Search results for "Segmentation"
showing 10 items of 674 documents
Alternative Financing of SMEs in the Baltic States: Myth or Reality?
2014
Abstract Alternative financing for small business can help companies whose owners can’t get traditional financing resources as small business loans, overdrafts and personal credit cards. New companies typically seek alternative financing when their companies have not been in business long enough to establish credit profiles with financial institutions. Access to alternative financing together with traditional financial resources represents one of the most significant challenges for new SMEs not only in the Baltic States but all over the world. Ensurance of new SMEs creation, existence and growth asks understanding of SMEs’ financing needs and alternative funding is one of them. Alternative …
Managing Risk in Financial Market in Shipping Industry
2011
Based on the knowledge from shipping we would like to study one option strategy for investments on shipping stocks. Since the term paper is relatively short we have chosen one market segment, namely the offshore market, and one shipping company, namely Farstad Shipping. We will use the theory of freight rates from Martin Stopford`s book, Maritime economics, and apply it to the real world. The reason for this is that the freight rates are the income for the shipping companies. Furthermore we will use the financial information from Farstad Shipping to see what the value of Farstad Shipping stocks should be in the future. This we will do based on the freight rates in the offshore market. The m…
A genetic algorithm for image segmentation
2002
The paper describes a new algorithm for image segmentation. It is based on a genetic approach that allow us to consider the segmentation problem as a global optimization problem (GOP). For this purpose, a fitness function, based on the similarity between images, has been defined. The similarity is a function of both the intensity and the spatial position of pixels. Preliminary results, obtained using real images, show a good performance of the segmentation algorithm.
Flexibility, Segmentation and Use of Labour in Finnish Retail Trade
1990
The purpose of the study is to analyse the effects of management strategies on the segmentation of five department store labour markets in Finland. (1) The competitive strategies of firms are expected to have different con sequences for the use of labour (2) Within firms, the differentiated use of labour is examined in relation to the use of different flexibility strategies In department stores the most important strategy for obtaining (numerical) flexibility has been the fast growth of part-time work.
Deep Learning Networks for Automatic Retroperitoneal Sarcoma Segmentation in Computerized Tomography
2022
The volume estimation of retroperitoneal sarcoma (RPS) is often difficult due to its huge dimensions and irregular shape; thus, it often requires manual segmentation, which is time-consuming and operator-dependent. This study aimed to evaluate two fully automated deep learning networks (ENet and ERFNet) for RPS segmentation. This retrospective study included 20 patients with RPS who received an abdominal computed tomography (CT) examination. Forty-nine CT examinations, with a total of 72 lesions, were included. Manual segmentation was performed by two radiologists in consensus, and automatic segmentation was performed using ENet and ERFNet. Significant differences between manual and automat…
On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI
2021
Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (…
Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement
2022
Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, th…
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
2023
For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of self-attention-based networks to the field of computer vision through ViTs seems to have changed the trend of using standard convolutions. Throughout this work, we apply different architectures such as U-Net, ViTs and ConvMixer, to compare their performance on a medical semantic segmentation problem. All the models have been trained from scratch on the DRIVE dataset and evaluated on their private counterparts to assess which of the models performed better in the segmentatio…
SPtsAnalysis: a high-throughput super-resolution single particle trajectory analysis to reconstruct organelle dynamics and membrane re-organization
2021
AbstractSuper-resolution imaging can generate thousands of single-particle trajectories. These data can potentially reconstruct subcellular organization and dynamics, as well as measure disease-linked changes. However, computational methods that can derive quantitative information from such massive datasets are currently lacking. Here we present data analysis and algorithms that are broadly applicable to reveal local binding and trafficking interactions and organization of dynamic sub-cellular sites. We applied this analysis to the endoplasmic reticulum and neuronal membrane. The method is based on spatio-temporal time window segmentation that explores data at multiple levels and detects th…
Evaluation of capabilities of fuzzy logic classification of different kind of data
2008
In this paper, in order to evaluate the capability of several data, acquired by different sensors, some object-oriented classification tests have been carried out. In particular, the results obtained with two RGB ortophotos, acquired with traditional methodology, have been compared with the ones obtained with two QuickBird images and with the ones obtained by ADS40 pushbroom sensor. The object classification is based on two next steps: The classification to objects is based on two next steps: the decomposition of the whole image in dimension objects bigger than the pixel, procedure called segmentation, and the next classification with Fuzzy logic. This approach provides more reliable result…