Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Background Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. Results In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Conclusions Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.

Texture analysis using graphs generated by deterministic partially self-avoiding walks

Texture is one of the most important visual attributes for image analysis. It has been widely used in image analysis and pattern recognition. A partially self-avoiding deterministic walk has recently been proposed as an approach for texture analysis with promising results. This approach uses walkers (called tourists) to exploit the gray scale image contexts in several levels. Here, we present an approach to generate graphs out of the trajectories produced by the tourist walks. The generated graphs embody important characteristics related to tourist transitivity in the image. Computed from these graphs, the statistical position (degree mean) and dispersion (entropy of two vertices with the same degree) measures are used as texture descriptors. A comparison with traditional texture analysis methods is performed to illustrate the high performance of this novel approach. (C) 2011 Elsevier Ltd. All rights reserved.

Multiscale Curvature Analysis of Asphaltic Aggregate Particles

A new method for characterization and analysis of asphaltic mixtures aggregate particles is reported. By relying on multiscale representation of the particles, curvature estimation, and discriminant analysis for optimal separation of the categories of mixtures, a particularly effective and comprehensive methodology is obtained. The potential of the methodology is illustrated with respect to three important types of particles used in asphaltic mixtures, namely basalt, gabbro, and gravel. The obtained results show that gravel particles are markedly distinct from the other two types of particles, with the gabbro category resulting with intermediate geometrical properties. The importance of each considered measurement in the discrimination between the three categories of particles was also quantified in terms of the adopted discriminant analysis.

Texture analysis based on maximum contrast walker

Recently, the deterministic tourist walk has emerged as a novel approach for texture analysis. This method employs a traveler visiting image pixels using a deterministic walk rule. Resulting trajectories provide clues about pixel interaction in the image that can be used for image classification and identification tasks. This paper proposes a new walk rule for the tourist which is based on contrast direction of a neighborhood. The yielded results using this approach are comparable with those from traditional texture analysis methods in the classification of a set of Brodatz textures and their rotated versions, thus confirming the potential of the method as a feasible texture analysis methodology. (C) 2010 Elsevier B.V. All rights reserved.

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter`s behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (SD(slope)) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as SD(slope), and it is sensitive to noise and spurious data. In general, SD(slope) offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

Objective: To develop a method for objective quantification of PD motor symptoms related to Off episodes and peak dose dyskinesias, using spiral data gathered by using a touch screen telemetry device. The aim was to objectively characterize predominant motor phenotypes (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Background: A retrospective analysis was conducted on recordings from 65 patients with advanced idiopathic PD from nine different clinics in Sweden, recruited from January 2006 until August 2010. In addition to the patient group, 10 healthy elderly subjects were recruited. Upper limb movement data were collected using a touch screen telemetry device from home environments of the subjects. Measurements with the device were performed four times per day during week-long test periods. On each test occasion, the subjects were asked to trace pre-drawn Archimedean spirals, using the dominant hand. The pre-drawn spiral was shown on the screen of the device. The spiral test was repeated three times per test occasion and they were instructed to complete it within 10 seconds. The device had a sampling rate of 10Hz and measured both position and time-stamps (in milliseconds) of the pen tip. Methods: Four independent raters (FB, DH, AJ and DN) used a web interface that animated the spiral drawings and allowed them to observe different kinematic features during the drawing process and to rate task performance. Initially, a number of kinematic features were assessed including ‘impairment’, ‘speed’, ‘irregularity’ and ‘hesitation’ followed by marking the predominant motor phenotype on a 3-category scale: tremor, bradykinesia and/or choreatic dyskinesia. There were only 2 test occasions for which all the four raters either classified them as tremor or could not identify the motor phenotype. Therefore, the two main motor phenotype categories were bradykinesia and dyskinesia. ‘Impairment’ was rated on a scale from 0 (no impairment) to 10 (extremely severe) whereas ‘speed’, ‘irregularity’ and ‘hesitation’ were rated on a scale from 0 (normal) to 4 (extremely severe). The proposed data-driven method consisted of the following steps. Initially, 28 spatiotemporal features were extracted from the time series signals before being presented to a Multilayer Perceptron (MLP) classifier. The features were based on different kinematic quantities of spirals including radius, angle, speed and velocity with the aim of measuring the severity of involuntary symptoms and discriminate between PD-specific (bradykinesia) and/or treatment-induced symptoms (dyskinesia). A Principal Component Analysis was applied on the features to reduce their dimensions where 4 relevant principal components (PCs) were retained and used as inputs to the MLP classifier. Finally, the MLP classifier mapped these components to the corresponding visually assessed motor phenotype scores for automating the process of scoring the bradykinesia and dyskinesia in PD patients whilst they draw spirals using the touch screen device. For motor phenotype (bradykinesia vs. dyskinesia) classification, the stratified 10-fold cross validation technique was employed. Results: There were good agreements between the four raters when rating the individual kinematic features with intra-class correlation coefficient (ICC) of 0.88 for ‘impairment’, 0.74 for ‘speed’, 0.70 for ‘irregularity’, and moderate agreements when rating ‘hesitation’ with an ICC of 0.49. When assessing the two main motor phenotype categories (bradykinesia or dyskinesia) in animated spirals the agreements between the four raters ranged from fair to moderate. There were good correlations between mean ratings of the four raters on individual kinematic features and computed scores. The MLP classifier classified the motor phenotype that is bradykinesia or dyskinesia with an accuracy of 85% in relation to visual classifications of the four movement disorder specialists. The test-retest reliability of the four PCs across the three spiral test trials was good with Cronbach’s Alpha coefficients of 0.80, 0.82, 0.54 and 0.49, respectively. These results indicate that the computed scores are stable and consistent over time. Significant differences were found between the two groups (patients and healthy elderly subjects) in all the PCs, except for the PC3. Conclusions: The proposed method automatically assessed the severity of unwanted symptoms and could reasonably well discriminate between PD-specific and/or treatment-induced motor symptoms, in relation to visual assessments of movement disorder specialists. The objective assessments could provide a time-effect summary score that could be useful for improving decision-making during symptom evaluation of individualized treatment when the goal is to maximize functional On time for patients while minimizing their Off episodes and troublesome dyskinesias.

Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Análise morfométrica digital na parede aórtica de suínos após implante de stents não revestidos versus stents revestidos com dacron

Os stents são dispositivos intravasculares implantados com o objetivo de dilatar ou fixar a placa de colesterol contra a parede arterial. O objetivo avaliar dois tipos de stents de aço inoxidável, um recoberto com poliéster (dacron) e outro não recoberto, implantados na aorta infra-renal de suínos jovens, foram avaliados por morfometria digital para medir o espessamento intimal. Foi realizado um estudo experimental randomizado, separados em dois grupos(stents não revestidos e revestidos com dacron) e duas fases (I e II). Oito stents recobertos com dacron e oito stents de aço inoxidável (30mm de extensão e 8 mm de diâmetro), não revestidos, foram implantados através de abordagem retroperitoneal na aorta infrarenal normal de 16 suínos normolipêmicos. Para a passagem do sistema de implante, foi necessário uma pequena arteriotomia na aorta distal (fase I). Após quatro semanas, a aorta com os stents foram removidas em monoblocos (fase II). Os valores de hematimetria e do lipidograma foram coletados nas duas fases e não apresentaram alterações que pudessem influenciar o estudo. Amostras de tecido dos sítios de fixação (proximal e distal) dos stents foram retiradas, confeccionadas lâminas, que foram coradas pelas técnicas de hematoxilina e eosina de Verhoeff e enviadas para análise morfométrica digital. A camada intima distal não apresentou diferença estatística significativa. A camada média proximal das porções proximal e da porção distal não apresentaram diferença entre os grupos. Os dois tipos de stents apresentaram 100% de perviedade, boa biocompatibilidade e boa incorporação a parede aórtica de suínos normolipêmicos. A camada íntima proximal do grupo de stents revestidos com dacron apresentou espessura maior do que os stents não revestidos, mas com significância estatísitica limítrofe.

Integração de dados geológicos, de sensoriamento remoto, espectrorradiométricos e geofísica aplicada à prospecção de depósitos filoneanos de fluorita hidrotermal no sudeste de Santa Catarina

Os processamentos de imagens orbitais efetuados através de técnicas de sensoriamento remoto geraram informações qualitativas de natureza textural (morfo-estruturas). Estas permitiram (1) o reconhecimento de áreas com diferentes padrões estruturais tendo diferentes potencialidades para a prospecção de fluorita, (2) a identificação de novos lineamentos estruturais potencialmente favoráveis à mineralização e (3) evidenciaram prolongamentos extensos para as principais estruturas mineralizadas, (4) às quais se associam um grande número de estruturas, antes desconhecidas, com grande potencial prospectivo. O aprimoramento de técnicas de classificação digital sobre produtos de razões de bandas e análise por componentes principais permitiu identificar a alteração hidrotermal associada às estruturas, incorporando novos critérios para a prospecção de fluorita. Buscando-se quantificar os dados de alteração hidrotermal, foi efetuada a análise espectrorradiométrica das rochas do distrito fluorítico. Integrando estas informações com dados TM LANDSAT 5, em nível de reflectância, obteve-se a classificação espectral das imagens orbitais, o que permitiu a identificação de estruturas menores com um detalhe nunca antes obtido. Os processamentos de dados aerogeofísicos forneceram resultados sobre estruturas (magnetometria) e corpos graníticos afetados por alteração hidrotermal (aerogamaespectrometria). Estes produtos foram integrados com dados TM LANDSAT 5 associando o atributo textural da imagem orbital ao comportamento radiométrico das rochas. Diagnosticou-se o lineamento Grão-Pará como o principal prospecto do distrito. E levantaram-se uma série de dados sobre a compartimentação tectônica da região, a zonação de fácies das rochas graníticas (rocha fonte do flúor) e as alterações hidrotermais associadas ao magmatismo granítico. Isto permitiu a compreensão da distribuição regional dos depósitos de fluorita, adicionando-se um novo critério à prospecção de fluorita, a relação espacial entre a mineralização e a rocha fonte de F. Esta última corresponde à fácies granítica da borda do Maciço Pedras Grandes.

Uso da análise discriminante regularizada (RDA) no reconhecimento de padrões em imagens digitais hiperespectral de sensoriamento remoto

Em cenas naturais, ocorrem com certa freqüência classes espectralmente muito similares, isto é, os vetores média são muito próximos. Em situações como esta, dados de baixa dimensionalidade (LandSat-TM, Spot) não permitem uma classificação acurada da cena. Por outro lado, sabe-se que dados em alta dimensionalidade [FUK 90] tornam possível a separação destas classes, desde que as matrizes covariância sejam suficientemente distintas. Neste caso, o problema de natureza prática que surge é o da estimação dos parâmetros que caracterizam a distribuição de cada classe. Na medida em que a dimensionalidade dos dados cresce, aumenta o número de parâmetros a serem estimados, especialmente na matriz covariância. Contudo, é sabido que, no mundo real, a quantidade de amostras de treinamento disponíveis, é freqüentemente muito limitada, ocasionando problemas na estimação dos parâmetros necessários ao classificador, degradando portanto a acurácia do processo de classificação, na medida em que a dimensionalidade dos dados aumenta. O Efeito de Hughes, como é chamado este fenômeno, já é bem conhecido no meio científico, e estudos vêm sendo realizados com o objetivo de mitigar este efeito. Entre as alternativas propostas com a finalidade de mitigar o Efeito de Hughes, encontram-se as técnicas de regularização da matriz covariância. Deste modo, técnicas de regularização para a estimação da matriz covariância das classes, tornam-se um tópico interessante de estudo, bem como o comportamento destas técnicas em ambientes de dados de imagens digitais de alta dimensionalidade em sensoriamento remoto, como por exemplo, os dados fornecidos pelo sensor AVIRIS. Neste estudo, é feita uma contextualização em sensoriamento remoto, descrito o sistema sensor AVIRIS, os princípios da análise discriminante linear (LDA), quadrática (QDA) e regularizada (RDA) são apresentados, bem como os experimentos práticos dos métodos, usando dados reais do sensor. Os resultados mostram que, com um número limitado de amostras de treinamento, as técnicas de regularização da matriz covariância foram eficientes em reduzir o Efeito de Hughes. Quanto à acurácia, em alguns casos o modelo quadrático continua sendo o melhor, apesar do Efeito de Hughes, e em outros casos o método de regularização é superior, além de suavizar este efeito. Esta dissertação está organizada da seguinte maneira: No primeiro capítulo é feita uma introdução aos temas: sensoriamento remoto (radiação eletromagnética, espectro eletromagnético, bandas espectrais, assinatura espectral), são também descritos os conceitos, funcionamento do sensor hiperespectral AVIRIS, e os conceitos básicos de reconhecimento de padrões e da abordagem estatística. No segundo capítulo, é feita uma revisão bibliográfica sobre os problemas associados à dimensionalidade dos dados, à descrição das técnicas paramétricas citadas anteriormente, aos métodos de QDA, LDA e RDA, e testes realizados com outros tipos de dados e seus resultados.O terceiro capítulo versa sobre a metodologia que será utilizada nos dados hiperespectrais disponíveis. O quarto capítulo apresenta os testes e experimentos da Análise Discriminante Regularizada (RDA) em imagens hiperespectrais obtidos pelo sensor AVIRIS. No quinto capítulo são apresentados as conclusões e análise final. A contribuição científica deste estudo, relaciona-se à utilização de métodos de regularização da matriz covariância, originalmente propostos por Friedman [FRI 89] para classificação de dados em alta dimensionalidade (dados sintéticos, dados de enologia), para o caso especifico de dados de sensoriamento remoto em alta dimensionalidade (imagens hiperespectrais). A conclusão principal desta dissertação é que o método RDA é útil no processo de classificação de imagens com dados em alta dimensionalidade e classes com características espectrais muito próximas.

Texturas de imagens utilizando conceitos de morfologia matemática

A textura é um atributo ainda pouco utilizado no reconhecimento automático de cenas naturais em sensoriamento remoto, já que ela advém da sensação visual causada pelas variações tonais existentes em uma determinada região da imagem, tornando difícil a sua quantificação. A morfologia matemática, através de operações como erosão, dilatação e abertura, permite decompor uma imagem em elementos fundamentais, as primitivas texturais. As primitivas texturais apresentam diversas dimensões, sendo possível associar um conjunto de primitivas com dimensões semelhantes, em uma determinada classe textural. O processo de classificação textural quantifica as primitivas texturais, extrai as distribuições das dimensões das mesmas e separa as diferentes distribuições por meio de um classificador de máxima-verossimilhança gaussiana. O resultado final é uma imagem temática na qual cada tema representa uma das texturas existentes na imagem original.

Morphologic and morphometric analysis of Hepatozoon spp. (Apicomplexa, Hepatozoidae) of snakes

Hepatozoon species are the most abundant hemoparasites of snakes. Its identification has been based mainly on the morphologic characterization of the gamonts in the peripheral blood of the vertebrate host and also of the cysts found in the internal organs of the vertebrate and invertebrate hosts. Using a computerized image analysis system, we studied five species of Hepatozoon from recently captured snakes in Botucatu, State of São Paulo, Brazil, to evaluate the importance of the morphology and morphometry of the gamonts for the characterization of Hepatozoon species and to analyze the morphologic changes induced in the erythrocytes by the parasite. The studied species were H. terzii of Boa constrictor amarali, Hepatozoon sp. of Crotalus durissusterrificus, H. philodryasi of Philodryas patagoniensis, and H. migonei and H. cyclagrasi of Hydrodynastes gigas. We observed three different groups, one of them including the species H. terzii, H. philodryasi and Hepatozoon sp. of C. durissus terrificus; and the other two consisting of H. migonei and H. cyclagrasi. Degree of alterations in the erythrocytes was variable and it may be useful for characterization of Hepatozoon species.

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Sistema auxiliar na análise de mamogramas digitais

This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

Sistema de teleoftalmologia para auxílio ao pré-diagnóstico de disfunções oculomotoras

The vision is one of the five senses of the human body and, in children is responsible for up to 80% of the perception of world around. Studies show that 50% of children with multiple disabilities have some visual impairment, and 4% of all children are diagnosed with strabismus. The strabismus is an eye disability associated with handling capacity of the eye, defined as any deviation from perfect ocular alignment. Besides of aesthetic aspect, the child may report blurred or double vision . Ophthalmological cases not diagnosed correctly are reasons for many school abandonments. The Ministry of Education of Brazil points to the visually impaired as a challenge to the educators of children, particularly in literacy process. The traditional eye examination for diagnosis of strabismus can be accomplished by inducing the eye movements through the doctor s instructions to the patient. This procedure can be played through the computer aided analysis of images captured on video. This paper presents a proposal for distributed system to assist health professionals in remote diagnosis of visual impairment associated with motor abilities of the eye, such as strabismus. It is hoped through this proposal to contribute improving the rates of school learning for children, allowing better diagnosis and, consequently, the student accompaniment