Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images.The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models.This new function is a combination of the gray-level information and first-order statistical features,called standard deviation parameters.In a comprehensive study,the developed algorithm and the efficiency of segmentation were first tested for synthetic images.Tests were also performed on breast and liver ultrasound images.The proposed method was compared with the watershed approach to show its efficiency.The performance of the segmentation was estimated using the area error rate.Using the standard deviation textural feature and a 5×5 kernel,our curve evolution was able to produce results close to the minimal area error rate(namely 8.88% for breast images and 10.82% for liver images).The image resolution was evaluated using the contrast-to-gradient method.The experiments showed promising segmentation results.     

Space, time and statistics

Spatiotemporal Models of Population and Community Dynamics by T. Czárán Chapman and Hall, Population and Community Biology Series, 1997. ￡59.00 hbk (xvii +284 pages) ISBN 0 412 57550 7.     

A nonlinear,second-order population model

Delay differential, difference, and partial differential equation models are being used more extensively to explain single-species population oscillations and limit cycle behavior. Ordinary differential equation (ODE) models have been largely ignored. This is because first-order ODE models are inherently monotonic. Certainly this is not usual population behavior in the real world. If it is assumed that the per capita growth rate of a population changes over time as a result of regulating factors impinging on it, then a more realistic biological model results. The model translates into a second-order nonlinear ODE. Such a model can exhibit oscillatory and limit cycle as well as monotonic solutions, i.e., behavior for which non-ODE models have been used to explain. Although first-order ODE models are gross simplifications of real phenomena, ODE models in general should not be disregarded as important analytical tools.     

Storytelling, statistics and hereditary thought: the narrative support of early statistics

This paper's main contention is that some basically methodological developments in science which are apparently distant and unrelated can be seen as part of a sequential story. Focusing on general inferential and epistemological matters, the paper links occurrences separated by both in time and space, by formal and representational issues rather than social or disciplinary links. It focuses on a few limited aspects of several cognitive practices in medical and biological contexts separated by geography, disciplines and decades, but connected by long term transdisciplinary representational and inferential structures and constraints. The paper intends to show a given set of knowledge claims based on organizing statistically empirical data can be seen to have been underpinned by a previous, more familiar, and probably more natural, narrative handling of similar evidence. To achieve that this paper moves from medicine in France in the late eighteenth and early nineteenth century to the second half of the nineteenth century in England among gentleman naturalists, following its subject: the shift from narrative depiction of hereditary transmission of physical peculiarities to posterior statistical articulations of the same phenomena. Some early defenders of heredity as an important (if not the most important) causal presence in the understanding of life adopted singular narratives, in the form of case stories from medical and natural history traditions, to flesh out a special kind of causality peculiar to heredity. This work tries to reconstruct historically the rationale that drove the use of such narratives. It then shows that when this rationale was methodologically challenged, its basic narrative and probabilistic underpinings were transferred to the statistical quantificational tools that took their place.     

Study of banana and coconut fibers Botanical composition, thermal degradation and textural observations

Four fibers from banana-trees (leaf, trunk) and coconut-tree (husk, fabric) were examined before their incorporation in cementitious matrices, in order to prepare insulating material for construction. Their botanical compositions have been determined following a method described previously. Thermal degradation of these fibers were studied between 200 and 700 degrees C under nitrogen gas flow. Temperature of pyrolysis was the experimental parameter investigated. The solid residues obtained were analyzed by classical elemental analysis, Fourier Transform Infra Red (FTIR) spectroscopy and were observed by Scanning Electron Microscopy (SEM). This study has shown (1) the relation between botanical, chemical composition with both localization of fibers in the tree and type of tree; (2) the rapid and preferential decomposition of banana fibers with increasing temperature of pyrolysis and (3) the rough samples are made of hollow fibers.     

Environmental proteomics, biodiversity statistics and food-web structure

Pioneering studies in environmental proteomics have revealed links between protein diversity and ecological function in simple ecological communities, such as microbial biofilms. In the near future, high-throughput proteomic methods will be applied to more complex ecological systems in which microbes and macrobes interact. Data structures in biodiversity and protein surveys have many similarities, so the statistical methods that ecologists use for analyzing biodiversity data should be adapted for use with quantitative surveys of protein diversity. However, increasing quantities of protein and bioinformatics data will not, by themselves, reveal the functional significance of proteins. Instead, ecologists should be measuring changes in the abundance of protein cohorts in response to replicated field manipulations, including nutrient enrichment and removal of top predators.     

Joint segmentation, calling, and normalization of multiple CGH profiles

The statistical analysis of array comparative genomic hybridization (CGH) data has now shifted to the joint assessment of copy number variations at the cohort level. Considering multiple profiles gives the opportunity to correct for systematic biases observed on single profiles, such as probe GC content or the so-called "wave effect." In this article, we extend the segmentation model developed in the univariate case to the joint analysis of multiple CGH profiles. Our contribution is multiple: we propose an integrated model to perform joint segmentation, normalization, and calling for multiple array CGH profiles. This model shows great flexibility, especially in the modeling of the wave effect that gives a likelihood framework to approaches proposed by others. We propose a new dynamic programming algorithm for break point positioning, as well as a model selection criterion based on a modified bayesian information criterion proposed in the univariate case. The performance of our method is assessed using simulated and real data sets. Our method is implemented in the R package cghseg.     

The Pasting and Gel Textural Properties of Corn Starch in Glucose,Fructose and Maltose Syrup

The pasting and gel textural properties of corn starch in syrup at different concentrations were investigated by Rapid Visco Analyzer (RVA) and Texture profile analysis (TPA) tests. The results showed that the pasting temperatures of corn starch greatly increased, especially at higher sugar concentration. Increasing concentration of syrup caused an increase in peak, trough and final viscosity of corn starch. Peak viscosity and the disintegration rate of starch increased in the following order: fructose syrup> maltose syrup> glucose syrup. Increasing syrup concentration to 13%, 25% and 50% resulted in a lower retrogradation rate than the control. When the maltose syrup concentration increased to 50%, the retrogradation rate decreased to 14.30% from 33.38%. The highest hardness was observed when the syrup concentration was 25%. There was a particular low hardness when the concentration of syrup was 50%. The springiness of starch gels in syrup was similar at different concentrations.     

Samples, sample selection, and statistics: living with uncertainty

The author explains how to determine how many animals in a population need to be sampled when monitoring for pathogens. He concludes that even with perfect testing programs, there is always a chance that an infected animal will be missed.     

Remote detection of invasive plants: a review of spectral,textural and phenological approaches

Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion.