1. Automatic analysis of biological images
Jonas De Vylder, Daniel Ochoa, Wilfried Philips,
Laury Chaerle, Dominique Van Der Straeten
Biological monitoring
Computer Vision
Recent research has led to the development of a wide range of new imaging instruments, such as high-throughput screening, multispectral imaging, new microscope systems, etc. These innovations have led to increasing biological insight, but these new imaging techniques come with a bottleneck: they generally produce huge amounts of data. Biological images contains valuable information, but it is time consuming, tedious and error prone to manually analyze this data. We propose the use of specialized computer vision techniques in order to ease the analysis of biological images.
Measure:
photosynthesis
temperature
growth
movement
cell division …
Measure influence of:
stress
fertilizers, agrochemicals
genetic engineering …
What?
Why?
Introduction
How?
Fluorescence imaging
Thermal
imaging
Colour
imaging
Microscopy
Active contours
Leaf tracking in time lapse sequences
An Active Contour is a curve that deforms in the spatial domain until an energy functional reaches its minimum. The energy functional is a combination of an internal and external energy:
The internal energy enforces
smoothness along the
contour and prevents the
contour to remain attracted to
isolated points.
The external energy is derived
from the image, so that the
contour will be attracted to
features of interest.
T = 53
T = 54
T = 55
T = 7
T = 8
T = 9
Active contours
Tracking
Unpredicted motion!
Error propagation!
Difference in contrast! Strong contrast overrules weak contrast
T = 7
T = 8
T = 9
T = 53
T = 54
T = 55
Proposed methods
First correct for rigid motion (without motion prediction! ), then deform contour
Adjust energy formulation: take estimated contrast into account
Nuclei detection
Population statistics
Experiments on model organisms are used to extend the understanding of complex biological processes. In C. elegans studies, populations of specimens are sampled to measure certain morpho-logical properties and a population is characterized based on statistics extracted from such samples. Automatic detection of C. elegans in such culture images is a difficult problem. By exploiting shape and appearance a reliable subset of segments can be identified, discarding possible false detections. Experiments show that measurements extracted from these samples correlate well with ground truth data.
The proposed method for cell nuclei detection is:
Fast
memory efficient
reliable
Use less samples, i.e. only correct samples
Detection
P(Length)
Use all samples, including unlikely samples
# nuclei (ground truth)
300
# detected nuclei (average)
299.9
Maximum Error -3
Precision
0.9960
Recall
0.9957
Size
Estimated mean=980 um
Real mean= 983 um
Ghent University, Dept. of Telecommunications and Information Processing