scholarly journals Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lumin Fan ◽  
Lingli Shen ◽  
Xinghua Zuo
Keyword(s):  
Image Segmentation ◽  
Active Contour Model ◽  
Convex Relaxation ◽  
Image Data ◽  
Ct Images ◽  
Image Feature ◽  
Phase Image ◽  
Discrete Problem ◽  
Dual Algorithm ◽  
Initialization Strategy

In this paper, we propose an improved algorithm based on the active contour model Mumford-Shah model for CT images, which is the subject of this study. After analyzing the classical Mumford-Shah model and related improvement algorithms, we found that most of the improvement algorithms start from the initialization strategy of the model and the minimum value solution of the energy generalization function, so we will also improve the classical Mumford-Shah model from these two perspectives. For the initialization strategy of the Mumford-Shah model, we propose to first reduce the dimensionality of the image data by the PCA principal component analysis method, and for the reduced image feature vector, we use K -means, a general clustering method, as the initial position algorithm of the segmentation curve. For the image data that have completed the above two preprocessing processes, we then use the Mumford-Shah model for image segmentation. The Mumford-Shah curve evolution model solves the image segmentation by finding the minimum of the energy generalization of its model to obtain the optimal result of image segmentation, so for solving the minimum of the Mumford-Shah model, we first optimize the discrete problem of the energy generalization of the model by the convex relaxation technique and then use the Chambolle-Pock pairwise algorithm We then use the Chambolle-Pock dual algorithm to solve the optimization problem of the model after convex relaxation and finally obtain the image segmentation results. Finally, a comparison with the existing model through many numerical experiments shows that the model proposed in this paper calculates the texture image segmentation with high accuracy and good edge retention. Although the work in this paper is aimed at two-phase image segmentation, it can be easily extended to multiphase segmentation problems.

scholarly journals A model for segmentation of CT images of liver lesions based on regional fitting and gradient information

2020 ◽  
Author(s):  
Baoshan Xue ◽  
Fengfeng Zhang ◽  
Xiaojian Yan ◽  
Rongmiao Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour Model ◽  
Local Area ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Liver Lesions ◽  
Gray Scale ◽  
Gradient Information ◽  
Segmentation Accuracy ◽  
Level Set Evolution

Abstract Background: The Computerized tomography (CT) images of liver have such disadvantages as uneven gray scale, fuzzy boundary and missing, so the commonly used image segmentation model of liver lesions has low segmentation accuracy. Methods: We propose a new hybrid active contour model based on regional fitting and gradient information for segmenting CT images of liver lesions. Firstly, the problem of uneven gray scale of liver lesions image was solved by local area fitting method, and the gradient information of liver lesions image was integrated to enhance the detection ability of the model on the edge of liver lesions. Secondly, we introduce the region area term, which can keep the image segmentation curve smooth in the process of segmentation, and effectively control the direction and speed of curve evolution. Finally, the performance of the Distance Regularized Level Set Evolution (DRLSE) model, Region-Scalable Fitting (RSF) model and the present model was compared in the segmentation of liver lesions. Results: It can be concluded from the experimental results that: compared with DRLSE model and RSF model，the average Dice similarity coefficient reached 97.7%, ncreased by 12.7% and 11.7% respectively; the under segmentation rate was 2%, 9% and 17% lower, and the over segmentation rate was 1.6%. Conclusion: Therefore, the segmentation model proposed in this paper has excellent segmentation performance and greatly improves the segmentation accuracy of liver lesions.

scholarly journals Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 410
Author(s):  
K. Gopi ◽  
J. Selvakumar
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Ct Images ◽  
Detection Accuracy ◽  
Analysis Tool ◽  
Contour Model

Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy. This paper proposes a model based on active contour using level set methods for segmentation of such lung CT images and focusing 3D lesion refinement. The features were determined by applying a multi-scale Gaussian filter. This proposed method is able to detect lung tumors in CT images with intensity, homogeneity and noise. The proposed method uses LIDC-IDRI dataset images to segment accurate 3D lesion of lung tumor CT images.  

scholarly journals Hybrid Methodology for Image Segmentation Based on Active Contour Module and Alpha-Shape Theory

2016 ◽  
Vol 10 (11) ◽  
pp. 30
Author(s):  
Mohammed Sabbih Hamoud Al-Tamimi
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Computational Method ◽  
X Rays ◽  
Alpha Shape ◽  
Contour Model ◽  
Objective Contour

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present study proposes the use of a hybrid image segmentation technique to acquire precise segmentation outcomes, while engaging “Alpha Shape (α-Shape)” in supposition to derive the original contour, followed by a refining process through engaging a conventional active contour model. Empirical results show high potential in the suggested computational method. Trials indicate that the primary contour is capable of being precisely set next to the objective contour and effectively have these objective contours extracted, devoid of any contour instigation. Some of the benefits associated with the novel hybrid contour include minimized cost of computation, enhanced anti-jamming capability, as well as enlarged utilization array of snake model.

scholarly journals A Model for Segmentation of CT Images of Liver Lesions Based on Regional Fitting and Gradient Information

2020 ◽  
Author(s):  
Baoshan Xue ◽  
Fengfeng Zhang ◽  
Xiaojian Yan ◽  
Rongmiao Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour Model ◽  
Local Area ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Liver Lesions ◽  
Gray Scale ◽  
Gradient Information ◽  
Fitting Method ◽  
Segmentation Accuracy

Abstract Background: The Computed Tomography (CT) images of liver have such disadvantages as uneven gray scale, fuzzy boundary and missing, so the commonly used image segmentation model of liver lesions has low segmentation accuracy.Methods: We propose a new hybrid active contour model based on regional fitting and gradient information for segmenting CT images of liver lesions. Firstly, the problem of uneven gray scale of liver lesions image was solved by local area fitting method, and the gradient information of liver lesions image was integrated to enhance the detection ability of the model on the edge of liver lesions. Secondly, we introduce the region area term, which can keep the image segmentation curve smooth in the process of segmentation, and effectively control the direction and speed of curve evolution. Finally, the performance of the DRLSE model, RSF model and the present model was compared in the segmentation of liver lesions. Results: It can be concluded from the experimental results that: compared with DRLSE model and RSF model，the average Dice similarity coefficient reached 97.7%, ncreased by 12.7% and 11.7% respectively; the under segmentation rate was 2%, 9% and 17% lower, and the over segmentation rate was 1.6%. Conclusion: Therefore, the segmentation model proposed in this paper has excellent segmentation performance and greatly improves the segmentation accuracy of liver lesions.

scholarly journals Active Contour Model with Adaptive weighted function for Robust Image Segmentation under Biased Conditions

2021 ◽  
pp. 114811
Author(s):  
Aditi Joshi ◽  
Mohammed Saquib Khan ◽  
Asim Niaz ◽  
Farhan Akram ◽  
Hyun Chul Song ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Function ◽  
Contour Model ◽  
Robust Image

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.

Sign in / Sign up

Export Citation Format

Share Document