Complex local phase based subjective surfaces (CLAPSS) and its application to DIC red blood cell image segmentation Taoyi Chen a,b,n , Yong Zhang c , Changhong Wang b , Zhenshen Qu b , Fei Wang c , Tanveer Syeda-Mahmood c a The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, Hebei 050081, China b Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150080, China c Healthcare Informatics, IBM Almaden Research Center, San Jose, CA 95120, USA article info Article history: Received 11 October 2011 Received in revised form 12 March 2012 Accepted 12 June 2012 Communicated by Qi Li Available online 17 July 2012 Keywords: Cell segmentation Subjective surfaces Complex local phase Red blood cell Level set abstract Differential Interference Contrast (DIC) microscopy is a common approach for researching the dynamics of cell behaviors. Segmentation of shape of erythrocyte (red blood cell) is the basis of quantitative analysis of its deformability and hence its filterability. Commonly used manual segmentation of shapes of individual cells from samples by human visual inspection requires a large amount of tedious work because it is time consuming and exhaustive. This makes automatic cell image analysis essential in biology studies. In this paper, a novel level set based technique, called Complex Local Phase based Subjective Surfaces (CLAPSS), is proposed for the segmentation of differential interference contrast (DIC) red blood cell microscopy images. Based on the framework of a generalized version of subjective surfaces (GSUBSURF), a complex local phase based edge indicator function is introduced to replace the traditional gradient based edge detector for the local image feature acquisition, which is the key for the evolution of the surface. In addition, we propose a new variation scheme for stretching factor to achieve relatively accurate segmentation results even if the reference point is located nearby cell boundaries. We show that the proposed method is more accurate and reliable than several existing methods in experiments. & 2012 Elsevier B.V. All rights reserved. 1. Introduction Red blood cells perform one of most important blood studies. A single drop of blood contains millions of red blood cells which are constantly traveling through human body delivering oxygen and removing waste. In Ref. [1], it was hypothesized that the blood could be filtered with the shape of red blood cell. Subsequently the shape variations of red blood cells have been reported to have significant relations with particular illnesses, such as Myalgic Encephalomye- litis (ME) and Multiple Sclerosis (MS). As a non-fluorescence, interference based microscopy imaging modality, the DIC microscopy system is particularly suitable for long-term investigation of living biological specimens. This project is therefore aimed to design automated methods to segment DIC cells to further analyze the changes of DIC cell shape and topology. While being visually contrastive, it is still challenging to auto- mate the cell segmentation and measurement of DIC images, due to the following observations: (1) Most DIC red blood cell images have low contrast with variational cell shapes. (2) The dual-beam inter- ference optics of DIC microscopes introduces non-uniform shadow- cast artifacts (uneven illumination). (3) The boundaries of some cells are vague or even missing. A wide variety of algorithms has been proposed over the years to tackle the problem of cell image segmentation. These algo- rithms can be categorized as follows: traditional segmentation, graph cut based segmentation, and active contour model. Traditional segmentation algorithms use the methods such as thresholding [2, 3], watershed [4, 5], and edge detection [6, 7]. Thres- holding method cannot separate cell pixels with low intensity contrast from the background, and cannot discriminate touching cells, as the spatial relations are not embedded in basic thresholding techniques. Watershed algorithm regards the intensity of an image as a topological surface and directly uses region minimums or ultimate eroded points as starting points. Though Original watershed can segment touching cells as long as the seeds are initialized properly, the over segmentation problem would be likely to occur. Subse- quently the two approaches to address the over segmentation of watershed: fragment merging [8] and marker-controlled [9] water- shed were proposed. However, none of these approaches analyzes jointly all available spectral information [10]. Edge detection is built on gradient or intensity. Image gradient values are determined in Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/neucom Neurocomputing 0925-2312/$ - see front matter & 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.neucom.2012.06.015 n Corresponding author at: The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, Hebei 050081, China. E-mail address: taoyichen@gmail.com (T. Chen). Neurocomputing 99 (2013) 98–110