Robust active contours based on local threshold preprocessing fitting energies for fast segmentation of inhomogenous images

2021 ◽  
Author(s):  
Baojun Guo ◽  
Jinlong Cui ◽  
Beibei Gao
Keyword(s):  
Active Contours ◽  
Local Threshold

scholarly journals COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION

2016 ◽  
Vol III-8 ◽  
pp. 153-158 ◽  
Author(s):  
V. Paravolidakis ◽  
K. Moirogiorgou ◽  
L. Ragia ◽  
M. Zervakis ◽  
C. Synolakis
Keyword(s):  
Edge Detection ◽  
Human Population ◽  
Active Contours ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Entire Region ◽  
Local Threshold ◽  
Step Algorithm ◽  
Fitting In ◽  
Processing Steps

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

scholarly journals COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION

2016 ◽  
Vol III-8 ◽  
pp. 153-158 ◽  
Author(s):  
V. Paravolidakis ◽  
K. Moirogiorgou ◽  
L. Ragia ◽  
M. Zervakis ◽  
C. Synolakis
Keyword(s):  
Edge Detection ◽  
Human Population ◽  
Active Contours ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Entire Region ◽  
Local Threshold ◽  
Step Algorithm ◽  
Fitting In ◽  
Processing Steps

Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.

scholarly journals Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ricardo A. Gonzales ◽  
Felicia Seemann ◽  
Jérôme Lamy ◽  
Per M. Arvidsson ◽  
Einar Heiberg ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Active Contours ◽  
Imaging Biomarkers ◽  
Observer Variability ◽  
Time Resolved ◽  
Variability Analysis ◽  
Automated Method ◽  
Inter Observer Variability ◽  
Atrial Size ◽  
And Function

Abstract Background Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. Methods This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. Results The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. Conclusion The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function.

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