Learn More
Mesh modeling is an important problem with many applications in image processing. A key issue in mesh modeling is how to generate a mesh structure that well represents an image by adapting to its content. We propose a new approach to mesh generation, which is based on a theoretical result derived on the error bound of a mesh representation. In the proposed(More)
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to(More)
It has recently been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the detected object's location. Here we develop solutions based on impulse restoration for the Gaussian noise case. We propose a new(More)
We have identified the value of 18F-fallypride [(S)-N-[(1-allyl-2-pyrrolidinyl)methyl]-5-(3-[18F]fluoropropyl)-2, 3-dimethoxybenzamide], as a dopamine D-2 receptor radiotracer for the study of striatal and extrastriatal receptors. Fallypride exhibits high affinities for D-2 and D-3 subtypes and low affinity for D-4 (3H-spiperone IC50s: D-2 = 0.05 nM [rat(More)
Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a(More)
Brain activation studies based on PET or fMRI seek to explore neuroscience questions by statistically analyzing the acquired images to produce statistical parametric images (SPIs). An increasingly wide range of univariate and multivariate analysis techniques are used to generate SPIs in order to detect mean-signal activations and/or long-range spatial(More)
Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on(More)
It is now widely accepted that image quality should be evaluated using task-based criteria, such as human-observer performance in a lesion-detection task. The channelized Hotelling observer (CHO) has been widely used as a surrogate for human observers in evaluating lesion detectability. In this paper, we propose that the problem of developing a numerical(More)
It has recently been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function that indicates the detected object's location. We develop solutions based on impulse restoration for the Gaussian-noise case. We propose a new iterative(More)