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1.
  • Rowa, Per, et al. (författare)
  • Automated Malaria Parasite Detection
  • 1977
  • Rapport (övrigt vetenskapligt)abstract
    • A system for malaria parasite detection in thin blood-smears is presented. Sample slides prepared with standard methods are accepted. A low-cost TV-camera mounted on an ordinary microscope with a computer controlled stage is used as a picture sensor. Frames, digitized in windows of 64 x 64 pixels are fed into a special purpose picture processor at normal frame rate (25 frames/sec). In the picture processor measurements are made on the images at high speed. The classification problem is split into different levels each having different characteristics such as different sampling density. Four classes, three of which are different types of malaria parasites, are recognized. As a whole the classification is best labelled as a sequential pattern recognition procedure.In its preliminary version the system has been run at a speed comparable to that of a human operator, that is l 500 cells per minute. A test on 80 000 cells gave 25 false negatives out of 283 parasites (9%) and 41 false positives (0.05%).
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2.
  • Tomasic, Ivan, et al. (författare)
  • Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device
  • 2019
  • Ingår i: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532330984 ; , s. 275-278
  • Konferensbidrag (refereegranskat)abstract
    • Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively. 
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3.
  • Tampu, Iulian Emil, et al. (författare)
  • Deep-learning for thyroid microstructure segmentation in 2D OCT images
  • 2021
  • Ingår i: Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV. - : SPIE - International Society for Optical Engineering.
  • Konferensbidrag (refereegranskat)abstract
    • Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples a effected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data.
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4.
  • Yousefi, Hossein, et al. (författare)
  • An optimised linear mechanical model for estimating brain shift caused by meningioma tumours
  • 2013
  • Ingår i: International Journal of Biomedical Science and Engineering. - : Science Publishing Group. - 2376-7227 .- 2376-7235. ; 1:1, s. 1-9
  • Tidskriftsartikel (refereegranskat)abstract
    • Estimation of brain deformation plays an important role in computer-aided therapy and image-guided neurosurgery systems. Tumour growth can cause brain deformation and change stress distribution in the brain. Biomechanical models exist that use a finite element method to estimate brain shift caused by tumour growth. Such models can be categorised as linear and non-linear models, both of which assume finite deformation of the brain after tumour growth. Linear models are easy to implement and fast enough to for applications such as IGS where the time is a great of concern. However their accuracy highly dependent on the parameters of the models in this paper, we proposed an optimisation approach to improve a naive linear model to achieve more precise estimation of brain displacements caused by tumour growth. The optimisation process has improved the accuracy of the model by adapting the brain model parameters according to different tomour sizes.We used patient-based tetrahedron finite element mesh with proper material properties for brain tissue and appropriate boundary conditions in the tumour region. Anatomical landmarks were determined by an expert and were divided into two different sets for evaluation and optimisation. Tetrahedral finite element meshes were used and the model parameters were optimised by minimising the mean square distance between the predicted locations of the anatomical landmarks derived from Brain Atlas images and their actual locations on the tumour images. Our results demonstrate great improvement in the accuracy of an optimised linear mechanical model that achieved an accuracy rate of approximately 92%.
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5.
  • Candefjord, Stefan, et al. (författare)
  • Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization
  • 2010
  • Ingår i: Measurement science and technology. - : IOP Publishing Ltd. - 0957-0233 .- 1361-6501. ; 21:125801, s. 1-8
  • Tidskriftsartikel (refereegranskat)abstract
    • Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard-–histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.
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6.
  • Candefjord, Stefan, et al. (författare)
  • Evaluating the use of a Raman fiberoptic probe in conjunction with a resonance sensor for measuring porcine tissue in vitro
  • 2009
  • Ingår i: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering. - Heidelberg : Springer. ; , s. 414-417, s. 414-417
  • Konferensbidrag (refereegranskat)abstract
    • Prostate cancer is the most common form of cancer and is the third leading cause of cancer-related death in European men. There is a need for new methods that can accurately localize and diagnose prostate cancer. In this study a new approach is presented: a combination of resonance sensor technology and Raman spectroscopy. Both methods have shown promising results for prostate cancer detection in vitro. The aim of this study was to evaluate the combined information from measurements with a Raman fiberoptic probe and a resonance sensor system. Pork belly tissue was used as a model system. A three-dimensional translation table was equipped with an in-house developed software, allowing measurements to be performed at the same point using two separate instruments. The Raman data was analyzed using principal component analysis and hierarchical clustering analysis. The spectra were divided into 5 distinct groups. The mean stiffness of each group was calculated from the resonance sensor measurements. One of the groups differed significantly (p < 0.05) from the others. A regression analysis, with the stiffness parameter as response variable and the principal component scores of the Raman data as the predictor variables, explained 67% of the total variability. The use of a smaller resonance sensor tip would probably increase the degree of correlation. In conclusion, Raman spectroscopy provides additional discriminatory power to the resonance sensor.
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7.
  • Eklund, Anders, et al. (författare)
  • Evaluation of applanation resonator sensors for intra-ocular pressure measurement : results from clinical and in vitro studies.
  • 2003
  • Ingår i: Medical and Biological Engineering and Computing. - 0140-0118 .- 1741-0444. ; 41:2, s. 190-197
  • Tidskriftsartikel (refereegranskat)abstract
    • Glaucoma is an eye disease that, in its most common form, is characterised by high intra-ocular pressure (IOP), reduced visual field and optic nerve damage. For diagnostic purposes and for follow-up after treatment, it is important to have simple and reliable methods for measuring IOP. Recently, an applanation resonator sensor (ARS) for measuring IOP was introduced and evaluated using an in vitro pig-eye model. In the present study, the first clinical evaluation of the same probe has been carried out, with experiments in vivo on human eyes. There was a low but significant correlation between IOP(ARS) and the IOP measured with a Goldmann applanation tonometer (r = 0.40, p = 0.001, n = 72). However, off-centre positioning of the sensor against the cornea caused a non-negligible source of error. The sensor probe was redesigned to have a spherical, instead of flat, contact surface against the eye and was evaluated in the in vitro model. The new probe showed reduced sensitivity to off-centre positioning, with a decrease in relative deviation from 89% to 11% (1 mm radius). For normalised data, linear regression between IOP(ARS) and direct IOP measurement in the vitreous chamber showed a correlation of r = 0.97 (p < 0.001, n = 108) and a standard deviation for the residuals of SD < or = 2.18 mm Hg (n = 108). It was concluded that a spherical contact surface should be preferred and that further development towards a clinical instrument should focus on probe design and signal analysis.
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8.
  • Jalkanen, Ville, 1978-, et al. (författare)
  • Instrument towards faster diagnosis and treatment of prostate cancer : Resonance sensor stiffness measurements on human prostate tissue in vitro
  • 2009
  • Ingår i: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering. - Heidelberg : Springer. ; , s. 145-148, s. 145-148
  • Konferensbidrag (refereegranskat)abstract
    • Prostate cancer is the most common cancer among men and the methods used to detect and diagnose prostate cancer are not sufficiently accurate. Radical prostatectomy is a surgical treatment of prostate cancer where the whole prostate is removed from the patient. Prostate tissue stiffness can be measured with a stiffness sensitive resonance sensor. The aim of this study was to measure the stiffness on the anterior and posterior side of fresh human prostate tissue in vitro and compare these two groups with each other and relate the findings with the prostate tissue histology.  In a prostate tissue slice with mostly normal healthy tissue, the anterior side was significantly harder (p-value < 0.05) as expected. In a prostate tissue slice with areas of cancer tumors, no difference was found between the anterior and posterior sides. However, large stiffness variations were found within groups with measurements points on cancer tissue (coefficient of variation, CV = 42 and 85%), as opposed to groups without cancer tissue (CV = 27 and 28%).  The large stiffness variations could be used as a sign for the presence of cancer. The results are promising for the development of an instrument and method for faster diagnosis on radical prostatectomy samples.
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9.
  • Jalkanen, Ville, 1978-, et al. (författare)
  • Prostate tissue stiffness as measured with a resonance sensor system : a study on silicone and human prostate tissue in vitro.
  • 2006
  • Ingår i: Medical and Biological Engineering and Computing. - 0140-0118 .- 1741-0444. ; 44:7, s. 593-603
  • Tidskriftsartikel (refereegranskat)abstract
    • Prostate cancer is the most common form of cancer in men in Europe and in the USA. Some prostate tumours are stiffer than the surrounding normal tissue, and it could therefore be of interest to measure prostate tissue stiffness. Resonance sensor technology based on piezoelectric resonance detects variations in tissue stiffness due to a change in the resonance frequency. An impression-controlled resonance sensor system was used to detect stiffness in silicone rubber and in human prostate tissue in vitro using two parameters, both combinations of frequency change and force. Variations in silicone rubber stiffness due to the mixing ratio of the two components could be detected (p<0.05) using both parameters. Measurements on prostate tissue showed that there existed a statistically significant (MANOVA test, p<0.001) reproducible difference between tumour tissue (n=13) and normal healthy tissue (n=98) when studying a multivariate parameter set. Both the tumour tissue and normal tissue groups had variations within them, which were assumed to be related to differences in tissue composition. Other sources of error could be uneven surfaces and different levels of dehydration for the prostates. Our results indicated that the resonance sensor could be used to detect stiffness variations in silicone and in human prostate tissue in vitro. This is promising for the development of a future diagnostic tool for prostate cancer.
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10.
  • Jalkanen, Ville, 1978-, et al. (författare)
  • Resonance sensor measurements of stiffness variations in prostate tissue in vitro : a weighted tissue proportion model
  • 2006
  • Ingår i: Physiological Measurement. - 0967-3334 .- 1361-6579. ; 27:12, s. 1373-86
  • Tidskriftsartikel (refereegranskat)abstract
    • Prostate cancer is the most common type of cancer in men in Europe and the US. The methods to detect prostate cancer are still precarious and new techniques are needed. A piezoelectric transducer element in a feedback system is set to vibrate with its resonance frequency. When the sensor element contacts an object a change in the resonance frequency is observed, and this feature has been utilized in sensor systems to describe physical properties of different objects. For medical applications it has been used to measure stiffness variations due to various patho-physiological conditions. In this study the sensor's ability to measure the stiffness of prostate tissue, from two excised prostatectomy specimens in vitro, was analysed. The specimens were also subjected to morphometric measurements, and the sensor parameter was compared with the morphology of the tissue with linear regression. In the probe impression interval 0.5-1.7 mm, the maximum R(2) > or = 0.60 (p < 0.05, n = 75). An increase in the proportion of prostate stones (corpora amylacea), stroma, or cancer in relation to healthy glandular tissue increased the measured stiffness. Cancer and stroma had the greatest effect on the measured stiffness. The deeper the sensor was pressed, the greater, i.e., deeper, volume it sensed. Tissue sections deeper in the tissue were assigned a lower mathematical weighting than sections closer to the sensor probe. It is concluded that cancer increases the measured stiffness as compared with healthy glandular tissue, but areas with predominantly stroma or many stones could be more difficult to differ from cancer.
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