A powerful experimental tool, ultra-sharp nano-electrode array is designed, fabricated and characterized. The application on
a combination of Scanning Electrochemical Microscopy (SECM) and the Atomic Force Microcopy (AFM) is demonstrated. It can measure sample electrochemically initiated by SECM changes of topography while detecting topography using AFM. In order to realize this, a specialized probe system that is composed of a micro-mechanical bending structure necessary for the AFM mode and an electrochemical UME-tip required for a high performance SECM is crucial. The probe array is a row of silicon transducers embedded in silicon nitride cantilever array. The sharp high-aspect ratio (20:1) silicon tips are shaped and a thin layer of silicon nitride is deposited, which embeds the silicon tips in a silicon nitride layer so that they protrude
through the nitride. Thus, the embedded silicon tips with a diameter less than 600 nm, the top radius less than 20 nm, and the aspect ratio as high as 20 can be achieved. A metal layer and an insulator layer are deposited on these tip structures to make each probe selectively conductive. Finally, cantilever structures are shaped and released by etching the silicon substrate from the backside. Electrochemical and impedance spectroscopic characterization show electrochemical functionality of the transducer system.
A pencil-shaped electrochemical transducer system for analysis
or surface modification in nanometer dimension has been
developed. High aspect ratio tip structures are shaped
combining isotropic and anisotropic deep reactive etch
processes to form the body of the transducer. In this way, tips
with an aspect ratio higher than 20 and a tip radius of smaller
than 50 nm can be achieved. Subsequently, a three-layer
system (an isolation layer: silicon nitride, a metal layer:
platinum or gold and an isolation layer: silicon nitride) was
deposited on the tip structure. Planarization of this structure
in combination with a back etch process enables a precise
exposure of the buried metal layer down to an electrode
dimension of 200 nm on the tip.
Electrochemical and impedance spectroscopic characterization
showed full electrochemical functionality of the transducer
system. Due to the high aspect ratio topography, this probe is
particularly suited for Scanning Electrochemical Microscope
(SECM) - methodologies.
Furthermore this technology promises a feasible production
possibility for both probe-arrays and probes on cantilevers.
KEYWORDS: Diagnostics, Image segmentation, Mammography, Biopsy, Feature extraction, Genetics, Databases, Computer aided diagnosis and therapy, Digital mammography, Breast cancer
We devised, built and tested a detector and segmentor of microcalcifications in mammograms. Our segmentor includes preprocessing, extraction of 17 features, genetic solution of the best subset of six features, and a k-nearest neighbor classifier to suppress false candidates.
We describe a database-aided system for diagnosing mammograms with automatic selection of features (i.e., attributes of objects or textures in images). A database of digitized images of calcifications and textures assists the diagnosis and reduces the negative biopsy rate. In our system mammograms are filtered to enhance the calcifications and textures; image features are automatically extracted and stored. Genetic selection and visual mapping of features permits global visualization of the entire database or selected portions of the databases for interaction with mammographers. We demonstrate that this system is able to cluster and retrieve visually and diagnostically similar mammograms.
We describe techniques for adaptive nonverbal visual querying of large databases of images. The technique facilitates (a) visual mapping, a technique visualizing the relationships among the images, revealed by plotting each image as a point in a multidimensional `feature space,' and (b) interactive selection of features to maximize the correspondence between the clusters in feature space and the user's understanding of the relationships among the stored images. We refer to these techniques of querying as Adaptive visual querying. Adaptive visual querying will facilitate browsing and searching image databases from examples of images and from computer-aided sketches.
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