Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation.

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.

Video tracking using learned hierarchical features.

In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network.

Incorporating patch subspace model in Mumford-Shah type active contours.

In this paper, we propose a unified energy minimization model for segmentation of non-smooth image structures, e.g., textures, based on Mumford-Shah functional and linear patch model.

Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering- and classification-based methods with learning.

In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-aided tumor segmentation methods (either contour-based or region-based) are necessary to alleviate clinicians' workload. For contour-based methods, a minimal user interaction to draw an initial contour inside or outside the tumor lesion for further curve evolution to match the tumor boundary is preferred, but parameters within most of these methods require manual adjustment, which is technically burdensome for clinicians without specific knowledge.

Towards automatic detection of age-related macular degeneration in retinal fundus images.

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. The disease is highly associated with age, and becoming increasingly prevalent in our aging societies. Drusen is a pathological feature that is well-associated with AMD.

Epitomized summarization of wireless capsule endoscopic videos for efficient visualization.

A video recording of an examination by Wireless Capsule Endoscopy (WCE) may typically contain more than 55,000 video frames, which makes the manual visual screening by an experienced gastroenterologist a highly time-consuming task. In this paper, we propose a novel method of epitomized summarization of WCE videos for efficient visualization to a gastroenterologist. For each short sequence of a WCE video, an epitomized frame is generated.

A computer assisted method for nuclear cataract grading from slit-lamp images using ranking.

In clinical diagnosis, a grade indicating the severity of nuclear cataract is often manually assigned by a trained ophthalmologist to a patient after comparing the lens' opacity severity in his/her slit-lamp images with a set of standard photos. This grading scheme is often subjective and time-consuming. In this paper, a novel computer-aided diagnosis method via ranking is proposed to facilitate nuclear cataract grading following conventional clinical decision-making process.

A computer-aided diagnosis system of nuclear cataract via ranking.

A novel computer-aided diagnosis system of nuclear cataract via ranking is firstly proposed in this paper. The grade of nuclear cataract in a slit-lamp image is predicted based on its neighboring labeled images in a ranked images list, which is achieved using an optimal ranking function. A new ranking evaluation measure is proposed for learning the optimal ranking function via direct optimization.

A kernel-induced space selection approach to model selection in KLDA.

Model selection in kernel linear discriminant analysis (KLDA) refers to the selection of appropriate parameters of a kernel function and the regularizer. By following the principle of maximum information preservation, this paper formulates the model selection problem as a problem of selecting an optimal kernel-induced space in which different classes are maximally separated from each other. A scatter-matrix-based criterion is developed to measure the "goodness" of a kernel-induced space, and the kernel parameters are tuned by maximizing this criterion.

Two criteria for model selection in multiclass support vector machines.

Practical applications call for efficient model selection criteria for multiclass support vector machine (SVM) classification. To solve this problem, this paper develops two model selection criteria by combining or redefining the radius-margin bound used in binary SVMs. The combination is justified by linking the test error rate of a multiclass SVM with that of a set of binary SVMs.

Incorporating temporal information into level set functional for robust ventricular boundary detection from echocardiographic image sequence.

Echocardiographic images often suffer from dropouts that lead to loss of signals on the ventricular boundary and cause the level set curve used to detect the boundary leaking out from the gaps on the boundary. In this paper, a novel method that incorporates temporal information into the level set functional is proposed to solve the leakage problem encountered when detecting the heart wall boundary from the echocardiographic image sequence. The ventricular boundary is quantitatively partitioned and classified into strong and weak segments.

Semi-supervised nasopharyngeal carcinoma lesion extraction from magnetic resonance images using online spectral clustering with a learned metric.

In this paper, we consider the extraction of nasopharyngeal carcinoma lesion from MR images as a region segmentation problem. We propose a semi-supervised segmentation approach to segment the lesion in two steps. First, a metric is learned in a supervised fashion, which maximizes the separation between two groups of pixels (tumor or non-tumor) with minimal user interaction.

Incorporating temporal information into active contour method for detecting heart wall boundary from echocardiographic image sequence.

A new method which incorporates temporal information into the active contour function is proposed to solve the dropout and speckle noise problems encountered when detecting the inner heart wall boundary from echocardiographic image sequence. The ventricular boundary is considered to be composed of strong and weak segments. The weak segments are interfered by image degradations in ultrasound, and they are too weak to constrain the curve evolution.

Incorporating temporal information for ventricular contour detection in echocardiographic image sequences.

A novel way to incorporate temporal information with level set algorithm is proposed to counter the dropout problem when detecting ventricular contours in echocardiographic raphic image sequences. The temporal information ided embed- ed into the speed term of the level set equation. By identifying the ventricular contours as strong or weak segments, the weak segments are strengthened based on temporal information from neighboring frames.

Incorporating temporal information for ventricular contour detection in echocardiographic image sequences.

A novel way to incorporate temporal information with level set algorithm is proposed to counter the dropout problem when detecting ventricular contours in echocardiographic raphic image sequences. The temporal information ided embed- ed into the speed term of the level set equation. By identifying the ventricular contours as strong or weak segments, the weak segments are strengthened based on temporal information from neighboring frames.

Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches.

We presented and evaluated two deformable model-based approaches, region plus contour deformation (RPCD), and level sets to extract metastatic cervical nodal lesions from pretreatment T2-weighted magnetic resonance images. The RPCD method first uses a region deformation to achieve a rough boundary of the target node from a manually drawn initial contour, based on signal statistics. After that, an active contour deformation is employed to drive the rough boundary to the real node-normal tissue interface.

The relationship between nasopharyngeal carcinoma tumor volume and TNM T-classification: a quantitative analysis.

Recent findings show that tumor volume is a significant prognostic factor for the treatment of nasopharyngeal carcinoma (NPC). The inclusion of tumor volume as an additional prognostic factor in the UICC TNM classification system was suggested; however, how tumor volume could possibly be incorporated is still unexplored. In this paper, we report a quantitative analysis on the relationship between NPC tumor volume and T-classification, using the data from 206 NPC patients.

Correlation between MR imaging-derived nasopharyngeal carcinoma tumor volume and TNM system.

Purpose: To measure nasopharyngeal carcinoma tumor volume based on magnetic resonance images using a validated semiautomated measurement methodology and correlate tumor volume with TNM T classification.

Methods And Materials: The study population consisted of 206 consecutive nasopharyngeal carcinoma patients who had magnetic resonance imaging staging scans. Tumor volume was measured using a semisupervised knowledge-based fuzzy clustering algorithm.

Characteristic wave detection in ECG signal using morphological transform.

Background: Detection of characteristic waves, such as QRS complex, P wave and T wave, is one of the essential tasks in the cardiovascular arrhythmia recognition from Electrocardiogram (ECG).

Methods: A multiscale morphological derivative (MMD) transform-based singularity detector, is developed for the detection of fiducial points in ECG signal, where these points are related to the characteristic waves such as the QRS complex, P wave and T wave. The MMD detector is constructed by substituting the conventional derivative with a multiscale morphological derivative.

A criterion for optimizing kernel parameters in KBDA for image retrieval.

A criterion is proposed to optimize the kernel parameters in Kernel-based Biased Discriminant Analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion.

Life-threatening ventricular arrhythmia recognition by nonlinear descriptor.

Background: Ventricular tachycardia (VT) and ventricular fibrillation (VF) are ventricular cardiac arrhythmia that could be catastrophic and life threatening. Correct and timely detection of VT or VF can save lives.

Methods: In this paper, a multiscale-based non-linear descriptor, the Hurst index, is proposed to characterize the ECG episode, so that VT and VF can be recognized as different from normal sinus rhythm (NSR) in the descriptor domain.

Texture decomposition by harmonics extraction from higher order statistics.

In this paper, a method of harmonics extraction from Higher Order Statistics (HOS) is developed for texture decomposition. We show that the diagonal slice of the fourth-order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies. We propose to use this fourth-order cumulants slice to estimate a power spectrum from which the harmonic frequencies can be easily extracted.