Digitally sampled in-line holograms may be linearly filtered to reconstruct a representation of the original object distribution, thereby decoding the information contained in the hologram. The decoding process is performed by digital computation rather than optically. Substitution of digital for optical decoding has several advantages, including selective suppression of the twin-image artifact, elimination of the far-field requirement, and automation of the data reduction and analysis process. The proposed filter is a truncated series expansion of the inverse of that operator that maps object opacity function to hologram intensity. The first term of the expansion is shown to be equivalent to conventional (optical) reconstruction, with successive terms increasingly sup-pressing the twin image. The algorithm is computationally efficient, requiring only a single fast Fourier transform pair.

Digital decoding of in-line holograms

A method is given for the evaluation, in transmission electron microscopy, of the amplitude and phase from the intensity distribution of an electron micrograph. The method requires a minimum of two micrographs taken under different defocus conditions. The iterative scheme requires only the relative defocus between micrographs, and the procedure is valid both in bright-field and dark-field microscopy for any specified coherence of the electron source. Assumptions on the scattering properties of the specimen, such as the weak-phase-weak-amplitude object, are not required. For a complete determination of the amplitude-phase distribution for electron transmission through the specimen, the electron micrograph must be corrected for the effect of lens aberrations and defocusing to give the electron wavefunction immediately after transmission; only in the case of a weak-phase object can this wavefunction be directly related to the projected potential distribution in the object. Inelastic electron scattering is explicitly omitted from the analysis presented.

Comment onA method for the solution of the phase problem in electron microscopy

Red blood cells, or erythrocytes, are seen to flicker under optical microscopy, a phenomenon initially described as thermal fluctuations of the cell membrane. But recent studies have suggested the involvement of non-equilibrium processes, without definitively ruling out equilibrium interpretations. Using active and passive microrheology to directly compare the membrane response and fluctuations on single erythrocytes, we report here a violation of the fluctuation–dissipation relation, which is a direct demonstration of the non-equilibrium nature of flickering. With an analytical model of the composite erythrocyte membrane and realistic stochastic simulations, we show that several molecular mechanisms may explain the active fluctuations, and we predict their kinetics. We demonstrate that tangential metabolic activity in the network formed by spectrin, a cytoskeletal protein, can generate curvature-mediated active membrane motions. We also show that other active membrane processes represented by direct normal force dipoles may explain the observed membrane activity. Our findings provide solid experimental and theoretical frameworks for future investigations of the origin and function of active motion in cells. The membranes of red blood cells exhibit a flickering motion that has long been ascribed a thermal origin. Microrheology experiments provide direct evidence that flickering is an active process characterized by non-equilibrium dynamics.

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Equilibrium physics breakdown reveals the active nature of red blood cell flickering

The use of color in image processing is motivated by two principal factors; First color is a powerful descriptor that often simplifies object identification and extraction from a scene. Second, human can discern thousands of color shades and intensities, compared to about only two dozen shades of gray. In RGB model, each color appears in its primary spectral components of red, green and blue. This model is based on Cartesian coordinate system. Images represented in RGB color model consist of three component images. One for each primary, when fed into an RGB monitor, these three images combines on the phosphor screen to produce a composite color image. The number of bits used to represent each pixel in RGB space is called the pixel depth. Consider an RGB image in which each of the red, green and blue images is an 8-bit image. Under these conditions each RGB color pixel is said to have a depth of 24 bit. MATLAB 7.0 2007b was used for the implementation of all results.

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A Theory Based on Conversion of RGB image to Gray image

The membranes of red blood cells exhibit a flickering motion that has long been ascribed a thermal origin. Microrheology experiments provide direct evidence that flickering is an active process characterized by non-equilibrium dynamics.

The task of segmenting cell nuclei in microscope images is a classical image analysis problem. The accurate nuclei segmentation may contribute to development of successful system which automate the analysis of microscope images for pathology detection. In this article we describe a method for semi-supervised training of fuzzy logic engine. The fuzzy logic engine is applied to connect a set of parameters proven to be important for nucleus segmentation. In addition each parameter for itself is detected using a set of fuzzy logic rules. We present results of nuclei segmentation using fuzzy logic set of rules.

Cell nuclei segmentation using fuzzy logic engine

Diagnosis of malaria must be rapid, accurate, simple to use, portable and low cost, as suggested by the World Health Organization (WHO) Despite recent efforts, the gold standard remains the light microscopy of a stained blood film This method can detect low parasitemia and identify different species of Plasmodium However, it is time consuming, it requires well trained microscopist and good instrumentation to minimize misinterpretation, thus the costs are considerable Moreover, the equipment cannot be easily transported and installed In this paper we propose a new technique named “secondary speckle sensing microscopy” (S3M) based upon extraction of correlation based statistics of speckle patterns generated while illuminating red blood cells with a laser and inspecting them under a microscope Then, using fuzzy logic ruling and principle component analysis, good quality of separation between healthy and infected red blood cells was demonstrated in preliminary experiments The proposed technique can be used for automated high rate detection of malaria infected red blood cells

Toward fast malaria detection by secondary speckle sensing microscopy

We present a new configuration for superresolution (SR) as well as for field-of-view (FOV) extension in a digital holography concept based on random movement of sparse metallic particles. In the SR configuration, the particles are in proximity to the recorded object, while in the FOV configuration, the particles are in proximity to the hologram plane. The particles' movement encodes the high spatial features in the plane of their movement. This high-resolution information can later be decoded by proper numerical postprocessing that either remedies the resolution limitations in the object plane (or the limited NA of the lens) or extends the FOV in the object plane.

Superresolved and field-of-view extended digital holography with particle encoding

The subject of extracting high-resolution data from low-resolution images is one of the most important digital processing applications in recent years, attracting much research. In this work the authors show how to improve the resolution of an image when a small part of the image is given in high-resolution. To obtain this result the authors use an iterative procedure imposing the low frequencies complete data of the original low-resolution image and the high-resolution data present only in a fraction of the image. The procedure is based on the Gerchberg-Papoulis algorithm and contains dynamic properties, not present in the original scheme. The result is a clearer image, with higher correlation to the required high-resolution image. The authors show the use of such a procedure on a rosette image and on a facial image to demonstrate the higher frequencies obtained and on a text sample to show improvement in textual understanding.

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Single-Image Digital Super-Resolution A Revised Gerchberg-Papoulis Algorithm

Text recognition and retrieval is a well known problem. Automated optical character recognition (OCR) tools do not supply a complete solution and in most cases human inspection is required. In this paper the authors suggest a novel text recognition algorithm based on usage of fuzzy logic rules relying on statistical data of the analyzed font. The new approach combines letter statistics and correlation coefficients in a set of fuzzy based rules, enabling the recognition of distorted letters that may not be retrieved otherwise. The authors focused on Rashi fonts associated with commentaries of the Bible that are actually handwritten calligraphy.

Eran Gur

Papers

Cell nuclei segmentation using fuzzy logic engine

Toward fast malaria detection by secondary speckle sensing microscopy

Superresolved and field-of-view extended digital holography with particle encoding

Single-Image Digital Super-Resolution A Revised Gerchberg-Papoulis Algorithm

Retrieval of Rashi Semi-cursive Handwriting via Fuzzy Logic