Pattern Detection Framework for MRI Images and Labeling Volume of Interest (VoI)

2015 ◽  
pp. 323-332 ◽  
Author(s):  
Rupal Snehkunj ◽  
Richa Mehta ◽  
Aashish N. Jani
Keyword(s):  
Pattern Detection ◽  
Volume Of Interest

A confocal laser scanning microscope for fluorescence and reflection at video rates

1989 ◽  
Vol 47 ◽  
pp. 134-135
Author(s):  
P.M. Houpt ◽  
A. Draaijer
Keyword(s):  
Light Source ◽  
Laser Scanning ◽  
Focal Point ◽  
Confocal Laser Scanning Microscope ◽  
Confocal Laser ◽  
Point Light Source ◽  
Volume Of Interest ◽  
Ion Laser ◽  
Point Light ◽  
Point Detector

In confocal microscopy, the object is scanned by the coinciding focal points (confocal) of a point light source and a point detector both focused on a certain plane in the object. Only light coming from the focal point is detected and, even more important, out-of-focus light is rejected.This makes it possible to slice up optically the ‘volume of interest’ in the object by moving it axially while scanning the focused point light source (X-Y) laterally. The successive confocal sections can be stored in a computer and used to reconstruct the object in a 3D image display.The instrument described is able to scan the object laterally with an Ar ion laser (488 nm) at video rates. The image of one confocal section of an object can be displayed within 40 milliseconds (1000 х 1000 pixels). The time to record the total information within the ‘volume of interest’ normally depends on the number of slices needed to cover it, but rarely exceeds a few seconds.

High Spatial Resolution Compositional Analysis at Interfaces

1980 ◽  
Vol 38 ◽  
pp. 356-359
Author(s):  
John B. Vander Sande ◽  
Thomas F. Kelly ◽  
Douglas Imeson
Keyword(s):  
Electron Microscope ◽  
High Spatial Resolution ◽  
Compositional Analysis ◽  
Scanning Transmission Electron Microscope ◽  
Thin Specimen ◽  
X Ray ◽  
Volume Of Interest ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Electron Energy Loss

In the scanning transmission electron microscope (STEM) a fine probe of electrons is scanned across the thin specimen, or the probe is stationarily placed on a volume of interest, and various products of the electron-specimen interaction are then collected and used for image formation or microanalysis. The microanalysis modes usually employed in STEM include, but are not restricted to, energy dispersive X-ray analysis, electron energy loss spectroscopy, and microdiffraction.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques

2021 ◽  
Author(s):  
Paraskevi Massara ◽  
Charles D G Keown-Stoneman ◽  
Lauren Erdman ◽  
Eric O Ohuma ◽  
Celine Bourdon ◽  
...  
Keyword(s):  
Latent Class ◽  
Growth Patterns ◽  
Pattern Detection ◽  
Distance Measures ◽  
Longitudinal Growth ◽  
Important Health ◽  
Modelling Techniques ◽  
Different Populations ◽  
Growth Features ◽  
Time Invariant

Abstract Background Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. Methods We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. Results Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%–89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. Conclusion Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

scholarly journals New Method for Analysis of the Temporomandibular Joint Using Cone Beam Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3070
Author(s):  
Sebastian Iwaszenko ◽  
Jakub Munk ◽  
Stefan Baron ◽  
Adam Smoliński
Keyword(s):  
Computed Tomography ◽  
Temporomandibular Joint ◽  
Cross Section ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Section Plane ◽  
New Approach ◽  
Volume Of Interest ◽  
Gap Width

Modern dentistry commonly uses a variety of imaging methods to support diagnosis and treatment. Among them, cone beam computed tomography (CBCT) is particularly useful in presenting head structures, such as the temporomandibular joint (TMJ). The determination of the morphology of the joint is an important part of the diagnosis as well as the monitoring of the treatment results. It can be accomplished by measurement of the TMJ gap width at three selected places, taken at a specific cross-section. This study presents a new approach to these measurements. First, the CBCT images are denoised using curvilinear methods, and the volume of interest is determined. Then, the orientation of the vertical cross-section plane is computed based on segmented axial sections of the TMJ head. Finally, the cross-section plane is used to determine the standardized locations, at which the width of the gap between condyle and fossa is measured. The elaborated method was tested on selected TMJ CBCT scans with satisfactory results. The proposed solution lays the basis for the development of an autonomous method of TMJ index identification.

scholarly journals Learning-free pattern detection for manuscript research:

2021 ◽  
Author(s):  
Hussein Mohammed ◽  
Volker Märgner ◽  
Giovanni Ciotti
Keyword(s):  
South Asian ◽  
State Of The Art ◽  
Research Question ◽  
Software Tool ◽  
Pattern Detection ◽  
Asian Studies ◽  
Medieval Manuscripts ◽  
South Asian Studies ◽  
Training Samples ◽  
Nearest Neighbour Classifier

AbstractAutomatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.

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