1,721,010 research outputs found
Advances in spatial economic data analysis: methods and applications
Full text linkSpatial economic studies traditionally exploit areal data at the regional or sub-regional level. More recently, scholars have started to exploit spatial data of a different nature and, at the same time, extend the fields of application in economics. Specifically, this special issue contributes to the spatial economic literature by providing empirical evidence on a wide range of phenomena (socio-economic deprivation, land price volatility, electoral competition, real estate market, firm survival and tourism economics) and exploiting data at the municipality, firm, house and even individual level. At the same time, it tackles some of the methodological issues faced by the above-mentioned analyses
On the use of auxiliary variables in agricultural survey design
No full textIn this chapter we have studied the role of auxiliary information in agricultural sample survey design. First we introduced the distinction between ex ante (before sample selec- tion) and ex post use of the auxiliary information. In accordance with this, we reviewed three families of operational strategies, summarizing the main results in the literature and considering various topics of particular interest in the current scientific debate. The two ex ante strategies considered in this chapter are the construction of efficient and/or optimal stratifications and of efficient sample designs
A Semi-Unsupervised Segmentation Methodology Based on Texture Recognition for Radiomics: A Preliminary Study on Brain Tumours
Full text linkBecause of the intrinsic anatomic complexity of the brain structures, brain tumors have a high mortality and disability rate, and an early diagnosis is mandatory to contain damages. The commonly used biopsy is the diagnostic gold standard method, but it is invasive and, due to intratumoral heterogeneity, biopsies may lead to an incorrect result. Moreover, some tumors cannot be resectable if located in critical eloquent areas. On the other hand, medical imaging procedures can evaluate the entire tumor in a non-invasive and reproducible way. Radiomics is an emerging diagnosis technique based on quantitative medical image analyses, which makes use of data provided by non-invasive diagnosis techniques such as X-ray, computer-tomography (CT), magnetic resonance (MR), and proton emission tomography (PET). Radiomics techniques require the comprehensive analysis of huge numbers of medical images to extract a large and useful number of phenotypic features (usually called radiomics biomarkers). The goal is to explore and obtain the associations between features of tumors, diagnosis and patients’ prognoses to choose the best treatments and maximize the patient’s survival rate. Current radiomics techniques are not standardized in term of segmentation, feature extraction, and selection, moreover, the decision on suitable therapies still requires the supervision of an expert doctor. In this paper, we propose a semi-automatic methodology aimed to help the identification and segmentation of malignant tissues by using the combination of binary texture recognition, growing area algorithm, and machine learning techniques. In particular, the proposed method not only helps to better identify pathologic tissues but also permits to analyze in a fast way the huge amount of data, in Dicom format, provided by non-invasive diagnostic techniques. A preliminary experimental assessment has been conducted, considering a real MRI database of brain tumors. The method has been compared with the segmentation software’s tools “slicer 3D”. The obtained results are quite promising and demonstrate the potentialities of the proposed semi-unsupervised segmentation methodology
- …
