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.

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 Automated mapping of local layer slope and tracing of internal layers in radio echograms

2014 ◽  
Vol 55 (67) ◽  
pp. 71-77 ◽  
Author(s):  
Christian Panton
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Ice Cores ◽  
Internal Layers ◽  
Automated Mapping ◽  
Iterative Optimization ◽  
Automated Method ◽  
Contour Model ◽  
Intensity Response ◽  
Radio Echo Sounding

AbstractAn automated method is presented for tracing layers in radio echograms. The method is designed to work with most radio-echo sounding echograms and has been successfully tested with a 180–210 MHz multichannel coherent depth sounder. To accurately trace layers, first approximate layer positions are calculated by integrating the local layer slope which is inferred by the intensity response to a slanted filter, then the positions are refined using an iterative optimization. The layers are traced using an active contour model or snake, which can be constrained to conserve both echogram features and smooth layers. With this technique it is possible to trace internal layers over distances of several hundred kilometers. The method was tested between two Greenland deep ice cores where the age–depth relation is known.

scholarly journals A Probabilistic Approach for Breast Boundary Extraction in Mammograms

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Hamed Habibi Aghdam ◽  
Domenec Puig ◽  
Agusti Solanas
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Probability Model ◽  
Probabilistic Approach ◽  
Local Binary Patterns ◽  
Extraction Process ◽  
Morphological Operations ◽  
Boundary Extraction ◽  
Contour Model ◽  
Starting Point

The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

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>

scholarly journals Bone fracture detection using active contour model with prior shape

2021 ◽  
Author(s):  
Yun Jia
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Morphological Operations ◽  
Segmentation Method ◽  
Fracture Detection ◽  
Cast Material ◽  
X Ray ◽  
Detection Tool ◽  
Contour Model ◽  
Prior Shape

In this research, an image segmentation method based on active contouring model was studied, which incorporates the prior shape into the active contour evolving process as the global constraint. The active contour model is implemented based on the level set method. The prior shape regulates the behavior of the active contour and keeps it from leaking out of the weak edges. The goal of this research is to determine the displacement and alignment between two fractured pieces of a bone which is encased in the cast material by segmenting them out and calculating their axes difference. The noise introduced by the cast material makes this task difficult. Morphological operations of dilation and erosion are deployed in this research as the noise reduction and edge detection tool. Experiment results are obtained successfully by applying this method upon the X-ray images of patients' fractured arm.

scholarly journals The multichannel integration active contour framework for crack detection

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985285
Author(s):  
Xiaomin Xie ◽  
Yilin Xia ◽  
Bo Liu ◽  
Kui Li ◽  
Tingting Wang
Keyword(s):  
Active Contour ◽  
Crack Detection ◽  
Active Contour Model ◽  
Morphological Operations ◽  
Salient Region Detection ◽  
Nonlocal Means ◽  
Feature Map ◽  
Region Detection ◽  
Contour Model ◽  
Infrastructure Maintenance

Crack is one of the most important defects to evaluate the health of concrete buildings. Hence, accurate detection is of great significance for the infrastructure maintenance. In this article, an efficient multichannel active contour model for crack extraction is proposed, which integrates various features of the cracks. Firstly, the nonlocal means technique is adopted to eliminate the effects of noise while preserving the edge details. Then, the novel multichannel active contour model energy function is constructed, which considers three characteristics of the cracks: (a) the intensity features map, which is on the basis of the distinct intensity of the cracks; (b) the saliency feature map, which is obtained by the frequency-tuned salient region detection; and (c) the line-like feature map, which is enhanced by the multi-scale Hessian filtering. Also, the line-like feature map is binarized by a set of morphological operations and the Otsu thresholding to initialize the active contour. The proposed approach has been compared with the existing detection models on the public database and the real-world cracks. The experimental results show the effectiveness and efficiency of the proposed model.

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.

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