Segmentation of biological images containing multitarget labeling using the jelly filling framework

Neeraj J. Gadgil; Paul Salama; Kenneth W. Dunn; Edward J. Delp III

doi:10.1117/1.JMI.5.4.044006

23 November 2018 Segmentation of biological images containing multitarget labeling using the jelly filling framework

Neeraj J. Gadgil, Paul Salama, Kenneth W. Dunn, Edward J. Delp III

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 4, 044006 (November 2018). https://doi.org/10.1117/1.JMI.5.4.044006

Abstract

Biomedical imaging when combined with digital image analysis is capable of quantitative morphological and physiological characterizations of biological structures. Recent fluorescence microscopy techniques can collect hundreds of focal plane images from deeper tissue volumes, thus enabling characterization of three-dimensional (3-D) biological structures at subcellular resolution. Automatic analysis methods are required to obtain quantitative, objective, and reproducible measurements of biological quantities. However, these images typically contain many artifacts such as poor edge details, nonuniform brightness, and distortions that vary along different axes, all of which complicate the automatic image analysis. Another challenge is due to “multitarget labeling,” in which a single probe labels multiple biological entities in acquired images. We present a “jelly filling” method for segmentation of 3-D biological images containing multitarget labeling. Intuitively, our iterative segmentation method is based on filling disjoint tubule regions of an image with a jelly-like fluid. This helps in the detection of components that are “floating” within a labeled jelly. Experimental results show that our proposed method is effective in segmenting important biological quantities.

Citation Download Citation

Neeraj J. Gadgil, Paul Salama, Kenneth W. Dunn, and Edward J. Delp III "Segmentation of biological images containing multitarget labeling using the jelly filling framework," Journal of Medical Imaging 5(4), 044006 (23 November 2018). https://doi.org/10.1117/1.JMI.5.4.044006

Received: 14 June 2018; Accepted: 5 November 2018; Published: 23 November 2018

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