We developed an artificial neural network to differentiate them. Artificial neural networks (ANN) find a large application in the area of medical imaging. They work in a manner rather similar to the brain and have good decision making criteria when trained appropriately. We trained the neural network by the backpropagation algorithm and tested it with different images from a database of thoracic radiographs (chest X-rays) of dogs from the LSU Veterinary Medical Center. If we give X-ray images directly as input to the ANN, it incurs substantial complexity and training time for the network to process the images. A pre-processing stage involving some image enhancement techniques helps to solve the problem to a certain extent. The CAD tool developed in this thesis works in two stages. We pre-process the digitized images (by contrast enhancement, thresholding, filtering, and blob analysis) obtained after scanning the X-rays and then separate the suspected nodule areas (SNA) from the image by a segmentation process. We then input enhanced SNAs to the backpropagation-trained ANN. When given these enhanced SNAs, the neural network recognition accuracy, compared to unprocessed images as inputs, improved from 70% to 83.33%
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