Rodrigo Nava

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.

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.

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

Mutual information improves image fusion quality assessments

The goal of image fusion techniques is to combine and preserve all of the important visual information present in multiple input images in a single output image. In many applications, the quality of the fused images is of fundamental importance and is usually assessed by visual analysis subjective to the interpreter. Many objective quality metrics exist in image fusion,1 but when no clearly-defined ground truth exists, we must construct an ideal fused image to use as a reference for comparing with the experimental results. Among the available ways to measure quality, both the mean square error (MSE) and signal-to-noise ratio (SNR) metrics are widely employed because they are easy to calculate and typically have low computational costs. Other metrics such as the Wigner signal-to-noise ratio (SNRw) and structural similarity quality index (SSIM)3 have been recently proposed, but these metrics require a reference image together with the processed image. Non-reference metrics are much more difficult to define, as knowledge of ground truth is not assumed. These metrics are not relative to an original image.4 Here we use mutual information (MI) as an information measure for evaluating image fusion performance. This measure represents how much of the information in the final fused image was obtained from the input images.

Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy

Pollen collection: 15 types - 120 samples/type.Proposal of contour-inner pollen segmentation: 50% accuracy rates.New contour profile descriptor.LogGabor descriptors firstly tested for pollen classification.Experiments of descriptors state of the art combination: rates above 99%. Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines computer vision comes to alleviate tedious recognition tasks. In this paper we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour-inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grains contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.

Extended Gabor approach applied to classification of emphysematous patterns in computed tomography

AbstractChronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance.

Texture descriptor approaches to level set segmentation in medical images

Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation; such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values.

Advances in Texture Analysis for Emphysema Classification

In recent years, with the advent of High-resolution Computed Tomography (HRCT), there has been an increased interest for diagnosing Chronic Obstructive Pulmonary Disease (COPD), which is commonly presented as emphysema. Since low-attenuation areas in HRCT images describe different emphysema patterns, the discrimination problem should focus on the characterization of both local intensities and global spatial variations. We propose a novel texture-based classification framework using complex Gabor filters and local binary patterns. We also analyzed a set of global and local texture descriptors to characterize emphysema morphology. The results have shown the effectiveness of our proposal and that the combination of descriptors provides robust features that lead to an improvement in the classification rate.

Supertexton-based segmentation in early Drosophila oogenesis

Studies concerning gene expression patterns of Drosophila are of paramount importance in basic biological research because many genes are conserved across organisms providing information of fundamental activity. However, mapping a gene requires analyzing hundreds of objects that have been segmented previously. Hence, a reliable segmentation is a crucial step. Here, we introduce the concept of supertextons and propose a novel segmentation procedure for localized Drosophila ovaries. First, a pre-segmentation step is performed using superpixels; each superpixel that belongs to a single class is transform into a feature vector. Then, a dictionary is built by clustering representative feature vectors per class, such clusters are called supertextons. Finally, during the classification stage, new superpixels are assigned to certain classes using the k-NN classifier and the supertexton dictionary. This proposal has been applied to segmentation of cells in Drosophila oogenesis where the results have shown the effectiveness of our approach.