Image Filtering Using Morphological Amoebas

International audienceThis article presents the use of anisotropic dynamic structuring elements, or amoebas, in order to build content-aware noise reduction filters. The amoeba is the ball defined by a special geodesic distance computed for each pixel, and can be used as a kernel for many kinds of filters and morphological operators. 1. Introduction Noise is possibly the most annoying problem in the field of image processing. There are two ways to work around it: either design particularly robust algorithms that can work in noisy environments, or try to eliminate the noise in a first step while losing as little relevant information as possible and consequently use a normally robust algorithm. There are of course many algorithms that aim at reducing the amount of noise in images. In mathematical morphology filters can be, broadly-speaking, divided into two groups: 1 alternate sequential filters based on morphological openings and clos-ings, that are quite effective but also remove thin elements such as canals or peninsulas. Even worse, they can displace the contours and thus create additional problems in a segmentation application

Morphological bilateral filtering

International audienceA current challenging topic in mathematical morphology is the construction of locally adaptive operators; i.e., structuring functions that are dependent on the input image itself at each position. Development of spatially-variant filtering is well established in the theory and practice of Gaussian filtering. The aim of the first part of the paper is to study how to generalize these convolution-based approaches in order to introduce adaptive nonlinear filters that asymptotically correspond to spatially-variant morphological dilation and erosion. In particular, starting from the bilateral filtering framework and using the notion of counter-harmonic mean, our goal is to propose a new low complexity approach to define spatially-variant bilateral structuring functions. Then, in the second part of the paper, an original formulation of spatially-variant flat morphological filters is proposed, where the adaptive structuring elements are obtained by thresholding the bilateral structuring functions. The methodological results of the paper are illustrated with various comparative examples

Contents lists available at ScienceDirect Pattern Recognition

journal homepage: www.elsevier.com/locate/pr Edge-preserving smoothing using a similarity measure in adaptive geodesi

Multiscale approach of retinal blood vessels segmentation based on vessels segmentation with different scales

In this work, the authors developed retinal blood vessels segmentation approach using contrast limited adaptive histogram equalization, morphological filtering, k-means clustering, matched filtering for thin and thick vessels selection. The authors also applied matched filtering for thin vessels selection using the kernels which were built in order to determine the existence of line segments with different length and orientatio

Transmission Electron Microscopy for the Characterization of Cellulose Nanocrystals

Cellulose nanocrystals (CNCs) are high aspect ratio nanomaterials readily obtained from cellulose microfibrils via strong acid hydrolysis. They feature unique properties stemming from their surface chemistry, their crystallinity, and their three-dimensional structure. CNCs have been exploited in a number of applications such as optically active coatings, nanocomposite materials, or aerogels. CNC size and shape determination is an important challenge and transmission electron microscopy (TEM) is one of the most powerful tools to achieve this goal. Because of the specifics of TEM imaging, CNCs require special attention. They have a low density, are highly susceptible to electron beam damage, and easily aggregate. Specific techniques for both imaging and sampling have been developed over the past decades. In this review, we describe the CNCs, their properties, their applications, and the need for a precise characterization of their morphology and size distribution. We also describe in detail the techniques used to record quality images of CNCs. Finally, we survey the literature to provide readers with specific examples of TEM images of CNCs

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

Shot noise, refractive index tomography and aberrations estimation in digital holographic microscopy

The present thesis develops some specific aspects of digital holographic microscopy (DHM), namely the effect of shot noise on the phase image accuracy, the use of DHM in micro-tomography and in aberrations evaluation of a microscope objective (MO). DHM is an imaging technique, allowing to measure quantitatively the wavefront transmitted through or reflected by a specimen seen through a MO. A hologram, composed by the interference of the wave coming from the object with a reference wave, is recorded with a camera and then numerically processed to extract both amplitude and phase information. Thanks to its interferometric nature, DHM provides phase images, corresponding to a nanometric accuracy along the optical axis of the microscope, revealing extremely detailed information about the specimen surface in reflection configuration or its internal structure in transmission configuration. DHM has proven its efficiency on numerous applications fields going from cells biology to MEMS-MOEMS devices. In a first part, the use of DHM as metrological tools in the field of micro-optics testing is demonstrated. DHM measurement principle is compared with techniques employed in Twyman-Green, Mach-Zehnder, and white-light interferometers. Refractive microlenses are characterized with reflection DHM and the data are confronted with data obtained with standard interferometers. Specific features of DHM such as digital focussing, measurement of shape differences with respect to a perfect model, surface roughness measurements, and evaluation of a lens optical performance are discussed. The capability to image nonspherical lenses without modification of the optical setup, a key advantage of DHM against conventional interferometers, is demonstrated on a cylindrical mircrolens and a square lenses array. A second part treats the effect of shot noise in DHM. DHM is a single shot imaging technique, and its short hologram acquisition time (down to microseconds) offers a reduced sensitivity to vibrations. Real time observation is achievable, thanks to present performances of personal computers and digital camera. Fast dynamic imaging at low-light level involves few photons, requiring proper settings of the system (integration time and gain of the camera; power of the light source) to minimize the influence of shot noise on the hologram when the highest phase accuracy is aimed. With simulated and experimental data, a systematic analysis of the fundamental shot noise influence on phase accuracy in DHM is presented. Different configurations of the reference wave and the object wave intensities are also discussed, illustrating the detection limit and the coherent amplification of the object wave. In a third part, DHM has for the first time been applied to perform optical diffraction tomography of biological specimens: a pollen grain and living amoebas. Quantitative 2D phase images are acquired for regularly-spaced angular positions of the specimen covering a total angle of π, allowing to build 3D quantitative refractive index distributions by an inverse Radon transform. A precision of 0.01 for the refractive index estimation and a spatial resolution in the micron range are shown. For the amoebas, morphometric measurements are extracted from the tomographic reconstructions. The fourth part presents a DHM technique to determine the integral refractive index and morphology of cells. As the refractive index is a function of the cell dry mass, depending on the intra-cellular concentration and the organelles arrangement, the optical phase shift induced by the specimen on the transmitted wave can be regarded as a powerful endogenous contrast agent. The dual-wavelengths technique proposed in this thesis exploits the dispersion of the perfusion medium to obtain a set of equations, allowing decoupling the contributions of the refractive index and the cellular thickness to the total phase signal. The two wavelengths are chosen in the vicinity of the absorption peak of a dye added to the perfusion medium, where the absorption is accompanied by a strong variation of the refractive index as a function of the wavelength. The technique is demonstrated on yeasts. The last part exposes two methods capable of measuring the complex 3D amplitude point spread function (APSF) of an optical imaging system. The first approach consists in evaluating in amplitude and phase the image of a single emitting point, a 60 nm diameter tip of a Scanning Near Field Optical Microscopy (SNOM) fiber, with an original digital holographic setup. A single hologram giving access to the transverse APSF, the 3D APSF is obtained by performing an axial scan of the SNOM fiber. The method is demonstrated on an 20x 0.4 NA MO. For a 100x 1.3 NA MO, measurements performed with the new setup are compared with the prediction of an analytical aberrations model. The second method allows measuring the APSF of a MO with a single holographic acquisition of its pupil wavefront. The aberration function is extracted from this pupil measurement and then inserted in a scalar model of diffraction allowing to calculate the distribution of the complex wavefront propagated around the focal point. The results are compared with a direct measurement of the APSF achieved with the first proposed approach