Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Classification-based probabilistic modeling of texture transition for fast line search tracking and delineation

We introduce a classification-based approach to finding occluding texture boundaries. The classifier is composed of a set of weak learners, which operate on image intensity discriminative features that are defined on small patches and are fast to compute. A database that is designed to simulate digitized occluding contours of textured objects in natural images is used to train the weak learners. The trained classifier score is then used to obtain a probabilistic model for the presence of texture transitions, which can readily be used for line search texture boundary detection in the direction normal to an initial boundary estimate. This method is fast and therefore suitable for real-time and interactive applications. It works as a robust estimator, which requires a ribbon-like search region and can handle complex texture structures without requiring a large number of observations. We demonstrate results both in the context of interactive 2D delineation and of fast 3D tracking and compare its performance with other existing methods for line search boundary detection.

Volume-based size distribution of accumulation and coarse particles (PM0.1–10) from cooking fume during oil heating

Particulate matter generated during the cooking process has been identified as one of the major problems of indoor air quality and indoor environmental health. Reliable assessment of exposure to cooking-generated particles requires accurate information of emission characteristics especially the size distribution. This study characterizes the volume/mass-based size distribution of the fume particles at the oil-heating stage for the typical Chinese-style cooking in a laboratory kitchen. A laser-diffraction size analyzer is applied to measure the volume frequency of fume particles ranged from 0.1 to 10 μm, which contribute to most mass proportion in PM2.5 and PM10. Measurements show that particle emissions have little dependence on the types of vegetable oil used but have a close relationship with the heating temperature. It is found that volume frequency of fume particles in the range of 1.0–4.0 μm accounts for nearly 100% of PM0.1–10 with the mode diameter 2.7 μm, median diameter 2.6 μm, Sauter mean diameter 3.0 μm, DeBroukere mean diameter 3.2 μm, and distribution span 0.48. Such information on emission characteristics obtained in this study can be possibly used to improve the assessment of indoor air quality due to PM0.1–10 in the kitchen and residential flat.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Grey matter volume in the cerebellum is related to processing of grammatical rules in a second language: a structural Voxel-Based Morphometry study

The experience of learning and using a second language (L2) has been shown to affect the grey matter (GM) structure of the brain. Importantly, GM density in several cortical and subcortical areas has been shown to be related to performance in L2 tasks. Here we show that bilingualism can lead to increased GM volume in the cerebellum, a structure that has been related to the processing of grammatical rules. Additionally, the cerebellar GM volume of highly proficient L2 speakers is correlated to their performance in a task tapping on grammatical processing in a L2, demonstrating the importance of the cerebellum for the establishment and use of grammatical rules in a L2.

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.

Visualization of spirography-based objective measures in Parkinson's disease

Objective: To investigate whether advanced visualizations of spirography-based objective measures are useful in differentiating drug-related motor dysfunctions between Off and dyskinesia in Parkinson’s disease (PD). Background: During the course of a 3 year longitudinal clinical study, in total 65 patients (43 males and 22 females with mean age of 65) with advanced PD and 10 healthy elderly (HE) subjects (5 males and 5 females with mean age of 61) were assessed. Both patients and HE subjects performed repeated and time-stamped assessments of their objective health indicators using a test battery implemented on a telemetry touch screen handheld computer, in their home environment settings. Among other tasks, the subjects were asked to trace a pre-drawn Archimedes spiral using the dominant hand and repeat the test three times per test occasion. Methods: A web-based framework was developed to enable a visual exploration of relevant spirography-based kinematic features by clinicians so they can in turn evaluate the motor states of the patients i.e. Off and dyskinesia. The system uses different visualization techniques such as time series plots, animation, and interaction and organizes them into different views to aid clinicians in measuring spatial and time-dependent irregularities that could be associated with the motor states. Along with the animation view, the system displays two time series plots for representing drawing speed (blue line) and displacement from ideal trajectory (orange line). The views are coordinated and linked i.e. user interactions in one of the views will be reflected in other views. For instance, when the user points in one of the pixels in the spiral view, the circle size of the underlying pixel increases and a vertical line appears in the time series views to depict the corresponding position. In addition, in order to enable clinicians to observe erratic movements more clearly and thus improve the detection of irregularities, the system displays a color-map which gives an idea of the longevity of the spirography task. Figure 2 shows single randomly selected spirals drawn by a: A) patient who experienced dyskinesias, B) HE subject, and C) patient in Off state. Results: According to a domain expert (DN), the spirals drawn in the Off and dyskinesia motor states are characterized by different spatial and time features. For instance, the spiral shown in Fig. 2A was drawn by a patient who showed symptoms of dyskinesia; the drawing speed was relatively high (cf. blue-colored time series plot and the short timestamp scale in the x axis) and the spatial displacement was high (cf. orange-colored time series plot) associated with smooth deviations as a result of uncontrollable movements. The patient also exhibited low amount of hesitation which could be reflected both in the animation of the spiral as well as time series plots. In contrast, the patient who was in the Off state exhibited different kinematic features, as shown in Fig. 2C. In the case of spirals drawn by a HE subject, there was a great precision during the drawing process as well as unchanging levels of time-dependent features over the test trial, as seen in Fig. 2B. Conclusions: Visualizing spirography-based objective measures enables identification of trends and patterns of drug-related motor dysfunctions at the patient’s individual level. Dynamic access of visualized motor tests may be useful during the evaluation of drug-related complications such as under- and over-medications, providing decision support to clinicians during evaluation of treatment effects as well as improve the quality of life of patients and their caregivers. In future, we plan to evaluate the proposed approach by assessing within- and between-clinician variability in ratings in order to determine its actual usefulness and then use these ratings as target outcomes in supervised machine learning, similarly as it was previously done in the study performed by Memedi et al. (2013).

Comparação entre métodos para determinação de carbono orgânico em amostras de solo mensuradas por volume ou massa

O uso da massa (g) ou do volume (cm³) como medida da quantidade de solo para determinação do teor de C orgânico do solo (COS), bem como a utilização de diferentes métodos de determinações analíticas empregados para essa mesma finalidade, pode alterar diretamente a interpretação dos resultados. Este trabalho teve como objetivo comparar os métodos colorimétricos e titulométricos de determinação de matéria orgânica em três solos com diferentes texturas, tomando-se as amostras por massa (pesagem) e por volume (cachimbagem). Houve variação no teor de COS entre os métodos estudados para um mesmo solo. O uso da massa e do volume alterou diretamente a interpretação dos teores de matéria orgânica no solo. Há variações nos métodos de determinação de COS entre solos de diferentes texturas.

The influence of area/volume ratio on microstructure and non-Ohmic properties of SnO2-based varistor ceramic blocks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Numeric simulation of pollutant dispersion by a control-volume based on finite element method

The dispersion of pollutants in the environment is an issue of great interest as it directly affects air quality, mainly in large cities. Experimental and numerical tools have been used to predict the behavior of pollutant species dispersion in the atmosphere. A software has been developed based on the control-volume based on the finite element method in order to obtain two-dimensional simulations of Navier-Stokes equations and heat or mass transportation in regions with obstacles, varying position of the pollutant source. Numeric results of some applications were obtained and, whenever possible, compared with literature results showing satisfactory accordance. Copyright (C) 2010 John Wiley & Sons, Ltd.

NUMERICAL SIMULATION of CONVECTION-DIFFUSION PROBLEMS BY THE CONTROL-VOLUME-BASED FINITE-ELEMENT METHOD

This work presents a numerical study of the tri-dimensional convection-diffusion equation by the control-volume-based on finite-element method using quadratic hexahedral elements. Considering that the equation governing this problem in its main variable may represent several properties, including temperature, turbulent kinetic energy, viscous dissipation rate of the turbulent kinetic energy, specific dissipation rate of the turbulent kinetic energy, or even the concentration of a contaminant in a given medium, among others, the wide applicability of this problem is thus evidenced. Three cases of temperature distributions will be studied specifically in this work, in addition to one case of pollutant dispersion upon analysis of the concentration of a contaminant in a fixed flow point. Some comparisons will be carried out against works found in the open literature, while others will be done according to each phenomenon characteristics.

Development and applications of three-dimensional gamma ray tomography system using ray casting volume rendering techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A sensor for monitoring the volume of nutrient in a solid substrate-based growth media by using electrochemical admittance spectroscopy

A sensor was fashioned to monitor the volume of nutrient in a solid substrate-based growth media by using electrochemical admittance spectroscopy. Several experimental parameters were investigated (i.e. The use of two- or three-electrode cells, the superficial area of the electrode, the amount of nutrient solution added to the growth media, and the influence of varying the dc and ac potential) to assess how these variables affect the admittance of the system. A linear correlation was observed between the maximum of the imaginary admittance and the volume of nutrient present. The response factor was 2.8 x 10(-5) S cm(-2) ml(-1) and the limit of detection (LOD) was 0.54 ml. The humidity of the growth media does not change the response of the nutrient toward the monitoring measurements. These results demonstrate that the volume of nutrient in this solid substrate-based growth media can be assessed using a ceramic sensor to measure the imaginary admittance. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Estimating the aboveground biomass and wood volume of savanna woodlands in Brazil's Pantanal wetlands based on allometric correlations

The quantity and distribution of vegetal biomass are important aspects to consider in ecosystem studies. However, little information is available about Brazil's Pantanal woodland savannas. This work involved the development of regression equations of the aerial biomass and wood volume of native tree species in a region of woodland savanna on Rio Negro farm in the Pantanal of Nhecolandia, Brazil. Samples were taken from 10 trees of each of five species: Protium heptaphyllum (Aub1.) Marchand, Magonia pubescens A. St.-Hil., Diptychandra aurantiaca Tul., Terminalia argentea Mart. and Zucc. and Licania minutiflora (Sagot) Fritsch and from a miscellaneous group of I I different species. Linear and nonlinear regression analyses were developed relating the diameter at breast height to the dry weight of wood, branches and leaves, wood volume and total aerial biomass. All the regressions showed a significance of P < 0.05 and an R-2 close to or above 0.8. The biomass curve predicted by linear regression analysis of the studied species was similar to the nonlinear regression, with the exception of L. minutiflora and the miscellaneous group. The breast height diameter proved a good choice for estimating biomass and wood volume. The estimated wood volume and biomass of the Pantanal woodland savanna is crucial information for understanding the carbon cycle and for ensuring the region's conservation and sustainable use. (c) 2006 Elsevier B.V. All rights reserved.