Active contour evolved by joint probability classification on Riemannian manifold. In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. ![]() The experimental results on medical and texture images demonstrate the advantages of the proposed method. Ultrasound image- based thyroid nodule automatic segmentation using convolutional neural networks.Purpose. Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases.However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background.In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Adobe Acrobat Was Installed As Part Of A Suite Error 404 on this page. Skip to navigation Open Source Graphics Tips and tricks for creating graphics and retouching images with open-source software. Home; Return to Content. Type or paste a DOI name into the text box. Click Go. Your browser will take you to a Web page (URL) associated with that DOI name. Send questions or comments to doi. IJSTR is an open access quality publication of peer reviewed and refereed international journals. IJSTR calls for research papers. Methods. Our CNN- based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi- view strategy is used to improve the performance of the CNN- based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. Results. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN- based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN- based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as \(0. Conclusion. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time- consuming and tedious manual segmentation approach, demonstrating the potential clinical applications. Keywords. Thyroid nodule Ultrasound image Convolutional neural network Segmentation.
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