A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced. A review of watersheds and related motion is first presented, and the major methods to determine watersheds are discussed. The algorithm is based on an immersion process analogy, in which the flooding of the water in the picture is efficiently simulated using of queue of pixel. It is described in detail provided in a pseudo C language. The accuracy of this algorithm is proven to be superior to that of the existing implementations, and it is shown that its adaptation to any kind of digital grid and its generalization to n-dimensional images (and even to graphs) are straightforward. The algorithm is reported to be faster than any other watershed algorithm. Applications of this algorithm with regard to picture segmentation are presented for magnetic resonance (MR) imagery and for digital elevation models. An example of 3-D watershed is also provided. >

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Watersheds in digital spaces: an efficient algorithm based on immersion simulations

Gctf: Real-time CTF determination and correction

Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

/pdf/anisotropic-diffusion-in-image-processing-z13u09qvsm.pdf

Anisotropic diffusion in image processing

Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm. >

/pdf/morphological-grayscale-reconstruction-in-image-analysis-nh3wqbbs21.pdf

Morphological grayscale reconstruction in image analysis: applications and efficient algorithms

A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored. >

Thinning methodologies-a comprehensive survey

https://repository.tudelft.nl/islandora/object/uuid%3A6fea1bc9-de9c-450f-8e34-a33c1129589a/datastream/OBJ/download

Recursive implementation of the Gaussian filter

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.

/pdf/robust-fusion-of-irregularly-sampled-data-using-adaptive-1iyjnl4vdp.pdf

Robust fusion of irregularly sampled data using adaptive normalized convolution

The impact of freeze-drying on microstructure and rehydration properties of carrot

In this paper we present methods for characterizing CCD cameras. Interesting properties are linearity of photometric response, signal-to-noise ratio, sensitivity, dark current, and spatial frequency response. The techniques to characterize CCD cameras are carefully designed to assist one in selecting a camera to solve a certain problem. The methods described were applied to a variety of cameras: an Astromed TE3/A with P86000 chip, a Photometrics CC200 series with Thompson chip TH7882, a Photometrics CC200 series with Kodak chip KAF1400, a Xillix' Micro Imager 1400 with Kodak chip KAF1400, an HCS MXR CCD with a Philips chip and a Sony XC-77RRCE.

/pdf/methods-for-ccd-camera-characterization-4yyz7sfusb.pdf

Methods for CCD Camera Characterization

Fluorescence in situ hybridization allows the enumeration of chromosomal abnormalities in interphase cell nuclei This process is called dot counting To estimate the distribution of chromosomes per cell, a large number of cells have to be analyzed, especially when the frequency of aberrant cells is low Automation of dot counting is required because manual counting is tedious, fatiguing, and time-consuming We developed a completely automated fluorescence microscope system that can examine 500 cells in approximately 15 min to determine the number of labeled chromosomes (seen as dots) in each cell nucleus This system works with two fluorescent dyes, one for the DNA hybridization dots and one for the cell nucleus After the stage has moved to a new field, the image is automatically focused, acquired by a Photometrics KAF 1400 camera (Photometrics Ltd, Tuscon, AZ, USA), and then analyzed on a Macintosh Quadra 840AV (Apple Computer, Inc, Cupertino, CA, USA) computer After the required number of cells has been analyzed, the user may interact to correct the computer by working with a gallery of the cell images The automated dot counter has been tested on a number of normal specimens where 4,'6-diamidino-2-phenylindole (DAPI) was used for the nucleus counterstain and a centromeric 8 probe was used to mark the desired chromosome The slides contained lymphocytes from cultured blood We compared the results of the dot counter with manual counting Manually obtained results, published in the literature, were used as the "ground truth" For a normal specimen, 975% of cells will have two dots Fully automated scanning of 13 slides showed that an average of 89% of all nuclei were counted correctly In other words, an average of 11% has to be interactively corrected, using a monitor display The machine accuracies, after interactive correction, are comparable to panels of human experts (manual) The fully automatically obtained results are biased with respect to manual counting An error analysis is given, and different causes are discussed

Lucas J. van Vliet

Papers

Recursive implementation of the Gaussian filter

Robust fusion of irregularly sampled data using adaptive normalized convolution

The impact of freeze-drying on microstructure and rehydration properties of carrot

Methods for CCD Camera Characterization

FISH and chips: automation of fluorescent dot counting in interphase cell nuclei.