Neurosphere segmentation in brightfield images

Jierong Cheng; Wei Xiong; Shue Ching Chia; Joo Hwee Lim; Shvetha Sankaran; Sohail Ahmed

doi:10.1117/12.2043365

21 March 2014 Neurosphere segmentation in brightfield images

Jierong Cheng, Wei Xiong, Shue Ching Chia, Joo Hwee Lim, Shvetha Sankaran, Sohail Ahmed

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90344D (2014) https://doi.org/10.1117/12.2043365
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

The challenge of segmenting neurospheres (NSPs) from brightfield images includes uneven background illumination (vignetting), low contrast and shadow-casting appearance near the well wall. We propose a pipeline for neurosphere segmentation in brightfield images, focusing on shadow-casting removal. Firstly, we remove vignetting by creating a synthetic blank field image from a set of brightfield images of the whole well. Then, radial line integration is proposed to remove the shadow-casting and therefore facilitate automatic segmentation. Furthermore, a weighted bi-directional decay function is introduced to prevent undesired gradient effect of line integration on NSPs without shadow-casting. Afterward, multiscale Laplacian of Gaussian (LoG) and localized region-based level set are used to detect the NSP boundaries. Experimental results show that our proposed radial line integration method (RLI) achieves higher detection accuracy over existing methods in terms of precision, recall and F-score with less computational time.

Citation Download Citation

Jierong Cheng, Wei Xiong, Shue Ching Chia, Joo Hwee Lim, Shvetha Sankaran, and Sohail Ahmed "Neurosphere segmentation in brightfield images", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90344D (21 March 2014); https://doi.org/10.1117/12.2043365

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