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Automated segmentation of medical ultrasound (US) images is a challenging problem due to the complicated features of lesions, inconsistent lesions across individuals, and the high segmentation accuracy requirement. From recently published papers in this area, the active contour model (ACM) and machine learning method produce more accurate lesion se...
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p class="LO-normal"> Red blood cells (RBCs), or erythrocytes, are usually disc-shaped. However, pathological conditions can change their shape. The complete blood count (CBC) is a test that can detect abnormal RBCs. Yet it is a manual test susceptible to errors, so there are efforts to automate the detection and classification of abnormal cells. A...
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... Image segmentation is a widely used technique in digital image processing, the essence is to segment an image into several areas with similar properties through the information from image features, such as gray and texture [1,2]. Image segmentation is widely used in real life [3][4][5][6][7]. For example, face recognition, localization of objects in satellite images, and traffic B Zhiheng Zhou zhouzh@scut.edu.cn 1 control systems are all closely related to image segmentation algorithms [8][9][10][11]. ...
In order to better handle images with intensity inhomogeneity and noise, an adaptive multi-level set active contour model based on block search is proposed in this paper. This model first defines a multiple edge extension criterion for the input image to block the image and avoid the loss of image edge information; Then, the proposed adaptive block search method is used to find the level set that is considered redundant, and the remaining parts are fused to obtain a rough binary mask; Finally, the target is extracted by using the newly defined energy functional and the edge contours of the extracted binary mask. The experimental results show that the average jaccard similarity coefficients of the proposed model for segmenting images with intensity inhomogeneity and real images are 97.81% and 98.41%, respectively, and the accuracy of the segmentation results is higher than that of other models participating in the comparison. Similarly, the results of the ablation experiment once again validated the robustness of the proposed model.
Intensity inhomogeneity is an intractable issue that leads to significant challenges for image segmentation. Moreover, inhomogeneous images are very common in real life, especially the medical images generated by medical instruments. Most existing active contour models yield poor performance when they are applied to segment these images. Thus, a new image segmentation model is developed in this paper by integrating the squared Hellinger distance and multiple truncation functions that are defined by local correlation features into the level set framework. As a result, the problems of intensity inhomogeneity and dark edges can be effectively solved. Specifically, the Hellinger distance is first defined as a fitting method of the energy function and has better performance in splitting the boundary of dark areas in inhomogeneous images. More importantly, the proposed model constructs local correlation features (LCFs) to redefine the scale of the local areas and makes the division of the local information more reasonable, which effectively enhances the accuracy of inhomogeneous images. Then, to combine the global and local information of the image, an adaptive combination coefficient function defined by the evolution process is applied in our model; moreover, the length term combined with the saliency feature of the image achieves a remarkable improvement effect. Finally, all terms are integrated into a variational level set framework, and our new image segmentation model is proposed. The experimental results on medical and natural inhomogeneous images demonstrate the excellent performance of our model over most state-of-the-art active contour models in terms of accuracy and robustness.
Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively.
Paracentesis is a high-demanding and routine operation, which has great potentials and benefits if semi-autonomous procedures can be developed. One of the most important techniques that facilitate semi-autonomous paracentesis is to segment the ascites from ultrasound images accurately and efficiently. The ascites, however, is usually with significantly different shapes and noise among different patients, and its shape/size changes dynamically during the paracentesis. This makes most of existing image segmentation methods either time consuming or inaccurate for segmenting ascites from its background. In this paper, we propose a two-stage active contour method to facilitate accurate and efficient segmentation of ascites. First, a morphological-driven thresholding method is developed to locate the initial contour of the ascites automatically. Then, the identified initial contour is fed into a novel sequential active contour algorithm to segment the ascites from background accurately. The proposed method is tested and compared with state-of-the-art active contour methods on over 100 real ultrasound images of ascites, and the results show the superiority of our method in both accuracy and time efficiency.
