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Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented-with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeonHalobacterium salinarumand the bacteriumEscherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

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The identification of plant species is not a trivial task, since it is carried out by experts and depends on the presence of fruits, flowers and leaves. However, fruits and flowers are not available throughout the year, while leaves are accessible most of the year. In order to assist the specialized work of species identification, methods of texture image analysis are used to extract characteristics from samples of imaged leaves and thus predict the species. Texture image analysis is a versatile and powerful technique able to extract measurements from patterns in the images. Using this technique, recent research has found a close relationship between texture and plant species (da Silva et al., 2015 and 2016). Here, we describe the procedure to extract texture features from microscopic images of leaves using Fourier (Cosgriff, 1960; Azencott, 1997; Gonzalez and Woods, 2006). It is important to highlight that other methods for texture extraction can be used as well. This protocol is split into two parts: (A) leaf epidermal dissociation and (B) automatic method for leaf epidermal image analysis.

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Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

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This paper proposes a methodology for plant analysis and identification based on extracting texture features from microscopic images of leaf epidermis. All the experiments were carried out using 32 plant species with 309 epidermal samples captured by an optical microscope coupled to a digital camera. The results of the computational methods using texture features were compared to the conventional approach, where quantitative measurements of stomatal traits (density, length and width) were manually obtained. Epidermis image classification using texture has achieved a success rate of over 96%, while success rate was around 60% for quantitative measurements taken manually. Furthermore, we verified the robustness of our method accounting for natural phenotypic plasticity of stomata, analysing samples from the same species grown in different environments. Texture methods were robust even when considering phenotypic plasticity of stomatal traits with a decrease of 20% in the success rate, as quantitative measurements proved to be fully sensitive with a decrease of 77%. Results from the comparison between the computational approach and the conventional quantitative measurements lead us to discover how computational systems are advantageous and promising in terms of solving problems related to Botany, such as species identification.

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The correct identification of plants is a common necessity not only to researchers but also to the lay public. Recently, computational methods have been employed to facilitate this task, however, there are few studies front of the wide diversity of plants occurring in the world. This study proposes to analyse images obtained from cross-sections of leaf midrib using fractal descriptors. These descriptors are obtained from the fractal dimension of the object computed at a range of scales. In this way, they provide rich information regarding the spatial distribution of the analysed structure and, as a consequence, they measure the multiscale morphology of the object of interest. In Biology, such morphology is of great importance because it is related to evolutionary aspects and is successfully employed to characterize and discriminate among different biological structures. Here, the fractal descriptors are used to identify the species of plants based on the image of their leaves. A large number of samples are examined, being 606 leaf samples of 50 species from Brazilian flora. The results are compared to other imaging methods in the literature and demonstrate that fractal descriptors are precise and reliable in the taxonomic process of plant species identification.

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Many Content-based Image Retrieval (CBIR) systems and image analysis tools employ color, shape and texture (in a combined fashion or not) as attributes, or signatures, to retrieve images from databases or to perform image analysis in general. Among these attributes, texture has turned out to be the most relevant, as it allows the identification of a larger number of images of a different nature. This paper introduces a novel signature which can be used for image analysis and retrieval. It combines texture with complexity extracted from objects within the images. The approach consists of a texture segmentation step, modeled as a Markov Random Field process, followed by the estimation of the complexity of each computed region. The complexity is given by a Multi-scale Fractal Dimension. Experiments have been conducted using an MRI database in both pattern recognition and image retrieval contexts. The results show the accuracy of the proposed method in comparison with other traditional texture descriptors and also indicate how the performance changes as the level of complexity is altered.

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Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM). In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

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The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists of two steps. First, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. Second, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors that are used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.

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There are many corneal diseases that can be detected using an eye-care device called videokeratograph. The videokeratograph is based on the principle of an apparatus called Placido disc and is used to precisely measure the anterior surface of the cornea. This disc contains rings alternately white and black, which are reflected on the patient's cornea during the examination. The device can find anomalies by analyzing the reflected image, using image-processing algorithms. Although the efficiency of most commercial videokeratographs is acceptable, manufacturers do not disseminate to the scientific community the technique used in the image analysis algorithms. This makes it difficult for the specialized researcher in order to find better algorithms for the image-processing and, consequently, increase the instrument's precision. In this work we have segmented the Placido disc in polar coordinates by implementing a diagonal section of the image, in the radial direction. The objective is to find the inflection points in the signal obtained. In this paper the signal is studied by using the Mumford-Shah segmentation method. The results are compared to those obtained with other classic methods in the literature, e.g. Marr-Hildreth filters, numerical derivative, Fourier derivative, morphological Laplacian and Canny derivative. The best result was achieved by using the Mumford-Shah functional. Using this technique it was possible to find the inflection positions with higher accuracy. The method did not detect any false inflection. Mumford-Shah's method demonstrated also a high precision in the task of eliminating noises from the original signal.

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Among the diseases affecting the commercial citrus production, the citrus black spot (CBS) is considered to cause substantial losses. The analyses of particles in suspension in the orchards and collected into a disc have been applied as a preventive action trying to identify the presence of fungus spores before symptom appearance. In this paper, we show the results of several shape analysis methods applied to the fungus, the first step to the aimed computer aided vision system, capable to assist the identification process. Experiments and comparative results among the methods are presented in this paper, showing that better results were obtained applying the curvature method.

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OBJETIVOS: A videoceratografia é tecnologia que vem sendo utilizada para análise da superfície da córnea desde meados dos anos 80. O objetivo deste trabalho é o desenvolvimento de diferentes técnicas para a detecção de bordas em imagens de Plácido e a comparação dos resultados obtidos em cada algoritmo, visando as conseqüências para o cálculo da curvatura axial. Métodos: Foram capturadas imagens de Plácido de 4 diferentes superfícies esféricas utilizando o equipamento Eyesys system 2000. Cada imagem foi salva no formato bitmap em disco rígido de um computador pessoal IBM PC. Seis algoritmos de processamento de imagem foram desenvolvidos utilizando técnicas documentadas na literatura. Os métodos considerados foram: (1) Derivada numérica de primeira ordem, (2) Derivada de Fourier de primeira ordem, (3) Derivada de Fourier de segunda ordem, (4) Filtro de Marr-Hildreth, (5) Filtro de Canny e (6) Laplaciano morfológico (morfologia matemática). Cada um dos algoritmos foi testado e analisado para imagens de Plácido. Resultados: As distâncias radiais do centro das imagens de Plácido às bordas obtidas por cada algoritmo foram comparadas com uma simulação computacional do sistema VK. A média do desvio padrão em pixels/milímetros/dioptria para todas as esferas para os métodos (1)-(6), respectivamente, foi: (1) 33,1695/0,7961/0,79, (2) 32,79/0,7870/0,7724, (3) 60,7150/1,4572/1,4192, (4)18,97/0,4553/0,4572, (5) 46,33/1,1119/1,0917, (6) 20,55/0,4932/0,48. Conclusão: Pesquisadores e oftalmologistas devem ficar atentos na escolha de equipamentos de videoceratografia e também quando comparar medidas de equipamentos diferentes, uma vez que podem ocorrer diferenças relacionadas ao método de processamento de imagens utilizado pelo fabricante. Demonstramos neste trabalho que o método de Marr-Hildreth é mais preciso que o método de Fourier ou métodos como derivada numérica.

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OBJECTIVE: Videokeratography (VK) has been a widespread technology for corneal surface analysis since the mid 1980s. Most manufactures use personal computers attached to a Placido disc apparatus in order to capture and process digital images. Although precision reported by most manufactures are within very good limits, none of them have disclosed, probably due to proprietary reasons, the nature of the algorithm used in their image-processing phase. This is a problem when researchers want to reproduce or test their own curvature or elevation algorithms on Placido images generated on different commercial videokeratographs or even compare their algorithms on data from different manufactures. Our main objective in this work was to develop certain basic techniques for Placido image edge detection and to compare the results of each algorithm in terms of precision at the image level and also the consequences for axial curvature computations. We also propose that manufactures come forward and at least explain which image-processing technique is used in their own algorithms so other researchers and laboratories can make better use of their data to improve VK algorithms. MATERIAL/METHODS: Placido images from an Eyesys system 2000 were captured for four different spherical surfaces. Each image was saved in bitmap format at the hard disk of an IBM computer. Six different image-processing algorithms were developed using different techniques well documented in the literature. The six methods were as follows: (1) first order numerical derivative, (2) first and (3) second order Fourier derivative, (4) the Marr-Hildreth filter, (5) Canny's method and (6) Mathematical Morphology. Each algorithm was tested on each of the Placido images. RESULTS: Edge radial distance from center of Placido image was compared for each algorithm and a computer simulation of the VK system. The simulated image was used as absolute reference. Another approach was to calculate Axial dioptric power using, again, well documented procedures, and compare the results for each image detection algorithm. Mean deviation in terms of pixels/millimeters/dioptric power for all spheres for methods (1-6) were, respectively, (1) 33.1695/0.7961/0.79, (2) 32.79/0.7870/0.7724, (3) 60.7150/1.4572/1.4192, (4)18.97/0.4553/0.4572, (5) 46.33/1.1119/1.0917 and (6) 20.55/0.4932/0.48. DISCUSSION: All methods have great deviation propagation in terms of dioptric power calculations when the axial algorithm is used and the absolute reference simulated edges are used to generate the calibration curves. This indicates that researchers should be more careful when using resulting image processing files from different videokeratographs to compare their own curvature or elevation algorithms among different instruments or even to measure the absolute precision of their new algorithms.

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PURPOSE: Videokeratography (VK) has been a widespread technology for corneal surface analysis since the mid-80s. The objective of this study was to develop different techniques for Plácido image edge detection and compare the results of each algorithm in terms of the consequences for axial curvature computations. METHODS: Plácido images from an Eyesys system 2000 were captured for 4 different spherical surfaces. Each image was saved in bitmap format at the hard disk of an IBM computer. Six different image-processing algorithms were developed using different techniques well-documented in the literature. The six methods were as follows: (1) First order numerical derivative, (2) First and (3) Second order Fourier derivative, (4) the Marr-Hildreth filter, (5) Canny's Method, (6) Mathematical morphology. Each algorithm was tested on each of the Plácido images. RESULTS: Edge radial distance from center of Plácido image was compared for each algorithm and a computer simulation of the videokeratography system. Mean deviation in terms of pixels/millimeters/dioptric power for all spheres for methods (1)-(6) were, respectively: (1) 33.1695/0.7961/0.79, (2) 32.79/0.7870/0.7724, (3) 60.7150/1.4572/1.4192, (4)18.97/0.4553/0.4572, (5) 46.33/1.1119/1.0917, (6) 20.55/0.4932/0.48. CONCLUSION: Researchers and clinical ophthalmologists should be more careful when choosing commercial videokeratographs and also when comparing measurements of different instruments, given that there may be differences associated with the image processing technique. We have shown here that the Marr/Hildreth (method (4)) image processing method is more precise than other methods such as Fourier or first order numerical methods.

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