Abstract: A method and system for classification of cells and particles in a biological sample using an automated image-based feature extraction and classification architecture. A method operates by applying a mask or series of masks to an image, extracting features from the unmasked portions of the image based on the content and location of colored pixels, selecting a subset of the extracted features, and mapping the subset of the extracted features into a classifier architecture. In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle. In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.

Abstract: A method and system for classification of cells and particles in a biological sample using an automated image-based feature extraction and classification architecture. A method operates by applying a mask or series of masks to an image, extracting features from the unmasked portions of the image based on the content and location of colored pixels, selecting a subset of the extracted features, and mapping the subset of the extracted features into a classifier architecture. In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle. In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.

Abstract: A method and system for classification of cells and particles in a biological sample using an automated image-based feature extraction and classification architecture. A method operates by applying a mask or series of masks to an image, extracting features from the unmasked portions of the image based on the content and location of colored pixels, selecting a subset of the extracted features, and mapping the subset of the extracted features into a classifier architecture. In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle. In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.

Abstract: Embodiments of the present invention encompass systems and methods for detecting the presence of a test pad on a test strip. Exemplary techniques involve receiving a test strip having at least one test pad, where individual test pads have a pad width with ink disposed on two ink zones at two opposing sides of the width of the pad, illuminating the at least one test pad with a light source, detecting reflected signals from the test pad, generating an image comprising of pixels of the two ink zones based on the reflected signals, detecting the presence of each of the ink zones by comparing the number of consecutive pixels against a predetermined threshold, and determining the presence of the test pad on the test strip if two ink zones are detected within the pad width.

Abstract: Embodiments of the present invention encompass systems and methods for detecting the presence of a test pad on a test strip. Exemplary techniques involve receiving a test strip having at least one test pad, where individual test pads have a pad width with ink disposed on two ink zones at two opposing sides of the width of the pad, illuminating the at least one test pad with a light source, detecting reflected signals from the test pad, generating an image comprising of pixels of the two ink zones based on the reflected signals, detecting the presence of each of the ink zones by comparing the number of consecutive pixels against a predetermined threshold, and determining the presence of the test pad on the test strip if two ink zones are detected within the pad width.

Abstract: A method and system for classification of cells and particles in a biological sample using an automated image-based feature extraction and classification architecture. A method operates by applying a mask or series of masks to an image, extracting features from the unmasked portions of the image based on the content and location of colored pixels, selecting a subset of the extracted features, and mapping the subset of the extracted features into a classifier architecture. In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle. In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.