Abstract
It is necessary to extract target specimens from bioholographic images for high-level analysis such as object identification, recognition, and tracking with the advent of application of digital holographic microscopy to transparent or semi-transparent biological specimens. We present an interactive graph cuts approach to segment the needed target specimens in the reconstructed bioholographic images. This method combines both regional and boundary information and is robust to extract targets with weak boundaries. Moreover, this technique can achieve globally optimal results while minimizing an energy function. We provide a convenient user interface, which can easily differentiate the foreground/background for various types of holographic images, as well as a dynamically modified coefficient, which specifies the importance of the regional and boundary information. The extracted results from our scheme have been compared with those from an advanced level-set-based segmentation method using an unbiased comparison algorithm. Experimental results show that this interactive graph cut technique can not only extract different kinds of target specimens in bioholographic images, but also yield good results when there are multiple similar objects in the holographic image or when the object boundaries are very weak.
Highlights
The Gabor digital holograms of the Diatom alga and the Sunflower stem cell were recorded with an image sensor array of 2048 × 2048 pixels with a pixel size of 9 × 9 μm[2], where the specimens were sandwiched between two transparent cover slips
The off-axis digital holograms of the red blood cells (RBCs) were recorded with an image sensor array of 1024 × 1024 pixels with a pixel size of (c)
The interactive graph-cut method, which incorporates regional and boundary information, has been applied to the segmentation of bioholographic images reconstructed from a digital hologram
Summary
Since holography was first presented by Gabor in 1948,1 threedimensional (3-D) optical imaging systems based on digital holography[2,3,4,5,6] are widely viewed as a promising approach in life sciences,[7,8,9,10] defense and security,[11,12,13] medical diagnoses,[14,15,16,17] robotics,[18,19] and medicine.[20,21,22] These systems have the strengths of being able to acquire images rapidly, illustrate the amplitude and phase information conveniently, and apply advanced image-processing techniques to complex field data never possible before. Another famous region-based algorithm is region growing and region splitting-merging.[27] these methods suffer from difficulties in finding appropriate stop criteria for region growing or splitting They will make the segmented boundary not smooth. The second type is the edgebased segmentation.[30] Those well-known algorithms include Sobel, Roberts, Canny, and Hough transformations.[27] All these methods require that the boundary between foreground and background should be distinct. They are very sensitive to noise and get many false edges. It is given a value of less than 0.5
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