CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

COMPUTER assisted technologies to enhance CONCURRENce in building DESIGN

Modern organisms are adapted to a wide variety of habitats and lifestyles. The processes of evolution have led to complex, interdependent, well-designed mechanisms of todays world and this research challenge is to transpose these innovative solutions to resolve problems in the context of architectural design practice, e.g., to relate design by nature with design by human. In a design by human environment, design synthesis can be performed with the use of rapid prototyping techniques that will enable to transform almost instantaneously any 2D design representation into a physical three-dimensional model, through a rapid prototyping printer machine. Rapid prototyping processes add layers of material one on top of another until a complete model is built and an analogy can be established with design by nature where the natural lay down of earth layers shapes the earth surface, a natural process occurring repeatedly over long periods of time. Concurrence in design will particularly benefit from rapid prototyping techniques, as the prime purpose of physical prototyping is to promptly assist iterative design, enabling design participants to work with a three-dimensional hardcopy and use it for the validation of their design-ideas. Concurrent design is a systematic approach aiming to facilitate the simultaneous involvment and commitment of all participants in the building design process, enabling both an effective reduction of time and costs at the design phase and a quality improvement of the design product. This paper presents the results of an exploratory survey investigating both how computer-aided design systems help designers to fully define the shape of their design-ideas and the extent of the application of rapid prototyping technologies coupled with Internet facilities by design practice. The findings suggest that design practitioners recognize that these technologies can greatly enhance concurrence in design, though acknowledging a lack of knowledge in relation to the issue of rapid prototyping.

CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

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.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Benefits from safer animal food products: preliminary results from a contingent valuation study of Italian households

The social cost of food scares has been the object of substantial applied research worldwide. In Italy, meat and dairy products are often the vectors of food-borne pathogens, and this is well known by the public. Most cases of food contamination and poisoning find their causes in the way food is handled after, rather than before purchase. However, a large fraction is still caused by mishandling at the industrial stage. With this in mind, we set out to estimate Italian households’ willingness to pay (WTP) for a reduction in the risk of meat and dairy food contamination using contingent valuation. The survey design incorporated features specifically conceived to overcome difficulties faced in previous survey research, especially with respect to individualized food expenditures and risk communication. In order to achieve this objective a CAPI (computer-assisted personal interview) survey was devised to tackle two major issues which emerged in previous contingent valuation studies. The first issue is connected to the way of communicating risk to consumers in order to allow them to make optimal choices and the second one to the results deriving from these studies. In fact, estimates from contingent valuation regarding food safety are given just for single products and so marketers may find it hard to extrapolate them to the aggregate. Our results show that in Italy there are segments of consumers who would benefit from higher standards of food safety for farm animal products.