Classification of cellular images and videos

Abstract

A method for performing cellular classification includes extracting a plurality of local feature descriptors (220) from a set of input images (210) and applying a coding process to convert each of the plurality of local feature descriptors into a multi-dimensional code (225). A feature pooling operation (230) is applied on each of the plurality of local feature descriptors to yield a plurality of image representations and each image representation is classified as one of a plurality of cell types (240).

Description

[0001] Cross References to Related Applications [0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 126,823, filed March 2, 2015, the entire contents of which are hereby incorporated by reference. technical field [0003] The present disclosure generally relates to methods, systems and apparatus for performing classification of cellular images and videos. The proposed technique can be used, for example, to classify endoscopic images and digital holographic microscopy images. Background technique [0004] In vivo cell imaging is the study of living cells using images acquired from an imaging system such as an endoscope. Due to recent advances in fluorescent protein and synthetic fluorophore technologies, increasing research efforts are focused on in vivo cellular imaging techniques that provide insights into the fundamental nature of cell and tissue function. In vivo cell imaging techniques now span multiple modalities including, for exam...

AI Technical Summary

Problems solved by technology

In conventional systems, results must be obtained through a time-consuming and computationally intensive manual testing process

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