Ted C. Wang

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This paper presents an approach for detecting microcalcifications in digital mammograms employing wavelet-based subband image decomposition. The microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a detection system that employs a suitable(More)
Anandamide [N-arachidonoylethanolamide (NAE)] was initially isolated from porcine brain and proposed as an endogenous ligand for cannabinoid receptors in 1992. Accumulating evidence has now suggested that, in the tissue, NAE is generated from N-arachidonoylphosphatidylethanolamides (N-ArPEs) by phosphodiesterase. In this study a sensitive and specific(More)
Recurring stupor can be caused by repeated metabolic, toxic or structural brain disturbances. Recently, cases of recurring stupor, with fast EEG activity were shown to display increased endogenous benzodiazepine-like activity during the episodes of stupor. Patients with recurring stupor underwent extensive metabolic and toxicologic screening, EEG and brain(More)
Breast cancer can be diagnosed with an early training course by detecting the presence of microcalcifications in screening mammograms. The multiresolution analysis using discrete wavelet transform presents characteristics which can be exploited to develop tools for detection of microcalcifications. The objective of this work is to study the best type of(More)
vi EXTENDED ABSTRACT The first mission of NASA's New Millennium Program, Deep Space 1 (DS1), has as one of its principal demonstration technologies the first autonomous optical navigation system to be used in deep space. The concept of DS1—to develop and validate new technologies in the context of a low-cost, deep-space planetary mission—was extremely(More)
Breast cancer is one of the usual cancers among the women in the worldwide population. The research paper is developing of a reliable tool to detect earlier signs of the breast cancer in mammograms. Accuracy rate of breast cancer in mammogram depends on image segmentation. Doctors and radiologists can miss the abnormality, due to inexperience's in the(More)
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