Gallbladder Segmentation from 2–D Ultrasound Images Using Active Contour Models and Gradient Vector Flow

2011 ◽  
pp. 86-97
Author(s):  
Marcin Ciecholewski
Keyword(s):  
Active Contour ◽  
Ultrasound Images ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Active Contour Models ◽  
Contour Models

scholarly journals A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow

2020 ◽  
Vol 10 (18) ◽  
pp. 6163 ◽  
Author(s):  
Joaquín Rodríguez ◽  
Gilberto Ochoa-Ruiz ◽  
Christian Mata
Keyword(s):  
Active Contour ◽  
Automatic Segmentation ◽  
Prostate Gland ◽  
Manual Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Active Contour Models ◽  
Prostate Segmentation ◽  
Segmentation Process ◽  
Contour Models

Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.

scholarly journals ACTIVE CONTOUR MODELS-BASED SEGMENTATION OF LEFT VENTRICLE IN ULTRASOUND IMAGES FOR DIFFERENT AXES VIEWS

2021 ◽  
Vol 21 (03) ◽  
pp. 2150031
Author(s):  
YANG ZHENG ◽  
ZHONGPING CHEN ◽  
JIAKE WANG ◽  
SHU JIANG ◽  
YU LIU
Keyword(s):  
Left Ventricle ◽  
Active Contour ◽  
Active Contour Model ◽  
Ultrasound Images ◽  
Similarity Coefficients ◽  
Active Contour Models ◽  
Shape Constraint ◽  
Chamber View ◽  
Contour Models ◽  
Axis View

Segmentation of the left ventricle in ultrasound images for viewing through different axes is a critical aspect. This paper proposes the development of novel active contour models with shape constraint to segment the left ventricle in three different axis views of the ultrasound images. The shapes observed in all the axis views of the left ventricle were not similar. According to the cardiac cycle, the valve opening in the end-diastolic phase influenced the left ventricle segmentation; hence, a shape constraint was embedded in the active contour model to keep ventricle’s shape, especially in the Apical long-axis view and Apical four-chamber view. Furthermore, for different axes views, diverse active contour models were proposed to fit each situation. The shape constraint in each function for different views exhibited a specific shape during the function iteration. In order to speed up the algorithm evolution, previous results were used for the initialization of the present active contour. We evaluated the proposed method on 57 patients with three different views: Apical long-axis view, Apical four-chamber view and Short-axis view at the papillary muscle level and obtained the Dice similarity coefficients of [Formula: see text], [Formula: see text] and [Formula: see text] and the Hausdorff distance metrics of [Formula: see text], [Formula: see text] and [Formula: see text], respectively. The qualitative and quantitative evaluations showed an advantage of our method in terms of segmentation accuracy.

scholarly journals Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 192
Author(s):  
Umer Sadiq Khan ◽  
Xingjun Zhang ◽  
Yuanqi Su
Keyword(s):  
Object Detection ◽  
Fourier Transformation ◽  
Active Contour ◽  
Active Contour Model ◽  
Salient Object Detection ◽  
Fast Fourier Transformation ◽  
Salient Object ◽  
Active Contour Models ◽  
Contour Model ◽  
Contour Models

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline.

Accurate blemish detection with active contour models

1996 ◽  
Vol 14 (1) ◽  
pp. 77-89 ◽  
Author(s):  
Qingsheng Yang ◽  
John A. Marchant
Keyword(s):  
Active Contour ◽  
Active Contour Models ◽  
Contour Models

T1w MRI Brain Tumor Image Segmentation using Active Contour Models and Convolution Neural Network Fusion

2021 ◽  
Author(s):  
Vamisdhar Entireddy ◽  
Babu K Rajesh ◽  
R Sampathkumar ◽  
Jyothirmai Gandeti ◽  
Syed Shameem ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Active Contour ◽  
Convolution Neural Network ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models
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