Abstract
Phase Contrast Microscopy (PCM) is an important tool for the long term study of living cells. Unlike fluorescence methods which suffer from photobleaching of fluorophore or dye molecules, PCM image contrast is generated by the natural variations in optical index of refraction. Unfortunately, the same physical principles which allow for these studies give rise to complex artifacts in the raw PCM imagery. Of particular interest in this paper are neuron images where these image imperfections manifest in very different ways for the two structures of specific interest: cell bodies (somas) and dendrites. To address these challenges, we introduce a novel parametric image model using the level set framework and an associated variational approach which simultaneously restores and segments this class of images. Using this technique as the basis for an automated image analysis pipeline, results for both the synthetic and real images validate and demonstrate the advantages of our approach.
Highlights
Phase Contrast Microscopy (PCM) is an important technique for the study of living cells
Rather than employing a pixel-based parametrization of the problem as in [2], we present a parametric image model consisting of two level set functions to represent the neuron images
Based on the above segmentation approach, we present the pipeline illustrated in Fig. 2 which performs image analysis for the PCM neuron images
Summary
Phase Contrast Microscopy (PCM) is an important technique for the study of living cells. Besides the PCM imaging artifacts, the development of analysis algorithms for neurons is even more challenging due to the requirement of identifying both cell bodies (somas) as well as dendrites, two classes of structures with very different geometries and contrasts within the data. The majority of image related research of neurons has been performed on fluorescence microscopy data [15] with very limited work focusing on the development of automatic algorithm for neuron segmentation (including both somas and dendrites) and connectivity analysis for PCM images [16, 17]. As in [2, 3], background bias correction is performed before the essential part of the pipeline as well as the major contributions of this paper which is “variational segmentation.” In this essential part, we build an energy functional of level set functions based on the PCM physical model and image information.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
