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 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.

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

A New Image Segmentation Model

2014 ◽  
Vol 519-520 ◽  
pp. 541-547
Author(s):  
Chao Liu ◽  
Jing Liu ◽  
Lu Lu Zhang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Weighted Average ◽  
Mean Value ◽  
Local Information ◽  
Experimental Result ◽  
New Model ◽  
Contour Model ◽  
Segmentation Accuracy

To build a new image segmentation model based on level set theory : Add edge detection operator to edgeless active contour model to detect local information; introduce adaptive coefficient of area item to let the model autonomously adjust and evolve curve position according to image information; adopt weighted average gray value to replace traditional absolute mean value to reduce error and improve segmentation result. Experimental result comparison shows that the new model can detect global information and local information at the same time, adaptively adjust curve evolution direction, and has a fast segmentation speed. Compared to edgeless active contour model, the new model is a more effective image segmentation method as it has greater advantages in image segmentation accuracy and computational complexity.

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 New Active Contour Medical Image Segmentation Method Based on Fractional Varying-Order Differential

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 206
Author(s):  
Yanshan Zhang ◽  
Yuru Tian
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Similarity Coefficient ◽  
Medical Image Segmentation ◽  
Contour Length ◽  
Dice Similarity Coefficient ◽  
Penalty Term ◽  
P Rate ◽  
Local Term

Image segmentation technology is dedicated to the segmentation of intensity inhomogeneous at present. In this paper, we propose a new method that incorporates fractional varying-order differential and local fitting energy to construct a new variational level set active contour model. The energy functions in this paper mainly include three parts: the local term, the regular term and the penalty term. The local term combined with fractional varying-order differential can obtain more details of the image. The regular term is used to regularize the image contour length. The penalty term is used to keep the evolution curve smooth. True positive (TP) rate, false positive (FP) rate, precision (P) rate, Jaccard similarity coefficient (JSC), and Dice similarity coefficient (DSC) are employed as the comparative measures for the segmentation results. Experimental results for both synthetic and real images show that our method has more accurate segmentation results than other models, and it is robust to intensity inhomogeneous or noises.

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 A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Chaolu Feng ◽  
Jinzhu Yang ◽  
Chunhui Lou ◽  
Wei Li ◽  
Kun Yu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Bias Correction ◽  
Active Contour Model ◽  
Multiple Objects ◽  
Level Set Function ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Term Energy ◽  
Set Function

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories.

scholarly journals Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xu Fu ◽  
Huaiqin Liu ◽  
Xiaowang Bi ◽  
Xiao Gong
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Kidney Diseases ◽  
Left Kidney ◽  
Test Group ◽  
Renal Cysts ◽  
Ct Images ◽  
Ct Imaging ◽  
Dice Similarity Coefficient ◽  
Chronic Kidney Diseases

This study focused on the application of deep learning algorithms in the segmentation of CT images, so as to diagnose chronic kidney diseases accurately and quantitatively. First, the residual dual-attention module (RDA module) was used for automatic segmentation of renal cysts in CT images. 79 patients with renal cysts were selected as research subjects, of whom 27 cases were defined as the test group and 52 cases were defined as the training group. The segmentation results of the test group were evaluated factoring into the Dice similarity coefficient (DSC), precision, and recall. The experimental results showed that the loss function value of the RDA-UNET model rapidly decayed and converged, and the segmentation results of the model in the study were roughly the same as those of manual labeling, indicating that the model had high accuracy in image segmentation, and the contour of the kidney can be segmented accurately. Next, the RDA-UNET model achieved 96.25% DSC, 96.34% precision, and 96.88% recall for the left kidney and 94.22% DSC, 95.34% precision, and 94.61% recall for the right kidney, which were better than other algorithms. The results showed that the algorithm model in this study was superior to other algorithms in each evaluation index. It explained the advantages of this model compared with other algorithm models. In conclusion, the RDA-UNET model can effectively improve the accuracy of CT image segmentation, and it is worth of promotion in the quantitative assessment of chronic kidney diseases through CT imaging.

scholarly journals Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

2020 ◽  
Author(s):  
Wen Chen ◽  
Yimin Li ◽  
Brandon A Dyer ◽  
Xue Feng ◽  
Shyam Rao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Qualitative Difference ◽  
Masticatory Muscles ◽  
Ct Images ◽  
Mean Value ◽  
Dice Similarity Coefficient ◽  
Segmentation Accuracy ◽  
The Mean ◽  
Deep Learning Model

Abstract Background: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. Material and methods: Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n=27) and validation (n=29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.Results: DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83±0.03 to 0.89±0.02, the ABAS mean DSC ranged from 0.79±0.05 to 0.85±0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P >0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS. Conclusions: DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

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