Semi-automatic Extraction Approach Research Articles

Simultaneous assessment of regional distributions of atrophy across the neuraxis in MS patients

BackgroundThe ability to assess brain and cord atrophy simultaneously would improve the efficiency of MRI to track disease evolution. ObjectiveTo test a promising tool to simultaneously map the regional distribution of atrophy in multiple sclerosis (MS) patients across the brain and cord. MethodsVoxel-based morphometry combined with a statistical parametric mapping probabilistic brain-spinal cord (SPM-BSC) template was applied to standard T1-weighted magnetic resonance imaging (MRI) scans covering the brain and cervical cord from 37 MS patients and 20 healthy controls (HC). We also measured the cord area at C2-C3 with a semi-automatic segmentation method using (i) the same T1-weighted acquisitions used for the new voxel-based analysis and (ii) dedicated spinal cord phase sensitive inversion recovery (PSIR) acquisitions. Cervical cord findings derived from the three approaches were compared to each other and the goodness to fit to clinical scores was assessed by regression analyses. ResultsThe SPM-BSC approach revealed a severity-dependent pattern of atrophy across the cervical cord and thalamus in MS patients when compared to HCs. The magnitude of cord atrophy was confirmed by the semi-automatic extraction approach at C2-C3 using both standard brain T1-weighted and advanced cord dedicated acquisitions. Associations between atrophy of cord and thalamus with disability and cognition were demonstrated. ConclusionAtrophy in the brain and cervical cord of MS patients can be identified simultaneously and rapidly at the voxel-level. The SPM-BSC approach yields similar results as available standard processing tools with the added advantage of performing the analysis simultaneously and faster.

Image analysis applied to the planning of a canvas painting restoration intervention

This work proposes a methodology of digital analysis of the state of conservation of a canvas painting to solve difficulties related to the pictorial reintegration of paintings that present an excessive number of different lacunae in terms of size and extent. The case study is related to the small-size oil on canvas painting executed by an unknown artist, where the lacunae were challenging to analyze and localize graphically. Therefore, it required a careful evaluation of the approach to be used during the pictorial reintegration intervention. Using an image analysis method, based on the semi-automatic extraction approach, the state of conservation’s graphic relief outlining different virtual operating proposals was obtained.

Landslide Susceptibility Mapping Using Landslide Numerical Risk Factor Model and Landslide Inventory Prepared Through OBIA in Chenab Valley, Jammu and Kashmir (India)

A landslide is the movement of rock, debris or earth down along the slope under the gravity. It may cause to loss of people’s life and their private and public properties. Landslide is a common hazard in steep slope areas, especially during the rainy season. A study of landslide helps urban planners, engineers and local communities to reduce losses caused by existing and future landslides by means of prevention, mitigation and avoidance. Therefore, the prime aim of this paper is to produce acceptable landslide hazard map for Chenab valley, Jammu and Kashmir. Semiautomatic extraction of the landslide is a suitable method that has been used in this paper to extract the location and extent of the landslides. IRS LISS-IV and CartoDEM have been used for object-based image analysis to extract and prepare a landslide inventory map. About 84 landslide potential sites have been identified by the semiautomatic extraction approach. Landslide numerical risk factor model is derived by using thirteen thematic layers with landslide inventory to prepare the landslide hazard map. The result showed that 21% area of the Chenab valley is falling under the very high hazard zone category of the landslide. The final result of the investigation will definitely be useful in the decision-making procedure at the time of emergency and will be used to prepare a preparedness plan for high-risk areas of Chenab valley. The ROC curve method is used for accuracy assessment that signifies the acceptable result for landslide susceptibility zonation of Chenab valley with 0.956 AUC value.

Comparison of type-based and alias-based component recognition for embedded systems software

Component-based software engineering has found broad acceptance within the embedded systems community over the last years. However, to fully exploit its potential in terms of reusability and cost-efficiency, existing code-bases have to be refactored in a component-based way. To support refactorization, static analysis techniques can be used to identify components within coarse-grained layered or even monolithic legacy software for embedded systems. We present an approach for semi-automatic extraction of components from automotive software and compare two different versions, one type-based component-recognition analysis of linear complexity with a more precise version based on a points-to analysis of almost linear algorithmic complexity. Both analyses are applied to an industrial implementation of an automotive communication stack. Each analysis is evaluated with two sets of additional manually created annotations of distinct size and precision. Thus, both analyses are fully evaluated in terms of execution-time, memory consumption and analysis precision, and its impact on the number of recognized components. We show that the analysis with higher precision allows the use of a smaller user-provided filter set and obtain a proper component recognition.