Details
| Original language | English |
|---|---|
| Title of host publication | 2012 9th Electronics, Robotics and Automotive Mechanics Conference |
| Pages | 3-8 |
| Number of pages | 6 |
| Publication status | Published - 10 Jun 2013 |
| Event | 2012 9th Electronics, Robotics and Automotive Mechanics Conference, CERMA 2012 - Cuernavaca, Morelos, Mexico Duration: 19 Nov 2012 → 23 Nov 2012 |
Abstract
An algorithm is presented that is able to detect the regions which correspond to the single isolated human insulin crystals in a group of previously segmented foreground regions. It is based on a single nearest prototype rule, which requires the knowledge of a class prototype for each class of segmented foreground regions. Each class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members extracted a priori from a training set of images. An arbitrary segmented foreground region is detected as the region of an isolated human insulin crystal if the region's vector of rotation, translation and scale invariant shape characteristics is much closer to the class prototype of the regions of single isolated human insulin crystals than to the other foreground region class prototypes. The Euclidian distance is used to compute the closeness between two vectors. Experimental results with real data revealed an average processing time of 0.15 seconds/image and a detection reliability of 95 percent.
Keywords
- Biomedical imaging, Detection, Human insulin crystals, In-situ microscopy, Microscope image analysis, Nearest class prototype rule
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Automotive Engineering
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2012 9th Electronics, Robotics and Automotive Mechanics Conference. 2013. p. 3-8.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Algorithm for Detection of Single Isolated Human Insulin Crystals for In-Situ Microscopy
AU - Martinez, Geovanni
AU - Lindner, Patrick
AU - Bluma, Arne
AU - Scheper, Thomas
PY - 2013/6/10
Y1 - 2013/6/10
N2 - An algorithm is presented that is able to detect the regions which correspond to the single isolated human insulin crystals in a group of previously segmented foreground regions. It is based on a single nearest prototype rule, which requires the knowledge of a class prototype for each class of segmented foreground regions. Each class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members extracted a priori from a training set of images. An arbitrary segmented foreground region is detected as the region of an isolated human insulin crystal if the region's vector of rotation, translation and scale invariant shape characteristics is much closer to the class prototype of the regions of single isolated human insulin crystals than to the other foreground region class prototypes. The Euclidian distance is used to compute the closeness between two vectors. Experimental results with real data revealed an average processing time of 0.15 seconds/image and a detection reliability of 95 percent.
AB - An algorithm is presented that is able to detect the regions which correspond to the single isolated human insulin crystals in a group of previously segmented foreground regions. It is based on a single nearest prototype rule, which requires the knowledge of a class prototype for each class of segmented foreground regions. Each class prototype represents a 7-dimensional mean vector of rotation, translation and scale invariant shape characteristics of several class members extracted a priori from a training set of images. An arbitrary segmented foreground region is detected as the region of an isolated human insulin crystal if the region's vector of rotation, translation and scale invariant shape characteristics is much closer to the class prototype of the regions of single isolated human insulin crystals than to the other foreground region class prototypes. The Euclidian distance is used to compute the closeness between two vectors. Experimental results with real data revealed an average processing time of 0.15 seconds/image and a detection reliability of 95 percent.
KW - Biomedical imaging
KW - Detection
KW - Human insulin crystals
KW - In-situ microscopy
KW - Microscope image analysis
KW - Nearest class prototype rule
UR - http://www.scopus.com/inward/record.url?scp=84880856222&partnerID=8YFLogxK
U2 - 10.1109/CERMA.2012.8
DO - 10.1109/CERMA.2012.8
M3 - Conference contribution
AN - SCOPUS:84880856222
SN - 9780769548784
SP - 3
EP - 8
BT - 2012 9th Electronics, Robotics and Automotive Mechanics Conference
T2 - 2012 9th Electronics, Robotics and Automotive Mechanics Conference, CERMA 2012
Y2 - 19 November 2012 through 23 November 2012
ER -