scholarly journals Abnormality Detection in Ovarian Ultrasound Images using Active Contours

2014 ◽  
Vol 1 (4) ◽  
pp. 14-23
Author(s):  
B.S. Usha ◽  
S Sandya
Keyword(s):  
Active Contours ◽  
Ultrasound Images ◽  
Abnormality Detection

Abnormality Detection of Fetus Using Ultrasound Images

2019 ◽  
Author(s):  
S. Bhavani ◽  
LINCY JEMINA S ◽  
PRABHA B ◽  
Shanthini Smilin
Keyword(s):  
Ultrasound Images ◽  
Abnormality Detection

scholarly journals Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Prabal Poudel ◽  
Alfredo Illanes ◽  
Debdoot Sheet ◽  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
Active Contours ◽  
Computation Time ◽  
Machine Intelligence ◽  
Ease Of Use ◽  
Human Interaction ◽  
Ultrasound Images ◽  
Learning Approaches ◽  
Thyroid State ◽  
Advantages And Disadvantages

The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime importance to track the shape and size of thyroid over time in order to evaluate its state. Thyroid segmentation and volume computation are important tools that can be used for thyroid state tracking assessment. Most of the proposed approaches are not automatic and require long time to correctly segment the thyroid. In this work, we compare three different nonautomatic segmentation algorithms (i.e., active contours without edges, graph cut, and pixel-based classifier) in freehand three-dimensional ultrasound imaging in terms of accuracy, robustness, ease of use, level of human interaction required, and computation time. We figured out that these methods lack automation and machine intelligence and are not highly accurate. Hence, we implemented two machine learning approaches (i.e., random forest and convolutional neural network) to improve the accuracy of segmentation as well as provide automation. This comparative study intends to discuss and analyse the advantages and disadvantages of different algorithms. In the last step, the volume of the thyroid is computed using the segmentation results, and the performance analysis of all the algorithms is carried out by comparing the segmentation results with the ground truth.

scholarly journals 3D segmentation of thyroid ultrasound images using active contours

2016 ◽  
Vol 2 (1) ◽  
pp. 467-470 ◽  
Author(s):  
Prabal Poudel ◽  
Christian Hansen ◽  
Julian Sprung ◽  
Michael Friebe
Keyword(s):  
Active Contour ◽  
3D Model ◽  
Active Contours ◽  
Active Contour Model ◽  
Thyroid Volume ◽  
Reconstruction Algorithm ◽  
Ultrasound Images ◽  
3D Segmentation ◽  
Segmentation Accuracy ◽  
2D Ultrasound

AbstractIn this paper, we propose a method to segment the thyroid from a set of 2D ultrasound images. We extended an active contour model in 2D to generate a 3D segmented thyroid volume. First, a preprocessing step is carried out to suppress the noise present in US data. Second, an active contour is used to segment the thyroid in each of the 2D images. Finally, all the segmented thyroid images are passed to a 3D reconstruction algorithm to obtain a 3D model of the thyroid. We obtained an average segmentation accuracy of 86.7% in six datasets with a total of 703 images.

Automatic measurement of dermal thickness from B-scan ultrasound images using active contours

2005 ◽  
Vol 11 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Jean-Michel Lagarde ◽  
Jerome George ◽  
Romain Soulcie ◽  
David Black
Keyword(s):  
Active Contours ◽  
Automatic Measurement ◽  
Ultrasound Images ◽  
Dermal Thickness
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