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This paper presents an active communication mechanism based on SNS user behavior (e.g., likes, shares, replies) in e-learning. For this, we propose a novel automatic link generation method by considering users' knowledge and interests during the conversation on SNS. The method generates two kinds of links to promote user communication in e-learning, 1) knowledge support for receivers who receive posts...
Glioma is one of the most common brain tumors with high mortality and its histological grading and typing is important both in therapeutic decision and prognosis evaluation. This paper aims at using the high-throughput image feature analysis method to estimate the histological grade and type of a patient by using Magnetic Resonance Imaging (MRI) instead of histological examination. The proposed method...
This paper presents TweeVist, a geo-tweet visualization system to support users grasp event happens over time and space from tweets while they browse any web pages based on spatio-temporal analysis. TweeVist presents a tag cloud of tweets in different time periods are associated with web pages based on detected events. In order to detect events, the system extracts normal events (e.g., crowded restaurants,...
Microblogging is a new form of blogging in which microbloggers can share their status in short posts. Microbloggers can be kept up to date with current information from microblog senders, however, Web users cannot be simultaneously updated by microblogs, to obtain the most recent information, whilst they browse Web pages since these are not updated in real time. To circumvent this time lag and provide...
Nowadays, 3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, it is cumbersome and time-consuming, even for an experienced clinician, to manually locate the fetal head and the mid-sagittal plane. In this paper, we introduce a totally automatic method for fetal head detection, which is based on a shape model and the marginal space learning framework. We approximate...
Complex network spectra features are proposed to be used by the classifier to classify atrial fibrillation (AF) and normal sinus rhythm (NSR). This novel complex network construction method utilizes the fuzzy symbolic dynamics (FSD) and recurrence complex network to analyze the synchronization of cardiac electrical activity. Firstly, the multi-lead epicardial signals recorded from dogs are transformed...
A textural feature extraction algorithm was proposed to automatically find candidate objects in the selected volume of interest (VOI) and compute textural features on multiplanar images for classification of the mesh and fascia. Firstly, candidate objects were found out in axial plane (A-plane) and coronal plane (C-plane) images with the preprocessing stage. Secondly, textural features of candidate...
A Webber local binary pattern (LBP) descriptor (WLBP) is proposed to improve the classification performance between chronic pancreatitis and autoimmune pancreatitis from endoscopic ultrasound images. First, the differential excitation map and orientation map are extracted according to Webber local descriptor from each image. Then an orientation weight correction mechanism based on the orientation...
Textural features were extracted from livers' B-mode ultrasonic images to diagnose hepatic fibrosis for chronic hepatitis-B patients (CHB). For a selected region of interest (ROI) of an ultrasonic image, 13 textural parameters based on its gray level co-occurrence matrix (GLCM) were calculated. A Fisher linear classifier was built using 83 images as its training set. The results of the test on 38...
We propose a local descriptor referred to as triangle chain code as well as a matching algorithm for point set matching and image registration. First, feature points are detected using Harris corner detector. Second, triangle chain code is constructed for every point, which carries the discriminative information regarding its k nearest neighbors (KNN). Third, the KNN neighborhoods of two points are...
To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
We present a method of automatically generating learning channels by using the semantic relations that implicitly exist in slides of a lecture that has accompanying recorded video. These days, many lecture videos with presentation files are shared over the Web from many universities through their own public sites. Although these materials are useful and valuable to many potential students, their use...
Endothelial permeability is associated with the genesis and development of atherosclerosis. Computerized image analysis is utilized to investigate the relationship between endothelial permeability and endothelial morphology. First, microscopic images are segmented to detect endothelial cells using the speckle reduction anisotropic diffusion and marker-controlled watershed, whose optimal parameter...
Segmentation of ultrasonic breast tumor images is a challenging topic in the clinical practice. A novel coarse-to-fine active contour (CFAC) model is proposed to extract boundaries of breast tumors based on a level-set framework. To apply the CFAC model, a Gaussian pyramid is firstly constructed to represent images at different resolution levels. Then, on the top pyramid level a region-based segmentation...
A computerized classification based on morphologic and texture features is proposed to increase the accuracy of the ultrasonic diagnosis of breast tumors. Firstly, tumor boundaries are obtained with the gray-level threshold segmentation algorithm and the dynamic programming method. Then five morphologic features and two texture features are extracted. Finally, an artificial neural network with the...
The accurate boundary extraction is an essential preprocessing step for computerized analysis of a breast ultrasonic image. In this study, a novel approach based on the wavelet transform and the dynamic programming is proposed to extract tumor boundaries from breast ultrasonic images. Firstly, a rectangular region-of-interest (ROI) is manually selected from the ultrasonic image, followed by the ROI-based...
The accurate boundary extraction is an essential preprocessing step for computerized analysis of a breast ultrasonic image. In this study, a novel approach based on the wavelet transform and the dynamic programming is proposed to extract tumor boundaries from breast ultrasonic images. Firstly, a rectangular region-of-interest (ROI) is manually selected from the ultrasonic image, followed by the ROI-based...
The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images...
Feature extraction techniques in the ultrasonic images of a liver were studied firstly. As an initial result, total 25 parameters relating to cirrhosis were extracted. They were obtained from the motion curve of the liver in an M-mode image, and from the texture using a B-mode image. Then the efficiency of every parameter was analyzed, and with the use of a feature fusion method a set of 20 useful...
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