scholarly journals An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuntao Wei ◽  
Xiaojuan Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Segmentation Algorithm ◽  
Initial Contour ◽  
Ct Image ◽  
Image Edge ◽  
Growth Graph ◽  
Region Growth ◽  
Image Segmentation Algorithm

The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

An Intelligent Image Segmentation Algorithm for Medical Ultrasonic Instruments

2009 ◽  
Vol 419-420 ◽  
pp. 701-704
Author(s):  
Guo Chang Gu ◽  
Chang Ming Zhu ◽  
Hai Bo Liu ◽  
Sheng Jing ◽  
Hua Long Yu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Relevance Vector Machine ◽  
Initial Contour ◽  
Ultrasound Images ◽  
Qualitative And Quantitative Analysis ◽  
Segmentation Method ◽  
Ultrasonic Images ◽  
Image Segmentation Algorithm

In company with medical instruments ' development, the corresponding software plays more and more role in the application. And medical image processing software has become an important component of the medical ultrasonic instruments. Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. But state-of-arts methods in the aspect of segmentation can not get satisfactory results. We propose an intelligent image segmentation method for medical ultrasonic images. The algorithm improved active contour model with relevance vector machine, where the advantages of supervised learning classification and the global region distribution information can be exploited to enhance the performance. In order to improve the segmentation speed and get precise initial contour, relevance vector machine also is used to obtain initial contour firstly. A large amount of experimental results have proved that our method outperforms many state-of-arts methods in the aspect of segmentation, and the method can be used in ultrasonic instruments effectively.

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.

Lung CT Image Segmentation Based on Mixture Active Contour Model

2014 ◽  
Vol 618 ◽  
pp. 405-409 ◽  
Author(s):  
Ke Qin Tao ◽  
Zhe Qu ◽  
Dong Dong Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Learning Algorithm ◽  
Active Contour Model ◽  
Gaussian Mixture ◽  
Ct Image ◽  
Penalty Term ◽  
Level Set Function ◽  
Contour Model ◽  
Lung Ct

In order to solve the difficult problem in lung CT image segmentation, the segmentation method based on Mixture Active Contour Model is proposed and the learning algorithm is presented. It gets the prior information of lung CT image segmentation through Gaussian Mixture Model, couples the penalty term and edge detection of the level set function. Experimental results illustrate the effectiveness of the method based on MACM in solving lung CT image segmentation.

scholarly journals An active contour model for medical image segmentation with application to brain CT image

2013 ◽  
Vol 40 (2) ◽  
pp. 021911 ◽  
Author(s):  
Xiaohua Qian ◽  
Jiahui Wang ◽  
Shuxu Guo ◽  
Qiang Li
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Medical Image Segmentation ◽  
Ct Image ◽  
Brain Ct ◽  
Contour Model

Improved Algorithm Based on Level Set Image Segmentation Methods

2014 ◽  
Vol 687-691 ◽  
pp. 4128-4131
Author(s):  
Hong Wei Han
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Initial Contour ◽  
Segmentation Result ◽  
Level Set Function ◽  
Segmentation Methods ◽  
Set Function ◽  
Image Segmentation Algorithm

Image segmentation is one of the most fundamental and important areas in the field of image processing and computer vision. The traditional level set methods need initialize the level set function as a distance function. If the initial contour is selected inappropriate, we may not get the desired ideal segmentation result. In order to solve the problem of level set automation initial, we proposed a new image segmentation algorithm based on level set and marker extraction. First, we extract the internal mark as level set initial curve by using Extended-minima transform. And then, through using the local binary fitting active contour model, we evolve the labeled image to get the final segmentation result. The simulation results show that this method has low computing complexity than the traditional level set method requirements, and can effectively solve the initialization problem of level set.

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