Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model
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...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2021/1545098 |
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| author | Lumin Fan Lingli Shen Xinghua Zuo |
| author_facet | Lumin Fan Lingli Shen Xinghua Zuo |
| author_sort | Lumin Fan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c1d1274c9550431ea7ee398fa49f4e13 |
| institution | Kabale University |
| issn | 1687-9120 1687-9139 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Advances in Mathematical Physics |
| spelling | doaj-art-c1d1274c9550431ea7ee398fa49f4e132025-02-03T01:08:52ZengWileyAdvances in Mathematical Physics1687-91201687-91392021-01-01202110.1155/2021/15450981545098Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah ModelLumin Fan0Lingli Shen1Xinghua Zuo2Medical Equipment Department, Shanghai East Hospital, Shanghai 200120, ChinaMaterials Procurement Department, Shanghai East Hospital, Shanghai 200120, ChinaMedical Equipment Department, Shanghai East Hospital, Shanghai 200120, ChinaIn 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.http://dx.doi.org/10.1155/2021/1545098 |
| spellingShingle | Lumin Fan Lingli Shen Xinghua Zuo Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model Advances in Mathematical Physics |
| title | Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model |
| title_full | Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model |
| title_fullStr | Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model |
| title_full_unstemmed | Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model |
| title_short | Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model |
| title_sort | feature extraction and recognition of medical ct images based on mumford shah model |
| url | http://dx.doi.org/10.1155/2021/1545098 |
| work_keys_str_mv | AT luminfan featureextractionandrecognitionofmedicalctimagesbasedonmumfordshahmodel AT linglishen featureextractionandrecognitionofmedicalctimagesbasedonmumfordshahmodel AT xinghuazuo featureextractionandrecognitionofmedicalctimagesbasedonmumfordshahmodel |