KEYWORDS: Image segmentation, Digital holography, 3D image processing, Holography, Visualization, Microscopy, Holograms, 3D image reconstruction, Glasses, 3D modeling
This paper demonstrates a technique that could prove useful for extracting three-dimensional (3D) models
from a single two-dimensional (2D) digital in-line holographic microscopy (DIHM) recording. Multiple intensity
images are reconstructed at a range of depths through a transmissive or partially transmissive scene recorded
by DIHM. A two step segmentation of each of these reconstructed intensity images facilitates the construction
of a data set of surfaces in 3D. First an adaptive thresholding step and then a border following step are
implemented. The surfaces of segmented features are rendered in 3D by applying the marching cubes algorithm
to polygonize the data set. Experimental results for a real world DIHM capture of a transmissive glass sample
are presented to demonstrate this segmentation and visualization process.
Digital holography is the process where an object's phase and amplitude information is retrieved from intensity images
obtained using a digital camera (e.g. CCD or CMOS sensor). In-line digital holographic techniques offer full use of the
recording device's sampling bandwidth, unlike off-axis holography where object information is not modulated onto
carrier fringes. Reconstructed images are obscured by the linear superposition of the unwanted, out of focus, twin
images. In addition to this, speckle noise degrades overall quality of the reconstructed images. The speckle effect is a
phenomenon of laser sources used in digital holographic systems. Minimizing the effects due to speckle noise, removal
of the twin image and using the full sampling bandwidth of the capture device aids overall reconstructed image quality.
Such improvements applied to digital holography can benefit applications such as holographic microscopy where the
reconstructed images are obscured with twin image information. Overcoming such problems allows greater flexibility in
current image processing techniques, which can be applied to segmenting biological cells (e.g. MCF-7 and MDA-MB-
231) to determine their overall cell density and viability. This could potentially be used to distinguish between apoptotic
and necrotic cells in large scale mammalian cell processes, currently the system of choice, within the biopharmaceutical industry.
This study investigates segmentation algorithms applicable to digital holography. An assessment of image segmentation tecnhniques applied to intensity images of reconstructions of digital holograms is provided. Digital holography differs from conventional imaging as 3D information is encoded. This allows depth information to be exploited so that focusing of 3D objects, or part there of, at different depths can be achieved. In this paper, segmentation of features is attained in microscopic and macroscopic scenes. We investigate a number
of recently proposed segmentation techniques including (i) depth from focus, (ii) active contours and (iii) hierarchical thresholding. The influence of noise reduction on the segmentation capabilities of each of the techniques on these scenes is demonstrated. For the macrocsopic scenes, each technique is applied before and
after speckle noise reduction is performed using a wavelet based approach. The performance of the segmentation techniques on the intensity information obtained from reconstructed holograms of microscopic scenes is also investigated before and after twin-image reduction has been applied. A comparison of the techniques
and their performances in these circumstances is provided.
This study explores the effectiveness of wavelet analysis techniques on digital holograms of real-world 3D objects.
Stationary and discrete wavelet transform techniques have been applied for noise reduction and compared. Noise
is a common problem in image analysis and successful reduction of noise without degradation of content is
difficult to achieve. These wavelet transform denoising techniques are contrasted with traditional noise reduction
techniques; mean filtering, median filtering, Fourier filtering. The different approaches are compared in terms of
speckle reduction, edge preservation and resolution preservation.
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