Feature-based fuzzy connectedness segmentation of ultrasound images with an object completion step
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Abstract:
Medical ultrasound (US) image segmentation and quantification can be challenging due to signal dropouts, missing boundaries, and presence of speckle, which gives images of similar objects quite different appearance. Typically, purely intensity-based methods do not lead to a good segmentation of the structures of interest. Prior work has shown that local phase and feature asymmetry, derived from the monogenic signal, extract structural information from US images. This paper proposes a new US segmentation approach based on the fuzzy connectedness framework. The approach uses local phase and feature asymmetry to define a novel affinity function, which drives the segmentation algorithm, incorporates a shape-based object completion step, and regularises the result by mean curvature flow. To appreciate the accuracy and robustness of the methodology across clinical data of varying appearance and quality, a novel entropy-based quantitative image quality assessment of the different regions of interest is introduced. The new method is applied to 81 US images of the fetal arm acquired at multiple gestational ages, as a means to define a new automated image-based biomarker of fetal nutrition. Quantitative and qualitative evaluation shows that the segmentation method is comparable to manual delineations and robust across image qualities that are typical of clinical practice.Keywords:
Robustness
Feature (linguistics)
Segmentation-based object categorization
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Segmentation-based object categorization
Region growing
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Segmentation-based object categorization
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Segmentation-based object categorization
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Region segmentation is one of the most common methods of medical image segmentation. However, it still has some disadvantages in practice: (1) Threshold choice will result in a poor performance in the image segmentation if there is little difference in the gray of an image. (2) Region-based segmentation algorithm is usually uncertain in defining the edge between the object and the background. This paper implements an improved algorithm to overcome these problems. In the algorithm, the FCM (fuzzy C-means) clustering method is used to improve the accuracy of image segmentation according to its stability. And Roberts operator is also added to compensate the deficiency in edge detection. This medical image segmentation method is a combination of region segmentation and edge segmentation, which is based on OTSU threshold segmentation, fuzzy C-means clustering and Roberts operator. Experiments show that the improved segmentation algorithm has a better performance than those traditional algorithms in the effect.
Segmentation-based object categorization
Region growing
Range segmentation
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Segmentation-based object categorization
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Image segmentation is an important image analysis technique, the segmentation result of which is critical to determine the performance of high-level modules in image processing system. After a brief introduction LLT image segmentation algorithm, focusing on analysis of the inadequacies of LLT algorithm, propose an improved ILLT image segmentation algorithm. Experiments show that the algorithm improves the efficiency of the image region segmentation.
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An image segmentation algorithm based on ensemble learning is proposed.First,several different image segmentation algorithms are used to produce many intermediate segmentation results.Then the intermediate image segmentation results are integrated with ensemble learning.Finally,the integrated output is adopted to image segmentation.The thresholding segmentation method,region growing segmentation method and FCM segmentation method are used in the experiments.Experimental results show that the quality of the proposed image segmentation algorithm based on ensemble learning technology significantly outperforms the best individual member.And the proposed method can be a good solution to incomplete image segmentation problem.At the same time,the performance of the proposed method is often more robust than a single algorithm.
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