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This article is part of the supplement: The 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009)

Open Access Research

An improved approach for the segmentation of starch granules in microscopic images

Shengwen Guo1, Jinshan Tang1*, Youping Deng2 and Qun Xia3

Author Affiliations

1 Image Processing and Bioimaging Research Laboratory, System Research Institute & Department of Advanced Technologies, Alcorn State University, 1000 ASU Drive, Alcorn State, MS 39096, USA

2 Rush Cancer Center, Rush University Medical Center, Chicago, IL 60612, USA

3 Department of Agriculture-Rest, Alcorn State University, 1000 ASU Drive, Alcorn State, MS 39096, USA

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BMC Genomics 2010, 11(Suppl 2):S13  doi:10.1186/1471-2164-11-S2-S13

Published: 2 November 2010

Abstract

Background

Starches are the main storage polysaccharides in plants and are distributed widely throughout plants including seeds, roots, tubers, leaves, stems and so on. Currently, microscopic observation is one of the most important ways to investigate and analyze the structure of starches. The position, shape, and size of the starch granules are the main measurements for quantitative analysis. In order to obtain these measurements, segmentation of starch granules from the background is very important. However, automatic segmentation of starch granules is still a challenging task because of the limitation of imaging condition and the complex scenarios of overlapping granules.

Results

We propose a novel method to segment starch granules in microscopic images. In the proposed method, we first separate starch granules from background using automatic thresholding and then roughly segment the image using watershed algorithm. In order to reduce the oversegmentation in watershed algorithm, we use the roundness of each segment, and analyze the gradient vector field to find the critical points so as to identify oversegments. After oversegments are found, we extract the features, such as the position and intensity of the oversegments, and use fuzzy c-means clustering to merge the oversegments to the objects with similar features. Experimental results demonstrate that the proposed method can alleviate oversegmentation of watershed segmentation algorithm successfully.

Conclusions

We present a new scheme for starch granules segmentation. The proposed scheme aims to alleviate the oversegmentation in watershed algorithm. We use the shape information and critical points of gradient vector flow (GVF) of starch granules to identify oversegments, and use fuzzy c-mean clustering based on prior knowledge to merge these oversegments to the objects. Experimental results on twenty microscopic starch images demonstrate the effectiveness of the proposed scheme.