WebJul 27, 2024 · Segmenting bone from background is required to quantify bone architecture in computed tomography (CT) image data. A deep learning approach using convolutional neural networks (CNN) is a promising alternative method for automatic segmentation. The study objectives were to evaluate the performance of CNNs in automatic segmentation … WebJan 2, 2024 · Region-based. Edge detection. Clustering-based segmentation. Of course, this is not an exhaustive list (namely, graph-based segmentation is widely used too), yet it gives a basic understanding of ...
Image Segmentation - Washington State University
WebThe most used techniques are region-based segmentation, ... and the edge-based segmentation, which searches for margins between regions with different characteristics ... Femoral anatomical axis is the axis between two points of the femoral shaft. There is no agreement in the literature on which items should be referenced. WebAug 27, 2007 · Segmentation of femurs in Anterior-Posterior x-ray images is very important for fracture detection, computer-aided surgery and surgical planning. Existing methods do not perform well in segmenting bones in x-ray images due to the presence of large amount of spurious edges. dorina čokolada s napolitankama
Fully automated, level set-based segmentation for knee …
WebMay 25, 2024 · SEMI-AUTOMATED 3D SEGMENTATION OF PELVIC REGION BONES IN CT VOLUMES FOR THE ANNOTATION OF MACHINE LEARNING DATASETS Radiation Protection Dosimetry Oxford Academic Abstract. Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction … WebJul 1, 2024 · This research aims to apply the localizing region-based active contour (LRAC) method to acquire the femur length in an ultrasound image automatically and to determine the effect of noise removal on the segmentation accuracy. The automatic femur length measurement system includes three main steps. WebMar 15, 2024 · For the main femur segmentation network, the fundamental structure was an encoder–decoder with two 2 × 2 pooling layers and two up-sample deconvolutions. We stacked two encoder–decoder structures and densely concatenated all corresponding layers, thus combining the advantages of U-Net and Dense-Net [ 15 ]. rac848