Abstract: Systems and methods for detecting objects in a holographic image are provided. The techniques include obtaining a holographic image having one or more objects depicted therein. A set of object templates is obtained. The set of object templates represents objects to be detected in the holographic image. One or more objects are detected in the holographic image using the set of object templates by iteratively computing a phase (?) of the optical wavefront at the hologram plane, background illumination (?). and encoding coefficients (A) for the set of object templates, until converged.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positons within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: Systems and methods for detecting objects in a holographic image are provided. The techniques include obtaining a holographic image having one or more objects depicted therein. A set of object templates is obtained. The set of object templates represents objects to be detected in the holographic image. One or more objects are detected in the holographic image using the set of object templates by iteratively computing a phase (?) of the optical wavefront at the hologram plane, background illumination (?). and encoding coefficients (A) for the set of object templates, until converged.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positions within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positons within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: A method is disclosed for classifying and/or counting objects (for example, cells) in an image that contains a mixture of several types of objects. Prior statistical information about the object mixtures (class proportion data) is used to improve classification results. The present technique may use a generative model for images containing mixtures of object types to derive a method for classifying and/or counting cells utilizing both class proportion data and classified object templates. The generative model describes an image as the sum of many images with a single cell, where the class of each cell is selected from some statistical distribution. Embodiments of the present techniques have been successfully used to classify white blood cells in images of lysed blood from both normal and abnormal blood donors.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positions within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: A system for detecting objects in a specimen includes a chamber for holding at least a portion of the specimen. The system also includes a lens-free image sensor for obtaining a holographic image of the portion of the specimen in the chamber. The system further includes a processor in communication with the image sensor, the processor programmed to obtain a holographic image having one or more objects depicted therein. The processor is further programmed to obtain at least one object template representing the object to be detected, and to detect at least one object in the holographic image.