TY  - JOUR
AB  - Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging.
AU  - Ungru, Kathrin
AU  - Ungru, Kathrin Elisabeth
AU  - Jiang, Xiaoyi
AU  - Xiaoyi, Jiang
AU  - Jiang Xiaoyi
DA  - 2017-02-16
DO  - http://dx.doi.org/10.1016/j.csbj.2017.02.001
KW  - Dynamic programming
KW  - Active contours
KW  - Energy minimization
KW  - Shortest path
KW  - Segmentation
KW  - Contour detection
LA  - eng
N1  - Computational and Structural Biotechnology Journal 15 (2017), 255–264
N1  - Finanziert durch den Open-Access-Publikationsfonds 2017 der Westfälischen Wilhelms-Universität Münster (WWU Münster).
PY  - 2017-02-16
TI  - Dynamic Programming Based Segmentation in Biomedical Imaging
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:6-48189534573
Y2  - 2024-12-27T07:01:59
ER  -