A segmentation system based on clustering method for pediatric DTI images

Abstract

A system is presented for the segmentation of white matter fiber tracts in pediatric diffusion tensor magnetic resonance imaging (DTI) images. DTI is an in vivo method to delineate the connectivity of white matter fiber tracts in human brain by fiber tractography. Fiber tractography is a promising method to visualize the whole bundles of fiber tracts. Fiber tractography is unable to provide a quantitative analysis and description of specific white matter fiber tracts. Obviously, segmenting and clustering the fiber tracts into anatomical bundles play an important role in fiber tracts analysis. Traditional manual segmentation method requires neuroanatomical expertise and significant time. It can not be a standardized and widely used method for segmentation of complicated fiber tracts in pediatric DTI images. Hence, an image segmentation system with an adaptive mean shift (AMS) clustering method is proposed to cluster fiber tracts into bundles automatically in this article. In the image segmentation system, fiber similarity measure based on Euclidean distance is used in the clustering method. Since the increase of children's mental illness in recent years, segmentation of pediatric DTI images by clustering methods is focused in our research. The effectiveness and robustness of adaptive mean shift clustering algorithm for segmentation of fiber tracts are also evaluated by error analysis experiments. In addition, the experiment results show that adaptive mean shift method used in our system is more efficient and effective than K-means and Fuzzy Cmeans (FCM) clustering methods for the segmentation of fiber tracts in real pediatric DTI images.

BCUIS‐Net: A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion

“…3,4 Among these, ultrasound imaging is an indispensable tool for early detection of breast cancer because of its costeffectiveness, radiation-free imaging procedure, and high sensitivity in dense breast tissue. 5 However, ultrasound scans are significantly influenced by individual patient variability and physician expertise. In addition, the large volume of examinations places a heavy workload on the physician.…”

“…In clinical practice, breast cancer diagnosis mainly includes X‐ray, breast ultrasound, and MRI 3,4 . Among these, ultrasound imaging is an indispensable tool for early detection of breast cancer because of its cost‐effectiveness, radiation‐free imaging procedure, and high sensitivity in dense breast tissue 5 . However, ultrasound scans are significantly influenced by individual patient variability and physician expertise.…”