A Substructure Segmentation Method of Left Heart Regions from Cardiac CT Images Using Local Mesh Descriptors, Context and Spatial Location Information

Zhang, Yaonan; Niu, Lechuan

doi:10.1134/s105466181902010x

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Textures in natural images are often highly complex, random, and irregular, so simply considering the inclusion of color and texture features cannot achieve accurate results [16]. The Gabor filter-based model requires the selection of appropriate image channels to obtain the desired results.…”

Section: Current Status Of Researchmentioning

confidence: 99%

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

Shen

Zuo

2021

Advances in Mathematical Physics

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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.

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Section: Current Status Of Researchmentioning

confidence: 99%

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

Shen

Zuo

2021

Advances in Mathematical Physics

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“…The approach to performing semantic segmentation tasks seems too broad and out of the central theme of this thesis' scope, so we would only focus on reviewing those highly related techniques. Semantic segmentation aims to divide an image into regions corresponding to different objects on the pixel level [9].…”

Section: Binary Segmentation and Mattingmentioning

confidence: 99%