Method for analysing and classifying objects of interest, for example biological or biochemical objects, on the basis of time-lapse images of at least one group of objects of interest, for example for use for cytometric cell analysis, specifically time-lapse or time-series analysis, in image-based cytometry, comprising:
- A) optically and electronically recording and electronically storing a plurality of digital images of the group of objects of interest located in an object region of an optical object examination device, the plurality of digital images comprising at least one series of digital images of the group of objects of interest recorded at different moments in time;
- B) subjecting at least the series of digital images, recorded at different moments in time, of the plurality of digital images to a digital image processing process for the purposes of segmentation comprising at least one of i) identifying image elements as object representations of individual objects of interest of the group of objects of interest and ii) identifying image elements as sub-object representations of individual sub-objects of the particular objects of interest of the group of objects of interest, and electronically storing segmentation data representing these segmentation and identification processes;
- C) at least on the basis of the segmentation data:
- associating identified object representations or sub-object representations in digital images of the series recorded at chronologically successive moments for identifying, as a representation, the same object or sub-object or for identifying, as representations, objects or sub-objects in a source-result relationship, and electronically storing these association data representing this association process and thus the identification process;
- D) at least on the basis of the segmentation data or the segmentation data and the association data and/or image content data, identified via the segmentation data or the segmentation data and the association data, from the digital images of the series:
- detecting first features, manifesting themselves directly or indirectly in an individual digital image of the series, of individual objects or sub-objects identified in the digital images of the series by segmentation or by segmentation and association, at least for a plurality of digital images of the series recorded at different moments in time, and electronically storing at least one first feature data set representing these features;
- E) at least on the basis of the association data or the association data and segmentation data and/or image content data, identified via the association data or the association data and the segmentation data, of the digital images of the series and/or first feature data of the first feature data set:
- detecting second features, manifesting themselves directly or indirectly as differences between a plurality of the digital images of the series, of individual objects or sub-objects identified in the digital images of the series by segmentation and association, at least for a plurality of digital images of the series recorded at different moments in time, and electronically storing at least one second feature data set representing these second features;
- F) defining at least one second classifier which relates to at least one second feature and can be applied to second feature data of the second feature data set in such a way that an individual object or sub-object, identified in the digital images of the series by association, belongs to a second class associated with the classifier if the second feature data, associated with said object or sub-object, of the second data set satisfy at least one second classification condition representing classification in relation to the at least one second feature, and electronically storing second classifier data representing the second classifier with the second classification condition;
- G) classification by applying at least one defined second classifier to the second feature data set for determining individual objects or sub-objects which are identified in the digital images of the series by association and which belong to the second class associated with the second classifier applied or belong to a plurality of second classes each associated with one of the second classifiers applied;
- and
- H) analysing the data, associated with the objects or sub-objects belonging to the second class or classes after said classification process, from at least one of i) the association data, ii) the segmentation data, iii) the image content data, identified via at least one of the association data and segmentation data, of the digital images of the series, iv) first feature data of the first feature data set and v) second feature data of the second feature data set in relation to at least one query of interest.