Boris Escalante-Ramírez

Comparative Evaluation of Autofocus Algorithms for a Real-Time System for Automatic Detection of Mycobacterium tuberculosis

Microscopy images must be acquired at the optimal focal plane for the objects of interest in a scene. Although manual focusing is a standard task for a trained observer, automatic systems often fail to properly find the focal plane under different microscope imaging modalities such as bright field microscopy or phase contrast microscopy. This article assesses several autofocus algorithms applied in the study of fluorescence‐labeled tuberculosis bacteria. The goal of this work was to find the optimal algorithm in order to build an automatic real‐time system for diagnosing sputum smear samples, where both accuracy and computational time are important. We analyzed 13 focusing methods, ranging from well‐known algorithms to the most recently proposed functions. We took into consideration criteria that are inherent to the autofocus function, such as accuracy, computational cost, and robustness to noise and to illumination changes. We also analyzed the additional benefit provided by preprocessing techniques based on morphological operators and image projection profiling.

Advanced modeling of visual information processing: A multi-resolution directional-oriented image transform based on Gaussian derivatives

Abstract In this work, a multi-channel model for image representation is derived based on the scale-space theory. This model is inspired in biological insights and includes some important properties of human vision such as the Gaussian derivative model for early vision proposed by Young [The Gaussian derivative theory of spatial vision: analysis of cortical cell receptive field line-weighting profiles, General Motors Res. Labs. Rep. 4920, 1986]. The image transform that we propose in this work uses analysis operators similar to those of the Hermite transform at multiple scales, but the synthesis scheme of our approach integrates the responses of all channels at different scales. The advantages of this scheme are: (1) Both analysis and synthesis operators are Gaussian derivatives. This allows for simplicity during implementation. (2) The operator functions possess better space-frequency localization, and it is possible to separate adjacent scales one octave apart, according to Wilsons results on human vision channels. [H.R. Wilson, J.R. Bergen, A four mechanism model for spatial vision. Vision Res. 19 (1979) 19–32). (3) In the case of two-dimensional (2-D) signals, it is easy to analyze local orientations at different scales. A discrete approximation is also derived from an asymptotic relation between the Gaussian derivatives and the discrete binomial filters. We show in this work how the proposed transform can be applied to the problems of image coding, noise reduction and image fusion. Practical considerations are also of concern.

Automated pollen identification using microscopic imaging and texture analysis

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralicks gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fishers discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques.

Autofocus evaluation for brightfield microscopy pathology

An essential and indispensable component of automated microscopy framework is the automatic focusing system, which determines the in-focus position of a given field of view by searching the maximum value of a focusing function over a range of z-axis positions. The focus function and its computation time are crucial to the accuracy and efficiency of the system. Sixteen focusing algorithms were analyzed for histological and histopathological images. In terms of accuracy, results have shown an overall high performance by most of the methods. However, we included in the evaluation study other criteria such as computational cost and focusing curve shape which are crucial for real-time applications and were used to highlight the best practices.

Multidimensional characterization of the perceptual quality of noise-reduced computed tomography images

Judging the perceptual quality of processed images is a cognitive process in which the perception of image attributes such as sharpness and noisiness plays an important role. In this paper, we use multidimensional scaling to study the perceptual factors that influence the quality impression of Computed Tomography (CT) images processed by a noise-reduction technique. We also characterize intersubject differences in the assessment of image attributes. We show that multidimensional scaling can be used reliably for the characterization of the subjective performance of image-processing algorithms. Evaluations using human subjects, such as the ones presented in this paper, will continue to play an important role, since objective measures for perceptual image quality, with proven validity in a broad range of applications, are not expected in the near future.

The Hermite transform as an efficient model for local image analysis: An application to medical image fusion

The Hermite transform is introduced as an image representation model that can be used to tackle the problem of fusion in multimodal medical imagery. This model includes some important properties of human visual perception, such as local orientation analysis and the Guassian derivative model of early vision. Local analysis is achieved by windowing the image with a Gaussian function, then a local expansion into orthogonal polynomials takes place at every window position. Expansion coefficients are called Hermite coefficients and it is shown that they can be directly obtained by convolving the image with Gaussian derivative filters, in agreement with psychophysical insights of human visual perception. A compact representation can be obtained by locally steering the Hermite coefficients towards the direction of local maximum energy. Image fusion is achieved by combining the steered Hermite coefficients of both source images with the method of verification of consistency. Fusion results are compared with a competitive wavelet-based technique, proving that the Hermite transform provides better reconstruction of relevant image structures.

Texture Image Retrieval Based on Log-Gabor Features

Since Daugman found out that the properties of Gabor filters match the early psychophysical features of simple receptive fields of the Human Visual System (HVS), they have been widely used to extract texture information from images for retrieval of image data. However, Gabor filters have not zero mean, which produces a non-uniform coverage of the Fourier domain. This distortion causes fairly poor pattern retrieval accuracy. To address this issue, we propose a simple yet efficient image retrieval approach based on a novel log-Gabor filter scheme. We make emphasis on the filter design to preserve the relationship with receptive fields and take advantage of their strong orientation selectivity. We provide an experimental evaluation of both Gabor and log-Gabor features using two metrics, the Kullback-Leibler (D KL ) and the Jensen-Shannon divergence (D JS ). The experiments with the USC-SIPI database confirm that our proposal shows better retrieval performance than the classic Gabor features. 3

Rotation-invariant texture features from the steered Hermite transform

Highlights? We use the steered Hermite transform to obtain rotation-invariant texture features. ? A feature selection strategy determines the filters with better classification power. ? Feature selection and dimension reduction was based on the augmented variance ratio. ? We evaluate classification accuracy for some kinds of texture features. ? The first successive orders of the Hermite transform increase classification power. We propose the steered Hermite transform to analyze and capture visual patterns from textures regardless their orientation. Visual texture information is locally described as one dimensional patterns by steering the Cartesian Hermite coefficients according to the energy direction; therefore, no predefined orientation selective filters are required. We evaluate classification accuracy of some texture features individually. During the training stage, a filter selection strategy based on the augmented variance ratio analysis of the training features is employed in order to determine the filters that provide better classification accuracy and reduce computational costs during the classification stage.

Lake Chapala change detection using time series

The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lakes limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

Image fusion with the Hermite transform

The Hermite transform is an image representation model that incorporates some important properties of visual perception such as the analysis through overlapping receptive fields and the Gaussian derivative model of early vision. It also allows the construction of pyramidal multiresolution analysis-synthesis schemes. We show how the Hermite transform can be used to build image fusion schemes that take advantage of the fact that Gaussian derivatives are good operators for the detection of relevant image patterns at different spatial scales. These patterns are later combined in the transform coefficient domain. Applications of this fusion algorithm are found in medical imagery and remote sensing, name.