Affine Shape and Linear Shape Applications

Keyword(s):  
2018 ◽  
Vol 8 ◽  
pp. 27
Author(s):  
Kerem Ozturk ◽  
Esra Soylu ◽  
Ugur Topal

Background: Linear atelectasis is a focal area of subsegmental atelectasis with a linear shape. Linear atelectasis may occur as a consequence of subsegmental bronchial obstruction. Aims: We propose an early roentgen sign of obstructing lung tumors, namely perihilar linear atelectasis, and ascertain whether this phenomenon could be used as a sign to detect radiographically occult primary lung cancer. Materials and Methods: We performed a retrospective review of 45,000 posteroanterior chest radiographs to determine the frequency of appearance and characteristics of perihilar linear atelectasis. The perihilar region of chest radiographs was evaluated for the presence of linear atelectasis. When linear atelectasis was found, the total thickness was measured. Student's t-test was used to evaluate statistical significance, correlating the thickness of atelectasis and the presence of obstructing central primary lung cancer. Results: Perihilar linear atelectasis was demonstrated in 58 patients. Atelectasis was caused by an obstructing tumor in 21 (36%) cases and a variety of other conditions in 37 (64%) patients. A statistically significant relationship (P < 0.001) was observed between the dimension of perihilar linear atelectasis and primary lung cancer, with 16 of 19 patients with thick (>5.5 mm) perihilar linear atelectasis found to have primary lung cancer. Conclusion: Thick perihilar linear atelectasis is a new diagnostic roentgen sign that suggests subsegmental bronchial obstruction. In this patient subgroup, who are otherwise asymptomatic, a persistent linear atelectasis can be due to primary lung cancer.


Author(s):  
L. Chen ◽  
F. Rottensteiner ◽  
C. Heipke

Abstract. Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features.


2020 ◽  
pp. 004051752094254
Author(s):  
Ting Fu ◽  
Yuze Zhang ◽  
Nicholus Tayari Akankwasa ◽  
Nanliang Chen ◽  
Huiting Lin

The twist mechanism of the fiber strand in the condensing zone in compact spinning is complex. This paper proposes a dynamic model to evaluate the additional twist of the fiber strands. Based on the flow simulation in the condensing zone, the fiber trajectory in the suction slot was simulated and obtained. Several spinning parameters such as suction slot angle, suction slot width, negative pressure, and shape of suction slot, were varied to show their effects on the additional twist. The simulation results indicated that by increasing the suction slot angle from 5° to 10° the additional twist increased significantly. Higher negative pressure also leads to an increase in the additional twist. The suction slot width has a greater effect on the fiber trajectory than on the additional twist. An arc-shape suction slot increased the additional twist compared with a linear-shape one. An experimental test conducted revealed a precise agreement with the simulation results.


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