Occipital spurs on lateral cephalometric radiographs: morphologic and morphometric features

2021 ◽  
Author(s):  
Dilara Nil Gunacar ◽  
Merve Gonca ◽  
Taha Emre Kose
Keyword(s):  
Morphometric Features

scholarly journals Robotic pancreaticoduodenecectomy improves outcomes for patients with high risk morphometric features

HPB ◽  
2017 ◽  
Vol 19 ◽  
pp. S42-S43
Author(s):  
P.R. Varley ◽  
M.S. Zenati ◽  
A. Klobuka ◽  
J. Tobler ◽  
H.J. Zeh ◽  
...  
Keyword(s):  
High Risk ◽  
Morphometric Features

Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation

2013 ◽  
Vol 333-335 ◽  
pp. 1065-1070
Author(s):  
Yuan Li ◽  
Fu Cang Jia ◽  
Xiao Dong Zhang ◽  
Cheng Huang ◽  
Huo Ling Luo
Keyword(s):  
Magnetic Resonance ◽  
Image Features ◽  
Morphometric Features ◽  
Segmentation Methods ◽  
Local Patch ◽  
Ranking Strategy ◽  
And Performance ◽  
Spatially Variant ◽  
Hippocampus Segmentation ◽  
Atlas Segmentation

The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The local ranking strategy is based on the metric of normalized cross correlation (NCC). Unlike its predecessors, this method estimates the fusion of each voxel patch-by-patch and makes use of gray image features as a prior. Validation results on 33 pairs of hippocampus MR images show good performance on the segmentation of hippocampus.

Two new annual fishes (Cyprinodontiformes: Rivulidae) unexpectedly discovered in the highlands of southern Brazil

Zootaxa ◽  
2021 ◽  
Vol 4949 (3) ◽  
pp. 499-520
Author(s):  
LUIS ESTEBAN KRAUSE LANÉS ◽  
MATHEUS VIEIRA VOLCAN ◽  
LEONARDO MALTCHIK
Keyword(s):  
New Species ◽  
Conservation Status ◽  
The Body ◽  
Colour Pattern ◽  
Southern Brazil ◽  
Body Depth ◽  
Body Contact ◽  
Morphometric Features ◽  
Natural Grassland ◽  
Two Populations

Two new species of Austrolebias are described based on specimens collected from temporary pools located in natural grassland landscape within the Araucaria Forest domain at exceptionally high altitudes (~1000 meters a.s.l.). Austrolebias botocudo sp. n. and Austrolebias nubium sp. n. occur, respectively, in drainages of upper rio Apuaê-Inhandava (upper rio Uruguay basin) and upper rio Taquari-Antas (upper rio Jacuí, Laguna dos Patos basin), in the Meridional Plateau of southern Brazil. Despite an intensive survey conducted in the area, only two populations of each species were recorded. Both new species occurs at altitudes that are among the higher recorded for species of the genus, and both are assigned to the subgenus Acrolebias. The new species described herein are easily distinguished for its congeners by the colour pattern of males, by presence of melanophores irregularly distributed in different parts of the body, contact organs cover the body and anal fins, position of fins related with vertebrae, by preopercular and mandibular series of neuromasts united, by a series of morphometric features and by larger maximum standard length. Austrolebias botocudo and A. nubium are distinguished from each other by colour pattern of males, length of contact organs in the flank and number of contact organs in scales of lateral line, dorsal profile of head, number of neuromasts in the preopercular + mandibular series, body depth in females, and by basihyal cartilage length. Additionally, we discuss the conservation status of the new species, and provided an identification key for the species of the subgenus Acrolebias. 

scholarly journals Comparative study of the zooplankton composition of six lacustrine ecosystems in Central Brazil during the dry season

2000 ◽  
Vol 60 (1) ◽  
pp. 101-111 ◽  
Author(s):  
F. L. do R. M. STARLING
Keyword(s):  
Zooplankton Community ◽  
Federal District ◽  
Dry Season ◽  
Zooplankton Species ◽  
Waste Stabilisation Ponds ◽  
Central Brazil ◽  
Morphometric Features ◽  
Santa Maria ◽  
Sørensen Index

Zooplankton community from six lacustrine ecosystems located in Federal District (Central Brazil) was studied based on samples collected during the dry season (July to September). A total of 71 taxa were recorded: 44 rotifers, 17 cladocerans and 10 copepods. The highest number of zooplankton species was recorded in oligotrophic Bonita Pond (32 species) and the lowest number in hypertrophic waste stabilisation ponds (7 species). This tendency of decreasing the diversity with increasing trophic level was consistent with a cluster analysis of the samples based on Sorensen index of similarity. From the overall similarity dendrogram, two groups of ecosystems were distinguished: one containing the natural ponds Bonita and Formosa and the other comprising the reservoirs Santa Maria, Descoberto and Paranoá. The role of morphometric features in determining the zooplankton community in such lacustrine ecosystems was also discussed.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

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