Lip segmentation using localized active contour model with automatic initial contour

2017 ◽  
Vol 29 (5) ◽  
pp. 1417-1424 ◽  
Author(s):  
Yuanyao Lu ◽  
Tenghe Zhou
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Contour Model ◽  
Lip Segmentation

Lip Segmentation Based on Combined Color Space and ACM with Rhombic Initial Contour

2018 ◽  
Vol 32 (08) ◽  
pp. 1856008
Author(s):  
Yuanyao Lu ◽  
Qingqing Liu
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Color Space ◽  
Initial Contour ◽  
Discrete Hartley Transform ◽  
Contour Model ◽  
Lip Reading ◽  
Reading System ◽  
Lip Segmentation ◽  
Text Component

Lip segmentation is one of critical steps in a lip-reading system, because it closely relates to the accuracy of system recognition. In this paper, we aim to improve the accuracy of lip segmentation. A novel color space is proposed which consists of the [Formula: see text] component in the CIE-LUV space and the sum of [Formula: see text]2 and [Formula: see text]3 components of the image after discrete Hartley transform (DHT). We select a rhombus as the initial contour as its shape is approximate to a closed lip shape relatively. These notions are achieved based on the method of the Active contour model. The active contour model (ACM) is performed by the Chan–Vese model, and the result of each component is gained separately. Finally, the ultimate results are obtained by merging the result of each component together. Through experiments we can get a conclusion that this method can get more accurate and smoother lip contour. Meanwhile, the proposed method is more efficient compared with the classic ACM because it avoids some problems in the classic active contour model, like the radius of the initial contour needs to be set manually according to the size of images.

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

scholarly journals Active contour model for satellitale image combining local and global region based approach

2019 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Mouri Hayat ◽  
Fizazi Hadria
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Multiple Objects ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Segmentation Of Images ◽  
Global And Local ◽  
Local Image

<p>Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works. </p><p><em> </em></p>

Heuristic Initialization for Active Contour Models in CT/MRI Image Processing

2015 ◽  
Vol 15 (03) ◽  
pp. 1550010
Author(s):  
Hao Liu ◽  
Hongbo Qian ◽  
Ning Dai ◽  
Jianning Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Heuristic Method ◽  
Initial Contour ◽  
Active Contour Models ◽  
Contour Model ◽  
Segmentation Approach ◽  
Edge Points ◽  
Contour Models ◽  
Level Set Evolution

It is an important segmentation approach of CT/MRI images to automatically extract contours in every slice using active contour models. The key point of the segmentation approach is to automatically construct initial contours for active contour models because any active contour model is sensitive to its initial contour. This paper presents an algorithm to construct such initial contours using a heuristic method. Assume that the contour in previous slice (previous contour) is accurate. The contour in the current slice (current contour) is constructed according to the previous contour using the way: Recognition and link of edge points of tissues according to the previous contour. The contour linking edge points is used as the initial contour of the distance regularized level set evolution (DRLSE) method and then an accurate contour can be extracted in the current slice.

scholarly journals Fractional Differentiation-Based Active Contour Model Driven by Local Intensity Fitting Energy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Gu ◽  
Renfang Wang
Keyword(s):  
Fractional Order ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Input Image ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Proposed Model

A novel active contour model is proposed for segmentation images with inhomogeneity. Firstly, fractional order filter is defined by eight convolution masks corresponding to the image orientation in the eight compass directions. Then, the fractional order differentiation image is obtained and applied to the level set method. Secondly, we defined a new energy functional based on local image information and fractional order differentiation image; the proposed model not only can describe the input image more accurately but also can deal with intensity inhomogeneity. Local fitting term can enhance the ability of the model to deal with intensity inhomogeneity. The defined penalty term is used to reduce the occurrence of false boundaries. Finally, in order to eliminate the time-consuming step of reinitialization and ensure stable evolution of level set function, the Gaussian filtering method is used. Experiments on synthetic and real images show that the proposed model is efficient for images with intensity inhomogeneity and flexible to initial contour.

scholarly journals Edge Enhancement Based Active Contour Model for Segmentation of Brain Tumor in MRI Images

2021 ◽  
Vol 19 (3) ◽  
Author(s):  
Mustafa Rashid Ismael
Keyword(s):  
Edge Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Morphological Operations ◽  
Significant Information ◽  
Automated Method ◽  
Contour Model ◽  
Tumor Region ◽  
Edge Sharpening

Tumor segmentation is one of the most significant tasks in brain image analysis due to the significant information obtained by the tumor region. Therefore, many methods have been proposed during the last two decades for segmenting the tumor in MRI images. In this paper, an automated method is proposed using an active contour model with an initial contour creation using edge sharpening, thresholding, and morphological operations. Four methods of edge detection are utilized in the edge sharpening process (Sobel, Roberts, Prewitt, and Canny) and their performance was investigated in terms of Dice, Jaccard, and F1 score. The experiments were implemented on BRATS datasets with both HGG and LGG images. The study indicates that sharpening the edges using edge detection is essential to improve the segmentation of the tumor region especially when it is used with an active contour model. The achieved results show the effectiveness of the proposed method and it outperformed some recent existing methods.

scholarly journals Image Segmentation Using Signed Pressure Force Based Active Contour Model

2013 ◽  
Vol 12 (1) ◽  
pp. 3195-3200
Author(s):  
Sagar Chouksey ◽  
Mayur Ghadle ◽  
Shreya Sharma ◽  
Rohan Puranik
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Computational Cost ◽  
Initial Contour ◽  
Pressure Force ◽  
Binary Function ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function

A novel signed pressure force (SPF) based active contour model (ACM) is proposed in this work. It is implemented with help of Gaussian filtering regularized level set method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of this method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed SPF with ACM has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient. The computational cost for traditional re-initialization can also be reduced.

A Deep Learning Based Level Set Model for Pancreas Segmentation

2020 ◽  
Vol 10 (11) ◽  
pp. 2681-2685
Author(s):  
Zhaoxuan Gong ◽  
Wei Guo ◽  
Wei Zhou ◽  
Dazhe Zhao ◽  
Wenjun Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Dice Similarity Coefficient ◽  
Contour Model ◽  
Differential Operation ◽  
Fractional Differential ◽  
Pancreas Segmentation

A deep learning based active contour framework is proposed for pancreas segmentation. Data extension and fractional differential operation are firstly applied for pre-processing. Second, deep learning method is designed to acquire the initial contour of pancreas. Subsequently, an intensity constrained term is designed to stop the contours at the edges. The intensity constrained term is integrated into a variational active contour model with three terms. The accurate pancreas segmentation is obtained by the evolution of the active contour model. Our approach reaches high detection dice similarity coefficient (DSC) of 83% and sensitivity of 85% in a dataset containing 40 abdominal CT scans. Comparisons with other level set models provide evidence that the proposed method offers desirable performances.

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