2,688 research outputs found
Fatty liver automatic diagnosis from ultrasound images
In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically
the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma
relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and
echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features.
The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity
Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles
We investigate the usefulness of quantitative ultrasound (QUS) and B-mode
texture features for characterization of ulnar nerve fascicles. Ultrasound data
were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the
nerves were extracted to prepare histology sections. 85 fascicles were matched
between the B-mode images and the histology sections. For each fascicle image,
we selected an intra-fascicular region of interest. We used histology sections
to determine features related to the concentration of collagen and myelin, and
ultrasound data to calculate backscatter coefficient (-24.89 dB 8.31),
attenuation coefficient (0.92 db/cm-MHz 0.04), Nakagami parameter (1.01
0.18) and entropy (6.92 0.83), as well as B-mode texture features
obtained via the gray level co-occurrence matrix algorithm. Significant
Spearman's rank correlations between the combined collagen and myelin
concentrations were obtained for the backscatter coefficient (R=-0.68), entropy
(R=-0.51), and for several texture features. Our study demonstrates that QUS
may potentially provide information on structural components of nerve
fascicles
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
Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data
In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging
Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization
Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted.
The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the
Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.This work was supported by Fundação para a Ciência e Tecnologia (ISR/IST plurianual funding) through the POS Conhecimento Program
which includes FEDER funds
Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data
In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest
neighbor classifier are compared. A population of 88 patients at five different
stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable
indicator that can improve the information in the staging of diffuse liver disease
Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine
Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computa-tional as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles
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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