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automatic analysis of biological images

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 computervision techniques in order to ease the analysis of biological images.

  • Measure:
    • temperature
    • movement
    • photosynthesis
    • growth
    • cell division
  • Measure influence of:
  • stress
  • fertilizers, agrochemicals
  • genetic engineering





Automatic analysis of biologicalimages

Fluorescence imaging






  • 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 = 54

T = 55

T = 53

T = 8

T = 9

T = 7

Active contours


Unpredicted motion!

Difference in contrast! Strong contrast overrules weak contrast

Error propagation!

T = 8

T = 9

T = 7

T = 54

T = 55

T = 53

Proposed methods

Jonas De Vylder, Daniel Ochoa, Wilfried Philips,

Laury Chaerle, Dominique Van Der Straeten

First correct for rigid motion (without motion prediction! ), then deform contour

Adjust energy formulation: take estimated contrast into account

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.

Nuclei detection

Biological monitoring

Leaf tracking in time lapse sequences

Active contours

Population statistics

Computer Vision

  • The proposed method for cell nuclei detection is:
    • Fast
    • memory efficient
    • reliable

Use less samples, i.e. only correct samples



Use all samples, including unlikely samples


Estimated mean=980 um

Real mean= 983 um

Ghent University, Dept. of Telecommunications and Information Processing