A particle classifier system suitable for classification of particles (2, 2', 2", 2"', 2"") in a liquid sample, said particle classifier system comprises: - an optical detection assembly comprising at least one image acquisition device (8, 41) with an optical axis, the image acquisition device is configured to acquire images of an image acquisition area perpendicular to said optical axis; - a sample device (32, 42) comprising at least one sample container suitable for holding a sample in liquid form; - a translating arrangement configured to translate said image acquisition area through at least a part of said sample container, wherein the translating arrangement is configured to translate the image acquisition area with a step length between each acquired image; - a control system configured to control said optical detection assembly and said translating unit to acquire images of a plurality of image acquisition areas; - an image analyzing processing system programmed to analyze said acquired images by a method comprising: creating objects of individual particles captured by said acquired images, wherein an object denotes a sub-image that is a segment of an acquired image of one single particle, creating stacks of objects of each individual particle, wherein a stack of objects is a collection of objects of the same particle, and wherein a stack can comprise one object in-focus and a number of objects out-of-focus, identifying complete stacks of objects comprising at least one object wherein said particle is in-focus, and two objects wherein said particle is out-of-focus, and wherein the step length is utilized when determining a distance between objects in the stacks of objects, and determining, for each of said complete stacks of objects, a set of values for a set of features of at least N features, wherein N is larger than or equal to 1, and wherein the determination of said values of said set of features relates to both in-focus and out-of-focus objects and involves data obtained from said at least one object wherein said particle is in-focus, and said at least two objects wherein said particle is out-of-focus; and - an artificial intelligent processing system programmed to associate said set of values for said determined set of features for each individual particle to a particle classification. - an optical detection assembly comprising at least one image acquisition device (8, 41) with an optical axis, the image acquisition device is configured to acquire images of an image acquisition area perpendicular to said optical axis; - a sample device (32, 42) comprising at least one sample container suitable for holding a sample in liquid form; - a translating arrangement configured to translate said image acquisition area through at least a part of said sample container, wherein the translating arrangement is configured to translate the image acquisition area with a step length between each acquired image; - a control system configured to control said optical detection assembly and said translating unit to acquire images of a plurality of image acquisition areas; - an image analyzing processing system programmed to analyze said acquired images by a method comprising: creating objects of individual particles captured by said acquired images, wherein an object denotes a sub-image that is a segment of an acquired image of one single particle, creating stacks of objects of each individual particle, wherein a stack of objects is a collection of objects of the same particle, and wherein a stack can comprise one object in-focus and a number of objects out-of-focus, identifying complete stacks of objects comprising at least one object wherein said particle is in-focus, and two objects wherein said particle is out-of-focus, and wherein the step length is utilized when determining a distance between objects in the stacks of objects, and determining, for each of said complete stacks of objects, a set of values for a set of features of at least N features, wherein N is larger than or equal to 1, and wherein the determination of said values of said set of features relates to both in-focus and out-of-focus objects and involves data obtained from said at least one object wherein said particle is in-focus, and said at least two objects wherein said particle is out-of-focus; and creating objects of individual particles captured by said acquired images, wherein an object denotes a sub-image that is a segment of an acquired image of one single particle, creating stacks of objects of each individual particle, wherein a stack of objects is a collection of objects of the same particle, and wherein a stack can comprise one object in-focus and a number of objects out-of-focus, identifying complete stacks of objects comprising at least one object wherein said particle is in-focus, and two objects wherein said particle is out-of-focus, and wherein the step length is utilized when determining a distance between objects in the stacks of objects, and determining, for each of said complete stacks of objects, a set of values for a set of features of at least N features, wherein N is larger than or equal to 1, and wherein the determination of said values of said set of features relates to both in-focus and out-of-focus objects and involves data obtained from said at least one object wherein said particle is in-focus, and said at least two objects wherein said particle is out-of-focus; and - an artificial intelligent processing system programmed to associate said set of values for said determined set of features for each individual particle to a particle classification.