Investigating the Relevance of Graph Cut Parameter on Interactive and Automatic Cell Segmentation.

Kazeem Oyeyemi Oyebode,Shengzhi Du,Barend Jacobus Van Wyk,Karim Djouani

doi:10.1155/2018/7396910

Kazeem Oyeyemi Oyebode, Shengzhi Du + Show 2 more

Open Access

https://doi.org/10.1155/2018/7396910

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Abstract

Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.

Highlights

Graph cut segmentation technique has become popular in recent times because of its ability in segmenting images into foreground and background using a global strategy.erefore, it has become a useful tool in many segmentation application areas
Erefore, it has become a useful tool in many segmentation application areas. One of such areas is the medical field, where the application of graph cut yields promising results in cell [1] and lung [2] segmentation. e automatic graph cut segmentation is useful as it speeds up cell segmentation, while the interactive segmentation provides the flexibility to select seed points when further investigation needs to be carried out in isolation
E graph cut energy function is equipped with a parameter (λ) which can be tuned to ensure that objects are not oversegmented and undersegmented. e graph cut parameter has been explored and exploited in the area of interactive segmentation with good results [3,4,5]

Summary

Introduction

Graph cut segmentation technique has become popular in recent times because of its ability in segmenting images into foreground and background using a global strategy. E automatic graph cut segmentation is useful as it speeds up cell segmentation, while the interactive segmentation provides the flexibility to select seed points when further investigation needs to be carried out in isolation. A similar approach to Candemir and Akgul [3] is investigated where a canny edge detector is used to obtain object boundaries, which is used to influence how weights are assigned to graph edges in the graph context [8]. Another method of interactive segmentation is proposed [9] where the parameter is learnt from the image. To the best of our knowledge, the investigation of the relevance of the graph cut parameter, in interactive and automatic cell segmentation, has not been carried out before

Materials and Methods

Results

True positive rate

Area under curve