Showing papers by "Heng-Da Cheng published in 2014"

Usefulness of the heart-rate variability complex for predicting cardiac mortality after acute myocardial infarction.

Abstract: Previous studies indicate that decreased heart-rate variability (HRV) is related to the risk of death in patients after acute myocardial infarction (AMI). However, the conventional indices of HRV have poor predictive value for mortality. Our aim was to develop novel predictive models based on support vector machine (SVM) to study the integrated features of HRV for improving risk stratification after AMI. A series of heart-rate dynamic parameters from 208 patients were analyzed after a mean follow-up time of 28 months. Patient electrocardiographic data were classified as either survivals or cardiac deaths. SVM models were established based on different combinations of heart-rate dynamic variables and compared to left ventricular ejection fraction (LVEF), standard deviation of normal-to-normal intervals (SDNN) and deceleration capacity (DC) of heart rate. We tested the accuracy of predictors by assessing the area under the receiver-operator characteristics curve (AUC). We evaluated a SVM algorithm that integrated various electrocardiographic features based on three models: (A) HRV complex; (B) 6 dimension vector; and (C) 8 dimension vector. Mean AUC of HRV complex was 0.8902, 0.8880 for 6 dimension vector and 0.8579 for 8 dimension vector, compared with 0.7424 for LVEF, 0.7932 for SDNN and 0.7399 for DC. HRV complex yielded the largest AUC and is the best classifier for predicting cardiac death after AMI.

A Fully Automatic Breast Ultrasound Image Segmentation Approach Based on Neutro-Connectedness

Abstract: Breast tumor segmentation is an important step of breast ultrasound (BUS) computer-aided diagnosis (CAD) systems. However, because of the poor quality of BUS images, it's a challenging task to develop a robust and accurate segmentation algorithm. Much progress has been made on applying fuzzy connectedness to segment objects from low quality images. However, the fuzzy connectedness method still has difficulty in segmenting objects with weak boundaries. The neutrosophic set theory has been widely applied to image processing, and shows more strengths in modeling uncertainty and indeterminacy. In this paper, two new concepts of neutrosophic subset and neutrosophic connectedness (neutro-connectedness) were defined to generalize the fuzzy subset and fuzzy connectedness. The newly proposed neutro-connectedness models the inherent uncertainty and indeterminacy of the spatial topological properties of the image. The proposed method is applied to a breast ultrasound database with 131 cases, and its performance is evaluated by similarity ratio (SIR), false positive ratio (FPR) and average Hausdroff error (AHE). In comparison with the fuzzy connectedness segmentation method, the proposed method is more accurate and robust in segmenting tumors in BUS images.

Multiple-instance learning with global and local features for thyroid ultrasound image classification

Abstract: Multi-modality thyroid ultrasound image can provide more information about the lesion for the physician to diagnosis. In this paper, the thyroid B-mode ultrasound image and the elastogrom are viewed as a bag. And the local features of the B-mode image and the global features of the elastogram are considered as instances of the bag. Multiple-instance learning (MIL) method is employed to solve thyroid ultrasound image classification problem. Local features of B-mode are mapped to the concept space by self-organizing map (SOM). The hue component of elastogram is extracted to represent the elasticity information of the lesion. The bag vector is composed of the concept vector of the B-mode and global elasticity of elastogram. Finally, a traditional supervised learning method, support vector machine (SVM), is employed for classifying the lesion. The experimental results show that the proposed method can achieve better performance.

A Computer-Aided Diagnostic Algorithm Improves the Accuracy of Transesophageal Echocardiography for Left Atrial Thrombi A Single-Center Prospective Study

Abstract: OBJECTIVES We investigated whether transesophageal echocardiography (TEE) assisted with a computer-aided diagnostic (CAD) algorithm was superior to TEE in diagnosing left atrial (LA)/left atrial appendage (LAA) thrombi in patients with atrial fibrillation (AF) in a single prospective study. METHODS Transesophageal echocardiography was performed in patients with AF, and images were reconstructed. Gray level co-occurrence matrix-based features were calculated and then classified using an artificial neural network. The original data and processed images by the CAD system were studied by 5 radiologists independently in a blind manner. The diagnostic performance of each radiologist was evaluated. RESULTS One hundred thirty patients with AF were investigated. Thirty-one patients (23.9%) had a diagnosis of LA/LAA thrombi. The mean sensitivity ± SD of TEE for LA/LAA thrombi was 0.933 ± 0.027, which was noticeably improved by CAD (0.955 ± 0.021; P < .05). The specificity of TEE was 0.811 ± 0.055, which was markedly lower than that by TEE plus CAD (0.970 ± 0.009; P < .05). The positive predictive value of TEE was low (0.613 ± 0.073) compared to that of TEE plus CAD (0.908 ± 0.027; P < .001), whereas the negative predictive values were comparable for TEE, CAD, and TEE plus CAD. Diagnosis of an LA/LAA thrombus by TEE plus CAD had a higher accuracy rate (0.966 ± 0.011) than that by TEE (0.840 ± 0.047; P < .01). The mean area under the receiver operating characteristic curve (Az) for TEE was 0.834 ± 0.009 (95% confidence interval [CI], 0.815-0.852), which was markedly lower than the Az for TEE plus CAD (0.932 ± 0.005; 95% CI, 0.921-0.943). The use of CAD significantly improved the Az values for all 5 radiologists (P < .001). CONCLUSIONS The CAD algorithm significantly improves the diagnostic accuracy of TEE for LA/LAA thrombi in patients with AF.

Multi-Object Tracking and Occlusion Reasoning Based on Adaptive Weighing Particle Filter

Abstract: Multi-object tracking has significant merit to our society. However, interactions among objects result in complex spatial occlusions, which gives rise to a challenging problem in tracking. We propose an adaptive weighing particle filter (AWPT) for tracking multiple objects and reasoning the occlusions among them. A weighing-occlusion modeling-weighing procedure is developed to adaptively weigh the particles. Moreover, we propose a stack occlusion model and define the operations on it to maintain the occlusion relationship. The experiments exhibit that the proposed method can effectively track fully occluded objects and reason about the occlusion relationships among them.

Scale robust head pose estimation based on relative homography transformation

Abstract: Head pose estimation has been widely studied in recent decades due to many significant applications. Different from most of the current methods which utilize face models to estimate head position, we develop a relative homography transformation based algorithm which is robust to the large scale change of the head. In the proposed method, salient Harris corners are detected on a face, and local binary pattern features are extracted around each of the corners. And then, relative homography transformation is calculated by using RANSAC optimization algorithm, which applies homography to a region of interest (ROI) on an image and calculates the transformation of a planar object moving in the scene relative to a virtual camera. By doing so, the face center initialized in the first frame will be tracked frame by frame. Meanwhile, a head shoulder model based Chamfer matching method is proposed to estimate the head centroid. With the face center and the detected head centroid, the head pose is estimated. The experiments show the effectiveness and robustness of the proposed algorithm.

Multiplayer tracking system for short track speed skating

Abstract: In this study, the authors propose a novel and effective short track speed skating tracking system. Aimed at several challenging tracking problems of short track skating: long-time occlusion and complex group situations, size variations, similar or identical uniforms, fast motion speed, quick orientation changes etc.; a novel fuzzy model is proposed to initialise contours of skaters even they wear the same kind of uniform. Then, a novel approach for multiple object tracking is developed to track skaters reliably. The experimental results demonstrate that the proposed system can solve the above challenging problems very effectively. The information provided by the proposed system, including trajectories, velocity analysis and two-dimensional reconstruction animations, is valuable to broadcasters, athletes and coaches alike. The proposed tracking algorithm can obtain better results than that by the other eight state-of-the-art trackers in short track speed skating tracking.

Adaptive reversible watermarking using trimmed prediction and pixel-selection-based sorting

Abstract: Prediction error expansion based on sorting is an important technique in reversible watermarking since it yields large embedding capacity and low distortion. In this paper, an efficient and adaptive reversible watermarking scheme is proposed based on trimmed prediction and pixel selection sorting. The trimmed prediction excludes one singular pixel from the neighboring region. A more efficient sorting method is used to achieve lower distortion. Then, a further sorting that considers context complexity is proposed to ensure better visual quality. The smooth pixels located in rough areas are assigned high priorities for carrying bits by using the prediction error expansion method. With these improvements, our method shows better performances in terms of capacity and distortion.