907 resultados para Evaluation methods for image segmentation
Resumo:
Mode of access: Internet.
Resumo:
Mode of access: Internet.
Resumo:
"Report no. ITRC FR 95/96-1".
Resumo:
Includes bibliographical references.
Resumo:
"Report no. FHWA-IL-UI-278"--Technical report documentation page.
Resumo:
"April 1980."
Resumo:
Caption title.
Resumo:
"Annotated bibliography": p. 33-39.
Resumo:
Recently, a 3D phantom that can provide a comprehensive and accurate measurement of the geometric distortion in MRI has been developed. Using this phantom, a full assessment of the geometric distortion in a number of clinical MRI systems (GE and Siemens) has been carried out and detailed results are presented in this paper. As expected, the main source of geometric distortion in modern superconducting MRI systems arises from the gradient field nonlinearity. Significantly large distortions with maximum absolute geometric errors ranged between 10 and 25 mm within a volume of 240 x 240 x 240 mm(3) were observed when imaging with the new generation of gradient systems that employs shorter coils. By comparison, the geometric distortion was much less in the older-generation gradient systems. With the vendor's correction method, the geometric distortion measured was significantly reduced but only within the plane in which these 2D correction methods were applied. Distortion along the axis normal to the plane was, as expected, virtually unchanged. Two-dimensional correction methods are a convenient approach and in principle they are the only methods that can be applied to correct geometric distortion in a single slice or in multiple noncontiguous slices. However, these methods only provide an incomplete solution to the problem and their value can be significantly reduced if the distortion along the normal of the correction plane is not small. (C) 2004 Elsevier Inc. All rights reserved.
Resumo:
This economic evaluation was part of the Australian National Evaluation of Pharmacotherapies for Opioid Dependence (NEPOD) project. Data from four trials of heroin detoxification methods, involving 365 participants, were pooled to enable a comprehensive comparison of the cost-effectiveness of five inpatient and outpatient detoxification methods. This study took the perspective of the treatment provider in assessing resource use and costs. Two short-term outcome measures were used-achievement of an initial 7-day period of abstinence, and entry into ongoing post-detoxification treatment. The mean costs of the various detoxification methods ranged widely, from AUD $491 (buprenorphine-based outpatient); to AUD $605 for conventional outpatient; AUD $1404 for conventional inpatient; AUD $1990 for rapid detoxification under sedation; and to AUD $2689 for anaesthesia per episode. An incremental cost-effectiveness analysis was carried out using conventional outpatient detoxification as the base comparator. The buprenorphine-based outpatient detoxification method was found to be the most cost-effective method overall, and rapid opioid detoxification under sedation was the most costeffective inpatient method.
Resumo:
The texture segmentation techniques are diversified by the existence of several approaches. In this paper, we propose fuzzy features for the segmentation of texture image. For this purpose, a membership function is constructed to represent the effect of the neighboring pixels on the current pixel in a window. Using these membership function values, we find a feature by weighted average method for the current pixel. This is repeated for all pixels in the window treating each time one pixel as the current pixel. Using these fuzzy based features, we derive three descriptors such as maximum, entropy, and energy for each window. To segment the texture image, the modified mountain clustering that is unsupervised and fuzzy c-means clustering have been used. The performance of the proposed features is compared with that of fractal features.
Resumo:
Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.