Probabilistic classification of intracranial gliomas in digital microscope images based on EGFR quantity

2009 ◽  
Author(s):  
Marcin Grzegorzek ◽  
Marianna Buckan ◽  
Sigrid Horn
Keyword(s):  
Probabilistic Classification ◽  
Microscope Images ◽  
Digital Microscope

scholarly journals Classification of endonasal HHT lesions using digital microscopy

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
F. Haubner ◽  
A. Schneider ◽  
H. Schinke ◽  
M. Bertlich ◽  
B. G. Weiss ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Treatment Modalities ◽  
Hereditary Haemorrhagic Telangiectasia ◽  
Digital Microscopy ◽  
Digital Microscope ◽  
Lower Severity ◽  
Gender Specific Differences ◽  
Vegf Level

Abstract Background Recurrent spontaneous epistaxis is the most common clinical manifestation and the most debilitating symptom in hereditary haemorrhagic telangiectasia (HHT) patients. To this date, there exist only a classification of HHT patients by different genetic mutations. There is no standard classification for the mucocutaneous endonasal manifestations of HHT. The aim of the present study was to document the variety of endonasal HHT lesions using digital microscopy and to propose a clinical classification. Methods We recorded the endonasal HHT lesions of 28 patients using a digital microscope. We reconstructed the 3D images und videos recorded by digital microscope afterwards and classified the endonasal lesions of HHT in two classes: Grade A, presence of only flat telangiectasias in the mucosa level and Grade B, (additional) presence of raised berry or wart-like telangiectasia spots. We investigated also Haemoglobin level by routine laboratory procedures, plasma VEGF level by ELISA, Severity of epistaxis by epistaxis severity score (ESS) and quality of life by a linear visual analogue scale (VAS). Results We found a higher quality of life and a lower severity of epistaxis in Grade A patients in comparison to Grade B patients. No difference in plasma VEGF level and in Haemoglobin between Grad A patients and Grade B patients could be detected. Plasma VEGF levels showed no gender specific differences. It could also not be correlated to the extranasal manifestation. Conclusion The classification for endonasal manifestation of HHT proposed in this study indicates severity of epistaxis und quality of life. Digital microscopy with the ability of 3D reconstruction of images presents a useful tool for such classifications. The classification of endonasal HHT lesions using digital microscopy allows to evaluate the dynamic of HHT lesions in the course of time independent of examiner. This allows also to evaluate the efficacy of the different treatment modalities by dynamic of HHT lesions. Moreover digital microscopy is very beneficial in academic teaching of rare diseases.

Probabilistic Classification of Age by Third Molar Development: The Use of Soft Evidence

2012 ◽  
Vol 58 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Fabio Corradi ◽  
Vilma Pinchi ◽  
Iljà Barsanti ◽  
Stefano Garatti
Keyword(s):  
Third Molar ◽  
Probabilistic Classification

scholarly journals Probabilistic Classification of Tree and Shrub Vegetation on Phytogeographic System

2015 ◽  
Vol 4 (6) ◽  
Author(s):  
Thomaz Correa e Castro da Costa ◽  
Andreia Fonseca Silva ◽  
Luciana Mara Temponi de Oliveira ◽  
Joao Herbert Moreira Viana
Keyword(s):  
Probabilistic Classification

scholarly journals Quality of screen printing in knit fabric for different mesh count on semi solid design

2020 ◽  
Vol 6 (6) ◽  
Author(s):  
Rasheda Begum Dina ◽  
Md Zulhash Uddin ◽  
UmmulKhair Fatema
Keyword(s):  
Screen Printing ◽  
Visual Assessment ◽  
Color Difference ◽  
Mesh Screen ◽  
Microscope Images ◽  
Different Types ◽  
Digital Microscope ◽  
Semi Solid ◽  
Screen Mesh

In semi solid design, the parameters of the quality of the printed fabric were color fastness, level of print detail, color difference, print coverage, saw-tooth effect, line sharpness etc. Actually, printed fabric quality was evaluated by these parameters. Again, print coverage, saw-tooth effect, line sharpness, etc. were examined by estimating different distances, angles etc. in printed fabric and these factors were compared with image positives. Mesh opening effect on the quality of screen printed fabric was investigated after printing the semi solid design on knit fabric using different mesh count screens and different types of the link. To print semi solid design on knit fabric different types of ink as well as non-identical mesh count was used. Then mesh screen out come on the design and form of printed fabric in screen printing was examined. For the evaluation of semi solid design effect there remains two different methods.The first one is visual assessment and another way is microscope observation. Here, to determine the probability of the amount of ink flowing by the screen, mesh opening area of every screen was considered and it was done from respective digital microscope images.

Probabilistic classification of tumour habitats in soft tissue sarcoma

2018 ◽  
Vol 31 (11) ◽  
pp. e4000 ◽  
Author(s):  
Shu Xing ◽  
Carolyn R. Freeman ◽  
Sungmi Jung ◽  
Robert Turcotte ◽  
Ives R. Levesque
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Probabilistic Classification

Real-Time Classification of Water Spray and Leaks for Robotic Firefighting

2015 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Joshua G. McNeil ◽  
Brian Y. Lattimer
Keyword(s):  
Vision System ◽  
Training Dataset ◽  
Water Spray ◽  
Bayes Classifier ◽  
Test Dataset ◽  
Hazardous Environments ◽  
Water Classification ◽  
Probabilistic Classification ◽  
Suppression System

Robotic firefighting is an area of increased focus as a way of limiting the exposure of firefighters to hazardous environments. A suppression system must incorporate multiple functionalities to allow for closed-loop firefighting control. One area of development is classifying water spray as a way of correcting errors between suppressant placement and fire location. An IR vision system is presented which is capable of identifying water. Image segmentation is performed, followed by a process that classifies regions of interest as water or non-water objects. A probabilistic classification method, using Naïve Bayes classifier, was applied on a varied dataset of differing water temperatures and sprays. Objects were segmented using frame differencing with image intensity and difference thresholds. Segments were manually labeled to create a training dataset. Precision, recall, F-measure, and G-measure results of the classifier on a separate test dataset ranged from 86.1-97.4% for classifying water objects using the test dataset with water classification alone having 94.2-97.4% accuracy.

Detection and Classification of Pollen Grain Microscope Images

2020 ◽  
Author(s):  
Sebastiano Battiato ◽  
Alessandro Ortis ◽  
Francesca Trenta ◽  
Lorenzo Ascari ◽  
Mara Politi ◽  
...  
Keyword(s):  
Pollen Grain ◽  
Microscope Images
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