Deep Learning in DXA Image Segmentation

Many existing techniques to acquire dual-energy X-ray absorptiometry(DXA)images are unable to accurately distinguish between bone and soft tissue.For the most part,this failure stems from bone shape variability,noise and low contrast in DXA images,inconsistent X-ray beam penetration producing shadowing effects,and person-to-person variations.This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models,fully convolutional networks(FCNs),SegNet,and U-Net to distinguish femur bone from soft tissue.We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques(i.e.,a decision-tree-based method using a pixel label decision tree[PLDT]and another method using Otsu’s thresholding)for femur DXA images,and we measured accuracy based on the average Jaccard index,sensitivity,and specificity.Deep learning models using SegNet,U-Net,and an FCN achieved average segmentation accuracies of 95.8%,95.1%,and 97.6%,respectively,compared to PLDT(91.4%)and Otsu’s thresholding(72.6%).Thus we conclude that an FCN outperforms other deep learning and conventional techniques when segmenting femur bone from soft tissue in DXA images.Accurate femur segmentation improves bone mineral density computation,which in turn enhances the diagnosing of osteoporosis.

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space.Examples of such objects include unstructured multimedia objects and points of interest(POIs)in a road network.The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space.Studies have been conducted on the bulk loading of large datasets in an M-tree.However,because previous algorithms involve excessive distance computations and disk accesses,they perform poorly in terms of their index construction and search capability.This study proposes two efficient M-tree bulk loading algorithms.Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve,thereby significantly improving the index construction and search performance.Our second algorithm is an extension of the first,and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance.Through the use of various synthetic and real-world datasets,the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.

Artificial Intelligence-Based Semantic Segmentation of Ocular Regions for Biometrics and Healthcare Applications

Multiple ocular region segmentation plays an important role in different applications such as biometrics,liveness detection,healthcare,and gaze estimation.Typically,segmentation techniques focus on a single region of the eye at a time.Despite the number of obvious advantages,very limited research has focused on multiple regions of the eye.Similarly,accurate segmentation of multiple eye regions is necessary in challenging scenarios involving blur,ghost effects low resolution,off-angles,and unusual glints.Currently,the available segmentation methods cannot address these constraints.In this paper,to address the accurate segmentation of multiple eye regions in unconstrainted scenarios,a lightweight outer residual encoder-decoder network suitable for various sensor images is proposed.The proposed method can determine the true boundaries of the eye regions from inferior-quality images using the high-frequency information flow from the outer residual encoder-decoder deep convolutional neural network(called ORED-Net).Moreover,the proposed ORED-Net model does not improve the performance based on the complexity,number of parameters or network depth.The proposed network is considerably lighter than previous state-of-theart models.Comprehensive experiments were performed,and optimal performance was achieved using SBVPI and UBIRIS.v2 datasets containing images of the eye region.The simulation results obtained using the proposed OREDNet,with the mean intersection over union score(mIoU)of 89.25 and 85.12 on the challenging SBVPI and UBIRIS.v2 datasets,respectively.