| 11. |
- Eklund, Patrik, 1958-, et al.
(författare)
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Unravelling the thrill of metric image spaces
- 1999
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Ingår i: Lecture Notes in Computer Science. - : Springer Berlin/Heidelberg. - 0302-9743 .- 1611-3349. ; 1568, s. 275-285
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we focus on distances between textures. and develop metrics on image spaces in contexts of image transformations. Given a metric on the range space, we can generate the initial topology for the domain space. For this topology we can obtain a corresponding metric using well-known metrization constructions, also providing granularity of the metrics. Examples are drawn front the Spatial Gray Level Dependency (SGLD) transformation and the application domain is texture recognition in medical imaging.
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| 12. |
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| 13. |
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| 14. |
- Eriksson, Anna U., et al.
(författare)
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Near infrared optical projection tomography for assessments of beta-cell mass distribution in diabetes research
- 2013
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Ingår i: Journal of Visualized Experiments. - : MyJove Corporation. - 1940-087X. ; 71
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Tidskriftsartikel (refereegranskat)abstract
- By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of pancreatic tissue, such as the rat pancreas, and to increase the number of channels (cell types) that may be studied in a single specimen. We further describe the implementation of a number of computational tools that provide: 1/ accurate positioning of a specimen's (in our case the pancreas) centre of mass (COM) at the axis of rotation (AR)2; 2/ improved algorithms for post-alignment tuning which prevents geometric distortions during the tomographic reconstruction2 and 3/ a protocol for intensity equalization to increase signal to noise ratios in OPT-based BCM determinations3. In addition, we describe a sample holder that minimizes the risk for unintentional movements of the specimen during image acquisition. Together, these protocols enable assessments of BCM distribution and other features, to be performed throughout the volume of intact pancreata or other organs (e.g. in studies of islet transplantation), with a resolution down to the level of individual islets of Langerhans.
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| 15. |
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| 16. |
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| 17. |
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| 18. |
- Georgsson, Fredrik, et al.
(författare)
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Development of an Autonomous Path Tracking Forest Machine : a status report
- 2005
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In many respects traditional automation in the forest-machine industry has reached an up- per limit, since the driver already has to deal with an excess of information and take too many decisions at a very high pace. To further automation still, introduction of semi-autonomous and autonomous functions are expected and considered necessary. This paper describes an ongoing project along these ideas. We describe the development of the hardware and software of an unmanned shuttle that shifts timber from the area of felling to the main roads for fur- ther transportation. A new path-tracking algorithm is introduced, and demonstrated as being superior to standard techniques, such as Follow-the-Carrot and Pure-Pursuit. To facilitate the research and development, a comprehensive software architecture for sensor and actuator interfacing is developed. Obstacle avoidance is accomplished by a new kind of radar, developed for and by the automotive industry. Localization is accomplished by a Kalman filter combining data from a Real-Time Kinematic Differential GPS/GLONASS and a gyro/compass. Tests conducted on a simulator and a small-scale robot show promising results. Tests on the real forest machine are ongoing, and will be completed before the end of 2005.
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| 19. |
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| 20. |
- Georgsson, Fredrik, 1971-, et al.
(författare)
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Fractal Analysis of Mammograms
- 2007
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Ingår i: SCIA 2007. - Berlin : Springer. - 9783540730392 ; , s. 92-101
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Konferensbidrag (refereegranskat)abstract
- In this paper it is shown that there is a difference in local fractal dimension between imaged glandular tissue, supporting tissue and muscle tissue based on an assessment from a mammogram. By estimating the density difference at four different local dimensions (2.06, 2.33, 2.48, 2.70) from 142 mammograms we can define a measure and by using this measure we are able to distinguish between the tissue types. A ROC-analysis gives us an area under the curve-value of 0.9998 for separating glandular tissue from muscular tissue and 0.9405 for separating glandular tissue from supporting tissue. To some extent we can say that the measured difference in fractal properties is due to different fractal properties of the unprojected tissue. For example, to a large extent muscle tissue seems to have different fractal properties than glandular or supportive tissue. However, a large variance in the local dimension densities makes it difficult to make proper use of the proposed measure for segmentation purposes.
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