A new image analysis algorithm for calculating percentage wood failure

Holzforschung ◽  
2008 ◽  
Vol 62 (2) ◽  
pp. 248-251 ◽  
Author(s):  
Yin Yang ◽  
Meng Gong ◽  
Ying Hei Chui
Keyword(s):  
Image Analysis ◽  
Wood Failure ◽  
Analysis Algorithm ◽  
Image Analysis Algorithm

scholarly journals Analysis algorithm for sky type and ice halo recognition in all-sky images

2019 ◽  
Vol 12 (8) ◽  
pp. 4241-4259 ◽  
Author(s):  
Sylke Boyd ◽  
Stephen Sorenson ◽  
Shelby Richard ◽  
Michelle King ◽  
Morton Greenslit
Keyword(s):  
Time Series ◽  
Image Analysis ◽  
Great Plains ◽  
Detailed Comparison ◽  
Analysis Algorithm ◽  
Long Time Series ◽  
Image Analysis Algorithm ◽  
Long Time ◽  
Atmospheric Radiation Measurement ◽  
Sky Type

Abstract. Halo displays, in particular the 22∘ halo, have been captured in long time series of images obtained from total sky imagers (TSIs) at various Atmospheric Radiation Measurement (ARM) sites. Halo displays form if smooth-faced hexagonal ice crystals are present in the optical path. We describe an image analysis algorithm for long time series of TSI images which scores images with respect to the presence of 22∘ halos. Each image is assigned an ice halo score (IHS) for 22∘ halos, as well as a photographic sky type (PST), which differentiates cirrostratus (PST-CS), partially cloudy (PST-PCL), cloudy (PST-CLD), or clear (PST-CLR) within a near-solar image analysis area. The color-resolved radial brightness behavior of the near-solar region is used to define the discriminant properties used to classify photographic sky type and assign an ice halo score. The scoring is based on the tools of multivariate Gaussian analysis applied to a standardized sun-centered image produced from the raw TSI image, following a series of calibrations, rotation, and coordinate transformation. The algorithm is trained based on a training set for each class of images. We present test results on halo observations and photographic sky type for the first 4 months of the year 2018, for TSI images obtained at the Southern Great Plains (SGP) ARM site. A detailed comparison of visual and algorithm scores for the month of March 2018 shows that the algorithm is about 90 % reliable in discriminating the four photographic sky types and identifies 86 % of all visual halos correctly. Numerous instances of halo appearances were identified for the period January through April 2018, with persistence times between 5 and 220 min. Varying by month, we found that between 9 % and 22 % of cirrostratus skies exhibited a full or partial 22∘ halo.

scholarly journals Osteoid Metaplasia in Femoral Artery Plaques Is Associated With the Clinical Severity of Lower Extremity Artery Disease in Men

2020 ◽  
Vol 7 ◽  
Author(s):  
Mirjami Laivuori ◽  
Johanna Tolva ◽  
A. Inkeri Lokki ◽  
Nina Linder ◽  
Johan Lundin ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Lower Extremity ◽  
Femoral Artery ◽  
Analysis Algorithm ◽  
Image Analysis Algorithm ◽  
Artery Disease ◽  
Area Percentage ◽  
Lower Extremity Artery Disease ◽  
Whole Slide Images

Lamellar metaplastic bone, osteoid metaplasia (OM), is found in atherosclerotic plaques, especially in the femoral arteries. In the carotid arteries, OM has been documented to be associated with plaque stability. This study investigated the clinical impact of OM load in femoral artery plaques of patients with lower extremity artery disease (LEAD) by using a deep learning-based image analysis algorithm. Plaques from 90 patients undergoing endarterectomy of the common femoral artery were collected and analyzed. After decalcification and fixation, 4-μm-thick longitudinal sections were stained with hematoxylin and eosin, digitized, and uploaded as whole-slide images on a cloud-based platform. A deep learning-based image analysis algorithm was trained to analyze the area percentage of OM in whole-slide images. Clinical data were extracted from electronic patient records, and the association with OM was analyzed. Fifty-one (56.7%) sections had OM. Females with diabetes had a higher area percentage of OM than females without diabetes. In male patients, the area percentage of OM inversely correlated with toe pressure and was significantly associated with severe symptoms of LEAD including rest pain, ulcer, or gangrene. According to our results, OM is a typical feature of femoral artery plaques and can be quantified using a deep learning-based image analysis method. The association of OM load with clinical features of LEAD appears to differ between male and female patients, highlighting the need for a gender-specific approach in the study of the mechanisms of atherosclerotic disease. In addition, the role of plaque characteristics in the treatment of atherosclerotic lesions warrants further consideration in the future.

scholarly journals Validation of an image analysis algorithm to quantify leukocyte populations using whole slide image analysis

2013 ◽  
Vol 1 (S1) ◽  
Author(s):  
Anthony J Milici ◽  
David Young ◽  
Steven J Potts ◽  
Holger Lange ◽  
Nicholas D Landis ◽  
...  
Keyword(s):  
Image Analysis ◽  
Analysis Algorithm ◽  
Whole Slide Image ◽  
Image Analysis Algorithm ◽  
Slide Image ◽  
Leukocyte Populations

Differentiation of free and embedded porcine pancreatic islets using a novel automated image analysis algorithm

1997 ◽  
Vol 29 (4) ◽  
pp. 1974 ◽  
Author(s):  
K.J. Wile ◽  
W. Schwartzkopf ◽  
M. Olsen ◽  
C. Laxer ◽  
T. Fetterhoff ◽  
...  
Keyword(s):  
Image Analysis ◽  
Pancreatic Islets ◽  
Automated Image Analysis ◽  
Analysis Algorithm ◽  
Image Analysis Algorithm
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