Gray Level Mapping Research Articles

Stereological image analysis of cultured human melanocytes observed by transmission electron microscopy.

The aims of the study were to write an image analysis (IA) program allowing the stereological quantification of human epidermal melanocyte melanization at the ultrastructural level and to specify the suitable preparative methods, in keeping with IA limits and stereological principles. Micrographs of cultured human melanocytes obtained in transmission electron microscopy were digitized with a scanner. The key step of the designed IA program is a thresholding based on the gray levels. Hence, gray level histograms (pixel frequency as a function of gray level) of melanocyte images exhibit a peak specific to melanin. The gray level thresholding used consists in isolating the melanin pixels that form profiles on a binary image and in storing the numerical data produced for a given melanocyte profile. These primary data are used to calculate numerous parameters via stereology with melanocyte cytoplasm and melanized melanosome as main reference spaces. The most important stereological parameters obtained are v(mi,cy) (melanin volume per average cell), v(mi,m) (melanin volume per average melanized melanosome), and nm (number of melanized melanosomes per average cell), and their validity is discussed. Melanocytes embedded in situ were abandoned for stereological reasons but pelleted melanocytes were found suitable. Using this computerized tool and stereology, we are able to perform quantitative studies producing varied data even from small cell samples. To our knowledge, this is the first stereological approach for quantifying intracellular melanization. A quantitative comparison of spectrophotometrical results (melanin assay) with stereological results obtained in ultraviolet B-irradiated Caucasian epidermal melanocytes will be performed in order to appraise this method.

Evaluation of three contrast correction methods for digital subtraction in dental radiography: an in vitro study.

In the present in vitro study we test the efficacy of three most often used contrast correction methods to reduce contrast mismatches that can adversely affect digital subtraction in dental radiography. Five radiographs of the lower molars of a beagle dog with an included reference aluminium wedge were radiographed with different exposure times, captured by a monochrome CCD video camera and digitized. To study only the contrast correction effects, the influences of projective geometry and physical noise from the camera and the analog-to-digital conversion were eliminated by simulating bone loss in digital copies of the five reference radiographic images. Each copy in which loss was simulated was contrast corrected by all methods and subtracted from its original image. Four parameters were defined to test the potential of a method to correct grey level values between two images without corrupting real changes. The ODTF (optical density thickness function) method, which is based on a function relating grey level values of the aluminium wedge image and the corresponding thicknesses of the wedge, induced less contrast correction error than the CDF (cumulative density function) and the LSQA (least square quadratic approximation) methods. Therefore, the ODTF method may detect more subtle changes than the other two methods. The experiments indicate that less error is induced by a contrast correction method which uses a grey level mapping function based on the density distribution in a region representing a reference structure. Moreover, CDF, ODTF, and LSQA functions obtained from the reference structure density distribution may be applied for objective contrast enhancements and for standardization of image quality, while the ODTF function also allows bone change volume estimations.

Lesion contrast enhancement in medical ultrasound imaging

Methods for improving the contrast-to-noise ratio (CNR) of low-contrast lesions in medical ultrasound imaging are described. Differences in the frequency spectra and amplitude distributions of the lesion and its surroundings can be used to increase the CNR of the lesion relative to the background. Automated graylevel mapping is used in combination with a contrast-weighted form of frequency-diversity speckle reduction. In clinical studies, the techniques have yielded mean CNR improvements of 3.2 dB above ordinary frequency-diversity imaging and 5.6 dB over sharper conventional images, with no post-processing graylevel mapping.

Image Segmentation by Variational Methods: Mumford and Shah Functional and the Discrete Approximations

In this paper we discuss the links between Mumford and Shah’s variational problem for (signal and) image segmentation, based on an energy functional of a continuous grey-level function, and the numerical algorithms proposed to solve it. These numerical approaches are based on a discrete functional. We recall that, in one dimension, this discrete functional is asymptotically equivalent to the continuous functional. This can be summarized in a $\Gamma $-convergence result. We show that the same result holds in dimension two, provided that the continuous energy is adapted to the anisotropy of the discrete approaches. We display a few experimental results in dimensions one and two.

Using Partial Derivatives of 3D Images to Extract Typical Surface Features

Three-dimensional edge detection in voxel images is used to locate points corresponding to surfaces of 3D structures. The next stage is to characterize the local geometry of these surfaces in order to extract points or lines which may be used by registration and tracking procedures. Typically one must calculate second-order differential characteristics of the surfaces such as the maximum, mean, and Gaussian curvature. The classical approach is to use local surface fitting, thereby confronting the problem of establishing links between 3D edge detection and local surface approximation. To avoid this problem, we propose to compute the curvatures at locations designated as edge points using directly the partial derivatives of the image. By assuming that the surface is defined locally by a isointensity contour (i.e., the 3D gradient at an edge point corresponds to the normal to the surface), one can calculate directly the curvatures and characterize the local curvature extrema (ridge points) from the first, second, and third derivatives of the gray level function. These partial derivatives can be computed using the operators of the edge detection. In the more general case where the contours are not isocontours (i.e., the gradient at an edge point only appoximates the normal to the surface), the only differential invariants of the image are in R 4. This leads us to treat the 3D image as a hypersurface (a three-dimensional manifold) in R 4. We give the relationships between the curvatures of the hypersurface and the curvatures of the surface defined by edge points. The maximum curvature at a point on the hypersurface depends on the second partial derivatives of the 3D image. We note that it may be more efficient to smooth the data in R 4. Moreover, this approach could also be used to detect corners of vertices. We present experimental results obtained using real data (X ray scanner data) and applying these two methods. As an example of the stability, we extract ridge lines in two 3D X ray scanner data of a skull taken in different positions.

SOLVING THE INVERSE PROBLEM FOR FUNCTION/IMAGE APPROXIMATION USING ITERATED FUNCTION SYSTEMS I: THEORETICAL BASIS

We are concerned with function approximation and image representation using Iterated Function Systems (IFS) over ℒp (X, µ): An N-map IFS with grey level maps (IFSM), to be denoted as (w, Φ), is a set w of N contraction maps wi: X → X over a compact metric space (X, d) (the "base space") with an associated set Φ of maps ϕi: R → R. Associated with each IFSM is a contractive operator T with fixed point [Formula: see text]. We provide a rigorous solution to the following inverse problem: Given a target υ ∈ ℒp(X, µ) and an ∊ > 0, find an IFSM whose attractor satisfies [Formula: see text].

Iterated fuzzy set systems: A new approach to the inverse problem for fractals and other sets

Images with grey or colour levels admit a natural representation in terms of fuzzy sets, but without the usual probabilistic interpretation of the latter. We introduce a fuzzy set approach which incorporates, in part, the technique of iterated function systems (IFS) for the construction, analysis, and/or approximation of typically fractal sets and images. The method represents a significant departure from IFS, especially in the interpretation of the resulting image. The introduction of “grey-level maps,” ϑ i : [0, 1] → [0, 1] associated with the contractive maps w i of the IFS affords much greater flexibility in the generation of images as well as in the inverse problem.

Improving the accuracy of direct histogram specification

A new grey-level mapping law for direct histogram specification is proposed. Using this law, the accuracy of mapping the original histogram to the specified histogram can be improved. A comparison between this new method and the currently used one with both artificial and real histograms, and a discussion of the error limits of both mapping laws, are also presented.

Morphological sampling

This paper presents a sampling strategy for gray-level functions based on mathematical morphology. Flexible sampling strategies are required for the construction of multiresolution image representations like pyramids and quad-trees. The sampling operator introduced here is a dilation. A number of different algorithms to reconstruct an image from its sampled version are presented. It is shown that a reconstruction which dominates the original image is obtained by application of the adjoint erosion.

Theoretical aspects of gray-level morphology

After a brief discussion of the extension of mathematical morphology to complete lattices, the space of gray-level functions is considered and the concept of a threshold set is introduced. It is shown how one can use binary morphological operators and thresholding techniques to build a large class of gray-level morphological operators. Particular attention is given to the class of so-called flat operators, i.e. operators which commute with thresholding. It is also shown how to define dilations and erosions with nonflat structuring elements if the gray-level set is finite. It is reported that mere truncation yields wrong results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A review on biomedical image processing and future trends

The last two decades have witnessed a revolutionary development in the field of biomedical and diagnostic imaging. Imaging procedures and modalities which were only in the experimental research phase in the early part of the last two decades, have now become universally accepted clinical procedures. They include computerized tomography (CT), magnetic resonance imaging, ultrasound imaging, nuclear medicine imaging, computerized hematological cell analysis, etc. In the past, the conventional and relatively simple image processing techniques such as image enhancement, gray-level mapping, spectral analysis, region extraction, etc. have been modified for biomedical images and successfully applied for processing and analysis. The role of image enhancement, gray-level mapping, and image reconstruction from projections algorithms in CT and other radiological imaging modalities is well evident. Recently, many advances in biomedical image processing, analysis, and understanding algorithms have shown a great potential for enhancing and interpreting useful diagnostic information from these images more accurately. This paper presents a review on the current state-of-the-art techniques in biomedical image processing and comments on future trends.

Multi-point matching along vertical lines in SPOT images

Abstract Digital elevation models(DEMs) are measured in SPOT images with a digital matching technique. A multi-point matching method developed by the author in earlier work is reformulated from epipolar geometry with the stereoparallaxes in one of the images used as unknowns, to SPOT geometry with the elevations as unknowns. Constraints minimizingthe curvature and the slope of the Computed DEM are investigated, as well as the use of data snooping, low-pass filtering and an alternative logarithmic grey level function. The investigatedimage pair was a stereo SPOT pair from Stockholm, with a rather poor base-to-height ratio of 0·29. All 2073 investigated points were compared with heights obtained from the official Swedish maps, scale 1:10000, with a 5 m contour interval. The results point towards a r.m.s. matching accuracy around 0·15-0·20 pixels in this image material.

Image restoration using a neural network

An approach for restoration of gray level images degraded by a known shift invariant blur function and additive noise is presented using a neural computational network. A neural network model is used to represent a possibly nonstationary image whose gray level function is the simple sum of the neuron state variables. The restoration procedure consists of two stages: estimation of the parameters of the neural network model and reconstruction of images. Owing to the model's fault-tolerant nature and computation capability, a high-quality image is obtained using this approach. A practical algorithm with reduced computational complexity is also presented. A procedure for learning the blur parameters from prototypes of original and degraded images is outlined. >

Gray scale of ultrasound instruments: evaluation using a test object.

B-mode scans of castor oil in a wedge-shaped container provide gray level maps. The relation between the gray levels and the amounts of absorbing medium can be used to measure the effects of electronic gain, transducer characteristics, and the overall gray level performance of different ultrasound systems. We report results from four units and ten transducers for varying scanning depths.

Gray-Level Mapping of Geologic Data: ABSTRACT

Gray-Level Mapping of Geologic Data: ABSTRACT

Computer generated holograms with continuously varying grey level. Application to the synthesis of holographic memory

Facilities of computer generated Fourier holograms are envisioned with the synthesis of a computer holographic memory in view. The computing process described is applicable to a class of objects made up of binary points. A relatively small number of samples is used, and this without any phase encoding. A double threshold amplitude saturation is performed on the Fourier transform, thus optimising the efficiency and S/N ratio, both studied on a theoretical model and by numerical simulation. Non linearities are finally cut out by a very simple filtering process implemented in incoherent light, the result being synthesis of a continuously varying grey level function. A 3 x 3 mm Fourier hologram of a 64 x 64 binary point object restores a highly acceptable image. Nevertheless the photoreduction is slow and limits storage density. A direct point by point discretely recorded hologram is proposed. For this purpose, we describe two alternate systems of a real-time computer-controlled optical microprinter. Using the above methods, characteristics are outlined for computers holographic memories.

Fortran iv program for grey-level mapping of spatial data

Afortran iv computer program for grey-level mapping of spatial data using a CDC 6600 is described. The program produces maps for irregularly distributed data in as many as 10 intensity levels which are displayed in grey tones by a lineprinter. Unlimited map size and unrestricted data input provide for as many as 5000 data points per 13-in.-wide strip of map. The program is easily modified for other installations. Examples are given of geochemical analyses of stream sediments in Northern Ireland.

Fortran IV program for grey-level mapping of spatial data

Fortran IV program for grey-level mapping of spatial data