Processing of 3D DIC microscopy images for data visualization

Feineigle, Patricia A.; Witkin, Andrew; Stonick, V.L.

doi:10.1109/icassp.1996.545847

Cited by 16 publications

(21 citation statements)

References 2 publications

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“…For comparison we use the results of four other DIC reconstruction algorithms, namely the Wiener filtering (Heise05) [2], an inverse filtering based (Yin11) [4], a previously published variational framework (Feineigle96) [7] and a linear equations system solver (Li09) [5]. Where it is possible, we use quantitative numerical comparison.…”

Section: Resultsmentioning

confidence: 99%

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DIC Microscopy Image Reconstruction Using a Novel Variational Framework

Koós

Molnár

Horváth

2015

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Quantitative microscopy (QM) became a key tool in systems-level drug discovery and disease diagnosis such as cancers and neurodegenerative disorders. However, to date QM is limited to epifluorescence microscopy which requires chemical labels, special imaging modality and often causes phototoxicity. Differential Interference Contrast (DIC) microscopy is label free and is low-phototoxic, thus it has great advantages over epifluorescence microscopy in numerous applications. Yet, DIC is not used for QM because the acquired images are not feasible directly for quantitative analysis. In this paper we propose a novel variational framework for DIC image reconstruction, enabling the modality for QM. Our energy functional uses a term that ensures similarity to the original DIC image and the total variation regularization term. The first term utilizes the point spread function (PSF) of the DIC microscope. The PSF is incorporated to our model by local integrals. We show that the derivation operation can be moved from the kernel to the image, which significantly accelerates the computations. The method outperforms other algorithms on synthetic and real DIC images.

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Section: Resultsmentioning

confidence: 99%

“…Here we present a variational framework. A basic variational approach has already been proposed in [7]. Our method is based on the principles of DIC microscopy image formation model.…”

Section: Introductionmentioning

confidence: 99%

DIC Microscopy Image Reconstruction Using a Novel Variational Framework

Koós

Molnár

Horváth

2015

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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“…This size is preserved when the FT is taken of each, allowing the two resulting frequency domain matrices to be multiplied directly. 3 This also applies to the 3-D case.…”

Section: F Morlet Filter Constructionmentioning

confidence: 97%

“…At each pixel location in a traverse, the real and imaginary Morlet values at the current frequency are calculated, 3 Multiplication is not matrix multiplication, but rather, point-to-point. based on (15) and (16), and added to the complex Morlet value at that location.…”

Section: F Morlet Filter Constructionmentioning

confidence: 99%

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Feature extraction of chromosomes from 3-D confocal microscope images

Kyan

Guan

Arnison

et al. 2001

IEEE Trans. Biomed. Eng.

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Abstract-An investigation of local energy surface detection integrated with neural network techniques for image segmentation is presented, as applied in the feature extraction of chromosomes from image datasets obtained using an experimental confocal microscope. Use of the confocal microscope enables biologists to observe dividing cells (living or preserved) within a three-dimensional (3-D) volume, that can be visualised from multiple aspects, allowing for increased structural insight. The Nomarski differential interference contrast mode used for imaging translucent specimens, such as chromosomes, produces images not suitable for volume rendering. Segmentation of the chromosomes from this data is, thus, necessary.A neural network based on competitive learning, known as Kohonen's self-organizing feature map (SOFM) was used to perform segmentation, using a collection of statistics or features defining the image. Our past investigation showed that standard features such as the localized mean and variance of pixel intensities provided reasonable extraction of objects such as mitotic chromosomes, but surface detail was only moderately resolved. In this current work, a biologically inspired feature known as local energy is investigated as an alternative image statistic based on phase congruency in the image. This, along with different combinations of other image statistics, is applied in a SOFM, producing 3-D images exhibiting vast improvement in the level of detail and clearly isolating the chromosomes from the background.Index Terms-DIC, differential interference contrast, feature extraction, feature space, image segmentation, local energy, Morlet wavelet, phase congruency, self organizing feature map, SOFM.

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