JOURNAL ARTICLE

A robust level set framework for medical image segmentation

Abstract

In this paper, a new speed function of level set framework is presented. The region information, instead of the image gradient information, is fused into the level set fundamental model to improve the robustness of the segmentation for medical images. This new speed function is particularly well adapted to situations where edges are weak and overlap. A number of experiments on ultrasound (US), CT, MR and X-ray modalities medical images were performed to evaluate the new method. The experimental results show the proposed method is effective and robust.

Keywords:
Robustness (evolution) Image segmentation Level set (data structures) Computer science Segmentation Artificial intelligence Computer vision Medical imaging Image (mathematics) Set (abstract data type) Scale-space segmentation Function (biology) Level set method Segmentation-based object categorization Pattern recognition (psychology)

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Topics

Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Object Detection Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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