Abstract: There is provided a method for enhancing endoscopic image data, comprising the steps of: (a) receiving a plurality of image frames from an imaging section of an endoscope apparatus at a predetermined frame rate; (b) converting said plurality of image frames into a plurality of digital image frames at said predetermined frame rate; (c) applying at least one active Look-up-Table, comprising at least one first transform, to at least part of said plurality of digital image frames at said predetermined frame rate, so as to generate a plurality of enhanced digital image frames; (d) providing said enhanced digital image frames to a display at said predetermined frame rate; (e) generating at least one optimised Look-up-Table, comprising at least one second transform that is optimised utilizing at least one image parameter of a predetermined quantity of said plurality of digital image frames; (f) updating said at least one active Look-up-Table with said at least one second transform; (g) executing step (c) for said pr

Abstract: The invention relates principally to the statistical analysis of protein separation patterns. The invention solves the problems associated with producing models which are predictive of classification using unreduced data. The invention provides a method of analysing a representation of a separation pattern, the representation including a neighborhood representing a region of the separation pattern, the neighborhood including a plurality of data points, the method comprising augmenting data by representing the entire region using each data point of the neighborhood; and building a classification model using some or all of the data points.

Abstract: The present invention relates principally to the statistical analysis of protein separation patterns. Specifically, the invention provides a method of performing operations on protein samples for the analysis of separation patterns, the method including separating a plurality of protein samples to produce a plurality of respective separation patterns; forming a representation of each separation pattern; building an importance map by iteratively recording performance values of classification models against locations in the importance map which correspond to locations in representations of separation patterns of data points used to build the classification models; and processing the importance map to identify important areas. Parts of the map which include high performance values indicate regions of the separation pattern which are statistically important in predicting a classification of the pattern.