NDLI: A robust convergence index filter for breast cancer cell segmentation

Content Provider	IEEE Xplore Digital Library
Author	Saha, Baidya Nath Saini, Amritpal Ray, Nilanjan Greiner, Russell Hugh, Judith Tambasco, Mauro
Copyright Year	2014
Description	Author affiliation: Department of Laboratory Medicine & Pathology, University of Alberta, Edmonton, Canada (Hugh, Judith) \|\| Department of Oncology, University of Calgary, Calgary, Canada (Saha, Baidya Nath) \|\| Department of Computing Science, University of Alberta, Edmonton, Canada (Saini, Amritpal; Ray, Nilanjan; Greiner, Russell) \|\| Department of Physics, San Diego State University, San Diego, California, USA (Tambasco, Mauro)
Abstract	COnvergence INdex (COIN) filter, a successful tool for cell localization, evaluates the degree of convergence of the gradient vectors within the neighborhood (region of support) toward a pixel of interest. All previous efforts were to increase the adaptability of the region of support to make the COIN filter robust and accurate. However, improving the quality of the image gradient map was ignored, which results in poor performance of the members of the COIN family in noisy settings. We propose a new Robust Convergence Index (RCI) filter that tailors the COIN filter in a noisy environment by (a) spreading the gradient vectors within non-homogeneous object regions by convolving an Aggregated Edge Probability Map (AEPM) with an edge preserving gradient vector kernel, and (b) increasing the convergence of the gradient vectors through the integration of the sine and cosine distribution as well as the magnitude of the gradient vectors. AEPM is computed through the consensus of the responses of a number of edge detectors over a wide range of scales, which lessens the effects of clutter by enforcing higher weights to the actual edges, and a non-parametric Kernel Density Estimation (KDE) is used to compute the edge probability map. Experimental results demonstrate that it obtains state-of-the-art performance.
Starting Page	922
Ending Page	926
File Size	740598
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Vectors Convergence Indexes Robustness Kernel Filtering algorithms Vector Field Kernel COnvergence INdex (COIN) Filter Robust Convergence Index (RCI) filter Kernel Density Estimation (KDE)
Content Type	Text
Resource Type	Article

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