Gabriel Cristóbal

Blind image quality assessment through anisotropy

We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.

Identification of tuberculosis bacteria based on shape and color

Tuberculosis and other mycobacteriosis are serious illnesses which control is based on early diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid high errors. Image-processing techniques provide a good tool for improving the manual screening of samples. In this paper, a new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. This technique is based on the combined use of some invariant shape features together with a simple thresholding operation on the chromatic channels. Some feature descriptors have been extracted from bacilli shape using an edited dataset of samples. A k-means clustering technique was applied for classification purposes and the sensitivity vs specificity results were evaluated using a standard ROC analysis procedure.

A Unified Approach to Superresolution and Multichannel Blind Deconvolution

This paper presents a new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Several experiments on synthetic and real data illustrate the robustness and utilization of the proposed technique in real applications.

Multifocus image fusion using the log-Gabor transform and a Multisize Windows technique

Today, multiresolution (MR) transforms are a widespread tool for image fusion. They decorrelate the image into several scaled and oriented sub-bands, which are usually averaged over a certain neighborhood (window) to obtain a measure of saliency. First, this paper aims to evaluate log-Gabor filters, which have been successfully applied to other image processing tasks, as an appealing candidate for MR image fusion as compared to other wavelet families. Consequently, this paper also sheds further light on appropriate values for MR settings such as the number of orientations, number of scales, overcompleteness and noise robustness. Additionally, we revise the novel Multisize Windows (MW) technique as a general approach for MR frameworks that exploits advantages of different window sizes. For all of these purposes, the proposed techniques are firstly assessed on simulated noisy experiments of multifocus fusion and then on a real microscopy scenario.

Self-Invertible 2D Log-Gabor Wavelets

Orthogonal and biorthogonal wavelets became very popular image processing tools but exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation invariance due to aliasing between subbands. Alternative multiresolution transforms which specifically solve these drawbacks have been proposed. These transforms are generally overcomplete and consequently offer large degrees of freedom in their design. At the same time their optimization gets a challenging task. We propose here the construction of log-Gabor wavelet transforms which allow exact reconstruction and strengthen the excellent mathematical properties of the Gabor filters. Two major improvements on the previous Gabor wavelet schemes are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. Secondly, the set of filters cover uniformly the Fourier domain including the highest and lowest frequencies and thus exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images. Compared to the state of the art, the log-Gabor wavelets show excellent ability to segregate the image information (e.g. the contrast edges) from spatially incoherent Gaussian noise by hard thresholding, and then to represent image features through a reduced set of large magnitude coefficients. Such characteristics make the transform a promising tool for processing natural images.

Diatom autofocusing in brightfield microscopy: a comparative study

We present a number of autofocusing methods in light microscopy for use in diatom identification. Among these, the Tenengrad method has been considered one of the best. The basic requirements for a practical autofocusing system are speed, sharpness and robustness to noise. Recently other focus measures based on a modified Laplacian method are said to perform better than Tenengrad. We investigate two sound methods based on a modified Tenengrad and a modified Laplacian. Measurements show that they provide a reliable and suitable focus measure that outperform similar methods. We investigate the window size analysis dependency and perform an univariate analysis on the focus measures. The focusing techniques are implemented in an automatic slide scanning system for diatom detection and identification for its use in the ADIAC project.

Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models

Tuberculosis and other kinds of mycobacteriosis are serious illnesses for which early diagnosis is critical for disease control. Sputum sample analysis is a common manual technique employed for bacillus detection but current sample‐analysis techniques are time‐consuming, very tedious, subject to poor specificity and require highly trained personnel. Image‐processing and pattern‐recognition techniques are appropriate tools for improving the manual screening of samples. Here we present a new technique for sputum image analysis that combines invariant shape features and chromatic channel thresholding. Some feature descriptors were extracted from an edited bacillus data set to characterize their shape. They were statistically represented by using a Gaussian mixture model representation and a minimal error Bayesian classification procedure was employed for the last identification stage. This technique constitutes a step towards automating the process and providing a high specificity.

Separating the chaff from the wheat: possible origins of the oblique effect

The oblique effect refers to a better perception of horizontal and vertical image features as compared with the perception at oblique angles. This effect can be observed in both animals and humans. Recent neurophysiological data suggest that the basis of this effect lies in the structure of the primary visual cortex, where more cortical area is devoted to processing contours with angles at horizontal and vertical orientations (cardinal orientations). It has been suggested that this cortical feature has developed according to the statistical properties of natural scenes. To examine this hypothesis in more detail, we established six image classes and categorized the images with respect to their semantical contents. From the images the oriented energy was calculated by using the corresponding power spectra. We defined simple measures for the degree (cardinal versus oblique energy ratio) and the skewness or anisotropy (aligned energy ratio) of the alignment of energy at horizontal and vertical orientations. Our results provide evidence that (1) alignment depends strongly on the environment, (2) the degree of alignment drops off characteristically at higher frequencies, and (3) in natural images there is on the average an anisotropy in the distribution of energy at the cardinal orientations (i.e., a difference between the amounts of vertical energy and horizontal energy). In light of our results, we further discuss whether the observed cortical anisotropy has its origin in phylogeny or ontogeny.

Automatic identification techniques of tuberculosis bacteria

Tuberculosis is a serious illness which control is mainly based on presumptive diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. However, the analysis of sputum is quite expensive, time consuming and requires highly trained personnel to avoid high errors. Image processing techniques provide a good tool for improving the manual screening of samples. In this paper we present a new bacilli detection technique with the aim to attain a high specificity rate and therefore for reducing the time required to analyze such sputum samples. This technique is based on the neuristic acknowlege extracted from the bacilli shape contour. It uses also the color information for image segmentation and finally a classification tree is used to categorize if a sample is positive or negative.

Sparse overcomplete Gabor wavelet representation based on local competitions

Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation.