A novel dictionary based computer vision method for the detection of cell nuclei.
Cell nuclei detection in fluorescent microscopic images is an important and time consuming task in a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make individual nuclei detection a challenging task for automated image analysis. This paper proposes a novel and robust detection method based on the active contour framework. Improvement over conventional approaches is achieved by exploiting prior knowledge of the nucleus shape in order to better detect individual nuclei. This prior knowledge is defined using a dictionary based approach which can be formulated as the optimization of a convex energy function. The proposed method shows accurate detection results for dense clusters of nuclei, for example, an F-measure (a measure for detection accuracy) of 0.96 for the detection of cell nuclei in peripheral blood mononuclear cells, compared to an F-measure of 0.90 achieved by state-of-the-art nuclei detection methods.
Department of Telecommunications and Information Processing, iMinds, Ghent University, Ghent, Belgium. jonas.devylder@telin.ugent.be
Jonas
J
De Vylder
Jan
J
Aelterman
Trees
T
Lepez
Mado
M
Vandewoestyne
Koen
K
Douterloigne
Dieter
D
Deforce
Wilfried
W
Philips
D002467:Cell Nucleus; D002478:Cells, Cultured; D003201:Computers; D006801:Humans; D008856:Microscopy, Fluorescence