COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using a U-NET and Probabilistic Active Contour Segmentation

2021 ◽  
Author(s):  
Leopoldo Cendejas-Zaragoza ◽  
Diomar E. Rodriguez-Obregon ◽  
Aldo R. Mejia-Rodriguez ◽  
Edgar R. Arce-Santana ◽  
Alejandro Santos-Diaz
Keyword(s):  
Active Contour ◽  
Ct Images ◽  
Pulmonary Lesion ◽  
Contour Segmentation

Quantification of Lymphedema in a Rat Model by 3D-Active Contour Segmentation by Magnetic Resonance Imaging

2012 ◽  
Vol 10 (1) ◽  
pp. 25-29 ◽  
Author(s):  
Tanja Sommer ◽  
Martin Meier ◽  
Frank Bruns ◽  
Reinhard Pabst ◽  
Gerhard Breves ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rat Model ◽  
Active Contour ◽  
Resonance Imaging ◽  
Contour Segmentation

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

scholarly journals Computer-Assisted Segmentation of Videocapsule Images Using Alpha-Divergence-Based Active Contour in the Framework of Intestinal Pathologies Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
L. Meziou ◽  
A. Histace ◽  
F. Precioso ◽  
O. Romain ◽  
X. Dray ◽  
...  
Keyword(s):  
Gastrointestinal Tract ◽  
Active Contour ◽  
Standard Technique ◽  
Poor Quality ◽  
Computer Assisted ◽  
Contour Segmentation ◽  
Semiautomatic Segmentation ◽  
Different Types ◽  
Statistical Similarity ◽  
Wireless Capsule

Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the “gold standard” technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this paper, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi)polyp(s) segmentation, to radiation enteritis delineation.

scholarly journals SEGMENTASI CITRA THORAX PARU-PARU MANUSIA DARI SINAR-X MENGGUNAKAN METODE KONTUR AKTIF

2019 ◽  
Vol 4 (2) ◽  
pp. 8-10
Author(s):  
Sintha Syaputri ◽  
Zulkarnain
Keyword(s):  
Active Contour ◽  
Active Contours ◽  
Contour Method ◽  
Image Objects ◽  
Contour Segmentation ◽  
Segmentation Process ◽  
Coordinate Position ◽  
Image Improvement ◽  
The Right

Research on medical image objects in the form of lung images of thoracic X-Rayis increasingly being developed because the information contained in medical images is used to analyze and determine the shape of the lungs. The process of normalization and image improvement is needed and continued with the segmentation process using the right method. The active snake contour method is used because it is resistant to the noise around the object. The research has been usedthe Matlab software GUI program version R2015a. The image through the initial preprocessing stage is converted into a grayscale image. The segmentation process used after the initialization process in the form of a small circle curve placed of the object to be segmented and the determination position of the active contour or detemination of the active parameters of the contour. Determination of the value active contour parameters greatly influences the results of segmentation and influences the direction of active contour movement. If the active coordinate position of the contour is outside the area to be segmented it will cause active contours to move away from the object. Validation the level of accuracy of segmentation results is done by comparing the results of the snake active contour segmentation to the results of manual segmentationused MSE method

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