Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Classification of polymers insulators hydrophobicity basead on digital image processing

Although the hydrophobicity is usually an arduous parameter to be determined in the field, it has been pointed out as a good option to monitor aging of polymeric outdoor insulators. Concerning this purpose, digital image processing of photos taken from wet insulators has been the main technique nowadays. However, important challenges on this technique still remain to be overcome, such as; images from non-controlled illumination conditions can interfere on analyses and no existence of standard surfaces with different levels of hydrophobicity. In this paper, the photo image samples were digitally filtered to reduce the illumination influence, and hydrophobic surface samples were prepared from wetting silicon surfaces with solution of water-alcohol. Furthermore norevious studies triying to quantify and relate these properties in a mathematical function were found, that could be used in the field by the electrical companies. Based on such considerations, high quality images of countless hydrophobic surfaces were obtained and three different image processing methodologies, the fractal dimension and two Haralick textures descriptors, entropy and homogeneity, associated with several digital filters, were compared. The entropy parameter Haralick's descriptors filtered with the White Top-Hat filter presented the best result to classify the hydrophobicity.

Chitosan-based biomaterials used in critical-size bone defects: radiographic study in rat's calvaria

OBJETIVO: Este estudo avaliou através de imagens radiográficas digitais, a ação de biomateriais de quitosana e de cloridrato de quitosana, com baixo e alto peso molecular, utilizados na correção de defeitos ósseos de tamanho crítico (DOTC)em calvária de ratos. MATERIAL E MÉTODO: DOTCs com 8 mm de diâmetro foram criados cirurgicamente na calvária de 50 ratos Holtzman. Em 10 animais o defeito foi preenchido foram preenchidos com coágulo sanguíneo (controle negativo). Os 40 animais restantes foram divididos de acordo com o biomaterial utilizado no preenchimento do defeito (quitosana de baixo peso e de alto peso molecular, e cloridrato de quitosana de baixo e de alto peso molecular), e foram avaliados em dois períodos experimentais (15 e 60 dias), totalizando 5 animais/biomaterial/período de avaliação. RESULTADO: A avaliação radiográfica foi feita utilizando duas radiografias digitais do crânio do animal: uma tomada logo após o defeito ósseo ser criado e a outra no momento do sacrifício. Nessas imagens, foi avaliada a densidade óssea radiográfica inicial e a final na área do defeito, que foram comparadas. As análises na densidade óssea radiográfica indicaram aumento da densidade óssea radiográfica dos DOTCs tratados para todos os biomateriais testados, em ambos os períodos. Resultados semelhantes foram encontrados no grupo controle. CONCLUSÃO: Conclui-se que os biomateriais de quitosana testados não foram capazes de aumentar a densidade radiográfica em DOTC realizados em calvária de ratos.

Quantification of cellular action fibers alignment from confocal microscopic images.

The cellular rheology has recently undergone a rapid development with particular attention to the cytoskeleton mechanical properties and its main components - actin filaments, intermediate filaments, microtubules and crosslinked proteins. However it is not clear what are the cellular structural changes that directly affect the cell mechanical properties. Thus, in this work, we aimed to quantify the structural rearrangement of these fibers that may emerge in changes in the cell mechanics. We created an image analysis platform to study smooth muscle cells from different arteries: aorta, mammary, renal, carotid and coronary and processed respectively 31, 29, 31, 30 and 35 cell image obtained by confocal microscopy. The platform was developed in Matlab (MathWorks) and it uses the Sobel operator to determine the actin fiber image orientation of the cell, labeled with phalloidin. The Sobel operator is used as a filter capable of calculating the pixel brightness gradient, point to point, in the image. The operator uses vertical and horizontal convolution kernels to calculate the magnitude and the angle of the pixel intensity gradient. The image analysis followed the sequence: (1) opens a given cells image set to be processed; (2) sets a fix threshold to eliminate noise, based on Otsu's method; (3) detect the fiber edges in the image using the Sobel operator; and (4) quantify the actin fiber orientation. Our first result is the probability distribution II(Δθ) to find a given fiber angle deviation (Δθ) from the main cell fiber orientation θ0. The II(Δθ) follows an exponential decay II(Δθ) = Aexp(-αΔθ) regarding to its θ0. We defined and determined a misalignment index α of the fibers of each artery kind: coronary αCo = (1.72 ‘+ or =’ 0.36)rad POT -1; renal αRe = (1.43 + or - 0.64)rad POT -1; aorta αAo = (1.42 + or - 0.43)rad POT -1; mammary αMa = (1.12 + or - 0.50)rad POT -1; and carotid αCa = (1.01 + or - 0.39)rad POT -1. The α of coronary and carotid are statistically different (p < 0.05) among all analyzed cells. We discussed our results correlating the misalignment index data with the experimental cell mechanical properties obtained by using Optical Magnetic Twisting Cytometry with the same group of cells.

Comparison of flow cytometry and indirect immunofluorescence assay in the diagnosis and cure criterion after therapy of American tegumentary leishmaniasis by anti-live Leishmania (Viannia) braziliensis immunoglobulin G

The aim of this study was to compare the techniques of indirect immunofluorescence assay (IFA) and flow cytometry to clinical and laboratorial evaluation of patients before and after clinical cure and to evaluate the applicability of flow cytometry in post-therapeutic monitoring of patients with American tegumentary leishmaniasis (ATL). Sera from 14 patients before treatment (BT), 13 patients 1 year after treatment (AT), 10 patients 2 and 5 years AT were evaluated. The results from flow cytometry were expressed as levels of IgG reactivity, based on the percentage of positive fluorescent parasites (PPFP). The 1:256 sample dilution allowed us to differentiate individuals BT and AT. Comparative analysis of IFA and flow cytometry by ROC (receiver operating characteristic curve) showed, respectively, AUC (area under curve) = 0.8 (95% CI = 0.64–0.89) and AUC = 0.90 (95% CI = 0.75–0.95), demonstrating that the flow cytometry had equivalent accuracy. Our data demonstrated that 20% was the best cut-off point identified by the ROC curve for the flow cytometry assay. This test showed a sensitivity of 86% and specificity of 77% while the IFA had a sensitivity of 78% and specificity of 85%. The after-treatment screening, through comparative analysis of the technique performance indexes, 1, 2 and 5 years AT, showed an equal performance of the flow cytometry compared with the IFA. However, flow cytometry shows to be a better diagnostic alternative when applied to the study of ATL in the cure criterion. The information obtained in this work opens perspectives to monitor cure after treatment of ATL.

Quantitative analysis of photodynamic therapy effects in rat mammary tumor vascular density using image-pro plus software

Photodynamic therapy (PDT) is a treatment modality that has advanced rapidly in recent years. It causes tissue and vascular damage with the interaction of a photosensitizing agent (PS), light of a proper wavelength, and molecular oxygen. Evaluation of vessel damage usually relies on histopathology evaluation. Results are often qualitative or at best semi-quantitative based on a subjective system. The aim of this study was to evaluate, using CD31 immunohistochem- istry and image analysis software, the vascular damage after PDT in a well-established rodent model of chemically induced mammary tumor. Fourteen Sprague-Dawley rats received a single dose of 7,12-dimethylbenz(a)anthraxcene (80 mg/kg by gavage), treatment efficacy was evaluated by comparing the vascular density of tumors after treatment with Photogem® as a PS, intraperitoneally, followed by interstitial fiber optic lighting, from a diode laser, at 200 mW/cm and light dose of 100 J/cm directed against his tumor (7 animals), with a control group (6 animals, no PDT). The animals were euthanized 30 hours after the lighting and mammary tumors were removed and samples from each lesion were formalin-fixed. Immunostained blood vessels were quantified by Image Pro-Plus version 7.0. The control group had an average of 3368.6 ± 4027.1 pixels per picture and the treated group had an average of 779 ± 1242.6 pixels per area (P < 0.01), indicating that PDT caused a significant decrease in vascular density of mammary tumors. The combined immu- nohistochemistry using CD31, with selection of representative areas by a trained pathology, followed by quantification of staining using Image Pro-Plus version 7.0 system was a practical and robust methodology for vessel damage evalua- tion, which probably could be used to assess other antiangiogenic treatments.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.

Supervised classification of basaltic aggregate particles based on texture properties

The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.

CART application to image primitives tracking

[EN] [EN] In this paper we present a new method for image primitives tracking based on a CART (Classification and Regression Tree). Primitives tracking procedure uses lines and circles as primitives. We have applied the proposed method to sport event scenarios, specifically, soccer matches. We estimate CART parameters using a learning procedure based on RGB image channels. In order to illustrate its performance, it has been applied to real HD (High Definition) video sequences and some numerical experiments are shown. The quality of the primitives tracking with the decision tree is validated by the percentage error rates obtained and the comparison with other techniques as a morphological method. We also present applications of the proposed method to camera calibration and graphic object insertion in real video sequences.

3-D geometry reconstruction using a color image stereo pair and partial differential equations

[EN] In this work, we present a new model for a dense disparity estimation and the 3-D geometry reconstruction using a color image stereo pair. First, we present a brief introduction to the 3-D Geometry of a camera system. Next, we propose a new model for the disparity estimation based on an energy functional. We look for the local minima of the energy using the associate Euler-Langrage partial differential equations. This model is a generalization to color image of the model developed in, with some changes in the strategy to avoid the irrelevant local minima. We present some numerical experiences of 3-D reconstruction, using this method some real stereo pairs.

Dense disparity map estimation respecting image discontinuities

[EN] We present an energy based approach to estimate a dense disparity map from a set of two weakly calibrated stereoscopic images while preserving its discontinuities resulting from image boundaries. We first derive a simplified expression for the disparity that allows us to estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the Nagel-Enkelmann operator. We investigate the associated Euler-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method The resulting parabolic problem has a unique solution. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. Experimental results on both synthetic and real images arere presented to illustrate the capabilities of this PDE and scale-space based method.

Viola-Jones based detectors: How much affects the training set?

[EN]This paper presents a study on the facial feature detection performance achieved using the Viola-Jones framework. A set of classi- ers using two di erent focuses to gather the training samples is created and tested on four di erent datasets covering a wide range of possibili- ties. The results achieved should serve researchers to choose the classi er that better ts their demands.

Computer vision based eyewear selector

[EN]This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

Reconstruction from image correspondences

A single picture provides a largely incomplete representation of the scene one is looking at. Usually it reproduces only a limited spatial portion of the scene according to the standpoint and the viewing angle, besides it contains only instantaneous information. Thus very little can be understood on the geometrical structure of the scene, the position and orientation of the observer with respect to it remaining also hard to guess. When multiple views, taken from different positions in space and time, observe the same scene, then a much deeper knowledge is potentially achievable. Understanding inter-views relations enables construction of a collective representation by fusing the information contained in every single image. Visual reconstruction methods confront with the formidable, and still unanswered, challenge of delivering a comprehensive representation of structure, motion and appearance of a scene from visual information. Multi-view visual reconstruction deals with the inference of relations among multiple views and the exploitation of revealed connections to attain the best possible representation. This thesis investigates novel methods and applications in the field of visual reconstruction from multiple views. Three main threads of research have been pursued: dense geometric reconstruction, camera pose reconstruction, sparse geometric reconstruction of deformable surfaces. Dense geometric reconstruction aims at delivering the appearance of a scene at every single point. The construction of a large panoramic image from a set of traditional pictures has been extensively studied in the context of image mosaicing techniques. An original algorithm for sequential registration suitable for real-time applications has been conceived. The integration of the algorithm into a visual surveillance system has lead to robust and efficient motion detection with Pan-Tilt-Zoom cameras. Moreover, an evaluation methodology for quantitatively assessing and comparing image mosaicing algorithms has been devised and made available to the community. Camera pose reconstruction deals with the recovery of the camera trajectory across an image sequence. A novel mosaic-based pose reconstruction algorithm has been conceived that exploit image-mosaics and traditional pose estimation algorithms to deliver more accurate estimates. An innovative markerless vision-based human-machine interface has also been proposed, so as to allow a user to interact with a gaming applications by moving a hand held consumer grade camera in unstructured environments. Finally, sparse geometric reconstruction refers to the computation of the coarse geometry of an object at few preset points. In this thesis, an innovative shape reconstruction algorithm for deformable objects has been designed. A cooperation with the Solar Impulse project allowed to deploy the algorithm in a very challenging real-world scenario, i.e. the accurate measurements of airplane wings deformations.