Abstract: A novel modification to the order statistics filters called the Adaptive Weighted-Local-Difference Order Statistics is shown that will act as a generic framework for the design of adaptive filters suitable for specific signal processing applications. To demonstrate the design of filters using this framework two implementations were defined and evaluated: Edge Orientation Adaptive Weighted-Local-Difference Median Filter (EOAWLDMF) and Luminance-Similarity Adaptive Weighted-Local-Difference Median Filter (LSAWLDMF) for restoration of noisy images.

Abstract: A novel modification to the order statistics filters called the Adaptive Weighted-Local-Difference Order Statistics is shown that will act as a generic framework for the design of adaptive filters suitable for specific signal processing applications. To demonstrate the design of filters using this framework two implementations were defined and evaluated: Edge Orientation Adaptive Weighted-Local-Difference Median Filter (EOAWLDMF) and Luminance-Similarity Adaptive Weighted-Local-Difference Median Filter (LSAWLDMF) for restoration of noisy images.

Abstract: Method and apparatus for enhancing representations of micro-calcifications in a digital mammogram image. The method includes smoothing the digital mammogram image using a fuzzy smoothing technique to yield a fuzzy smoothed image. The method includes subtracting the fuzzy smoothed image from the digital mammogram image to yield a micro-calcifications enhanced image. The method includes scaling gray level values of pixels in the micro-calcifications enhanced image by a predetermined amount to provide a digital mammogram image with enhanced representations of the micro-calcifications. Apparatus for enhancing representations of micro-calcifications includes an image processing unit that uses an image acquisition unit to receive an image and a digital signal processor to process the image to provide a digital mammogram image with enhanced representations of micro-calcifications.

Abstract: Method and apparatus for enhancing representations of micro-calcifications in a digital mammogram image. The method includes smoothing the digital mammogram image using a fuzzy smoothing technique to yield a fuzzy smoothed image. The method includes subtracting the fuzzy smoothed image from the digital mammogram image to yield a micro-calcifications enhanced image. The method includes scaling gray level values of pixels in the micro-calcifications enhanced image by a predetermined amount to provide a digital mammogram image with enhanced representations of the micro-calcifications. Apparatus for enhancing representations of micro-calcifications includes an image processing unit that uses an image acquisition unit to receive an image and a digital signal processor to process the image to provide a digital mammogram image with enhanced representations of micro-calcifications.

Abstract: Method and system for determining skinline in digital mammogram images. The method includes smoothening a digital mammogram image to yield a smoothened image. The method also includes determining gradient in the digital mammogram image to yield a gradient map. Further, the method includes extracting breast region from the digital mammogram image based on the smoothened image and the gradient map using fuzzy rule based pixel classification to yield a binary image. Moreover, the method includes filtering the binary image to remove noise and to yield a filtered image. The method also includes extracting boundary of the breast region in the filtered image. Furthermore, the method includes detecting the skinline based on the boundary of the breast region.

Abstract: A digital microscope comprises a housing with an image acquisition, an image processing, and a network communication (APC) module. The APC module can further comprise an image capture unit, coupled to an image sensor with a view to a subject on a slide, the image capture unit receiving an image of the subject. The APC module also comprises an image processing unit, coupled to the image capture unit, the image processing unit enhancing the image with classifications. Also, a network interface of the APC module, coupled to the image processing unit and to a network, the network interface sending the enhanced image across to the network and to receive control commands, the control commands associated with the view of the subject.

Abstract: Method and system for analyzing an image of an oral sample. The method includes receiving the image of the oral sample. The method also includes converting the image to a gray-scale image. Further, the method includes de-noising the gray-scale image. Furthermore, the method includes enhancing epithelial region in the gray-scale image. Also, the method includes generating a binary image from the gray-scale image. The method further includes detecting boundary of the epithelial region in the binary image. Furthermore, the method includes extracting the boundary of the epithelial region. The method also includes extracting the basal cell nuclei in the epithelial region. Further, the method includes determining one or more parameters of the epithelial region and the basal cell nuclei to enable detection of the oral sample as one of pre-malignant and non-malignant.

Abstract: Method and system for analyzing breast carcinoma using microscopic image analysis of fine needle aspirates. The method includes extracting a G-plane image from an image. The method also includes de-noising the G-plane image. Further, the method includes balancing histogram imbalance associated with the G-plane image. Furthermore, the method includes generating a binary image from the G-plane image. The method also includes filtering the binary image to yield a nuclear map. Further, the method includes extracting a nuclear contour from the nuclear map. Moreover, the method includes determining one or more parameters from at least one of the G-plane image, the nuclear map and the nuclear contour to enable detection of the breast lesion as one of malignant and non-malignant.

Abstract: A color filter array includes arrangements of green-filtered pixels to optimize performance and accuracy of color interpolation operations. Where red or blue pixels are found, green values are estimated with a high degree of accuracy. Unknown red and blue intensity values are interpolated by converting known red and blue values to hue. The interpolated hue values are then converted to intensity data.

Abstract: A color filter array includes arrangements of green-filtered pixels to optimize performance and accuracy of color interpolation operations. Where red or blue pixels are found, green values are estimated with a high degree of accuracy. Unknown red and blue intensity values are interpolated by converting known red and blue values to hue. The interpolated hue values are then converted to intensity data.