Wei Wang

Learn More
In this paper, we propose a new method for automated detection and segmentation of different tissue types in digitized uterine cervix images using mean-shift clustering and support vector machines (SVM) classification on cluster features. We specifically target the segmentation of precancerous lesions in a NCI/NLM archive of 60,000 cervigrams. Due to large(More)
This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each(More)
Comparison of a group of multiple observer segmentations is known to be a challenging problem. A good segmentation evaluation method would allow different segmentations not only to be compared, but to be combined to generate a "true" segmentation with higher consensus. Numerous multi-observer segmentation evaluation approaches have been proposed in the(More)
In this paper, we introduce a new classifier ensemble approach , applied to tissue segmentation in optical images of the uterine cervix. Ensemble methods combine the predictions of a set of diverse classifiers. The main contribution of our approach is an effective way of combination based on each classifier's performance level—namely, the sensitivity and(More)
We empirically evaluate a distance-guided learning method embedded in a multiple classifier system (MCS) for tissue segmentation in optical images of the uterine cervix. Instead of combining multiple base classifiers as in traditional ensemble methods, we propose a Bhattacharyya distance based metric for measuring the similarity in decision boundary shapes(More)
The accurate and automatic segmentation of tissue regions in cervigram images can aid in the identification and classification of precancerous regions. We implement and analyze four GPU (Graphics Processing Unit) based clustering algorithms: K-means, mean shift, deterministic annealing, and spatially coherent deterministic annealing. From our results, we(More)
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable.(More)
Rapid bio-oxidation of carbon monoxide (CO), a photoproduct of dissolved organic matter, results in diel cycles reflecting photochemical–biogeochemical–physical interactions. These cycles were characterized by time-series studies of hydrography, meteorology, insolation, optics, and CO concentration ([CO]). Diel patterns of near-surface [CO] generally varied(More)
The aim of this study was to understand the growth dynamics of Saccharina japonica (previously known as Laminaria japonica), particularly the portion lost during its growth cycle and the key factors that control loss rate in Sungo Bay, China. Growth and loss of S. japonica were investigated between January and July 2010 in Sungo Bay. Losses of the seaweed(More)