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eyes:logics:classclean [2017/06/09 17:20] nfische created |
eyes:logics:classclean [2017/06/09 18:07] (current) nfische [Usage] |
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| ===== Usage ===== | ===== Usage ===== | ||
| - | The ClassClean logic computes a quality score for particles in context of a group of particles ("class"). Therefore, this logic requires particles to be aligned and classified beforehand - either by assignment to a reference from alignment or a class from classification. ClassClean can be operated in two modes: In "clean" mode particles are evaluated within each class and dependent on chosen parameters a certain fraction of only the best particles is kept. In "refine" mode existing classes from a previous "clean" mode run may be supplemented with a new set of particles. These new particles are then i) assigned to the best matching class, ii) evaluated within this class and iii) either kept when they improve the quality measure of this class or discarded. | + | The ClassClean logic computes a quality score for particles in context of a group of particles ("class"). Therefore, this logic requires particles to be aligned and classified beforehand - either by assignment to a reference from [[eyes:logics:Alignment]] or a class from [[eyes:logics:Classification]]. ClassClean can be operated in two modes: In "clean" mode particles are evaluated within each class and dependent on chosen parameters a certain fraction of only the best particles is kept. In "refine" mode existing classes from a previous "clean" mode run may be supplemented with a new set of particles. These new particles are then i) assigned to the best matching class, ii) evaluated within this class and iii) either kept when they improve the quality measure of this class or discarded. Using a circular mask helps in particle evaluation by removing background noise. Note: The ClassClean logic performs no alignment. |
| ===== Example ==== | ===== Example ==== | ||
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| ===== Modes/Processes ===== | ===== Modes/Processes ===== | ||
| ==== Clean ==== | ==== Clean ==== | ||
| - | Use this mode to remove bad particles from a data set. | + | Use this mode to remove bad particle images from a data set. |
| |< 100% 30% >| | |< 100% 30% >| | ||
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| | -> Class truncation threshold | Maximum number of best particles to be kept for each class | | | -> Class truncation threshold | Maximum number of best particles to be kept for each class | | ||
| | Class truncation - //disabled// | Use this option to define the fraction of best particles to be kept solely by the "Sigma value" | | | Class truncation - //disabled// | Use this option to define the fraction of best particles to be kept solely by the "Sigma value" | | ||
| - | | Next main parameter | and more more more | | + | |Header key | Define whether classes should be defined by preceding alignment ("referenceData") or classification ("ClusterMember") | |
| - | | -> and its sub-parameter | ... descriptions | | + | |Mask usage | Define whether no mask ("none"), a circular mask or a soft circular mask should be used. | |
| + | | -> Relative radius | Define radius of the circular mask | | ||
| + | |Minimum class size | Minimum number of particles to be kept within each class. | | ||
| + | |Sigma value | Define in terms of standard deviations what fraction of the best particles should be kept. | | ||
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Input ^ Description ^ | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | + | | Input Images | Particle images to be cleaned. Note: This particle images must have been aligned and/or classified | |
| - | | SecondInput | Input Description 2 | | + | |
| - | | //ThirdInput// | Input Description 3: Optional Input in Italic | | + | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Output ^ Description ^ | ^ Output ^ Description ^ | ||
| - | | FirstOutput | Output Description | | + | | Class members | Good particle images to be kept | |
| + | | Class sums | Class averages from cleaned classes containing only the good particles| | ||
| + | | Discarded Images | Bad particles images discarded from the data| | ||
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ New/Changed Header Values ^ Description ^ | ^ New/Changed Header Values ^ Description ^ | ||
| - | | headerValue1 | what does it say? how is it changed? | | + | | ClassCleanClassQuality | Overall quality measure of the class the particle image belongs to | |
| - | | headerValue2 | what does it say? how is it changed? | | + | | ClassCleanImageQuality | Quality measure of the individual particle image within the context of its class | |
| - | | headerValue3 | what does it say? how is it changed? | | + | | classSize | Number of class members after cleaning | |
| - | | headerValue4 | what does it say? how is it changed? | | + | |
| - | ==== thisIsTheNameOfMode2 ==== | + | ==== Refine ==== |
| - | Here, a short introduction for the given mode should be placed. Again, state WHAT and WHY this mode us useful in not more than 2 sentences. | + | Use this mode to add only good particle images to an already existing "cleaned" data set. |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Parameters ^ Description ^ | ^ Parameters ^ Description ^ | ||
| - | | Some changeable parameter | Description of this parameter | | + | |Header key | Define whether classes should be defined by preceding alignment ("referenceData") or classification ("ClusterMember") | |
| - | | -> and its sub-parameter | more description | | + | |Mask usage | Define whether no mask ("none"), a circular mask or a soft circular mask should be used. | |
| - | | Next main parameter | and more more more | | + | | -> Relative radius | Define radius (in fractions) of the circular mask | |
| - | | -> and its sub-parameter | ... descriptions | | + | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Input ^ Description ^ | ^ Input ^ Description ^ | ||
| - | | FirstInput | Input Description 1 | | + | | Input data (Preclassified) | Preclassified particle images from a previous “clean” run. |
| - | | SecondImput | Input Description 2 | | + | | Input data (Unclassified) | New particle images to be evaluated and to be either added to the existing classes or to be discarded. |
| - | | ThridImput | Input Description 1 | | + | |
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ Output ^ Description ^ | ^ Output ^ Description ^ | ||
| - | | FirstOutput | Output Description | | + | | Class members | Good particle images to be kept | |
| + | | Class sums | Class averages from cleaned classes containing only the good particles| | ||
| + | | Discarded Images | Bad particles images discarded from the data| | ||
| |< 100% 30% >| | |< 100% 30% >| | ||
| ^ New/Changed Header Values ^ Description ^ | ^ New/Changed Header Values ^ Description ^ | ||
| - | | headerValue1 | what does it say? how is it changed? | | + | | ClassCleanClassQuality | Overall quality measure of the class the particle image belongs to | |
| - | | headerValue2 | what does it say? how is it changed? | | + | | ClassCleanImageQuality | Quality measure of the individual particle image within the context of its class | |
| - | | headerValue3 | what does it say? how is it changed? | | + | | classSize | Number of class members after cleaning | |
| - | | headerValue4 | what does it say? how is it changed? | | + | |referenceData/clusterMember(depending on user selection)| Class where the good "unclassified" images have been placed by ClassClean | |
| - | + | ||
| - | ===== Concept ===== | + | |
| - | In this paragraph, the "HOW a logic works under the hood" and WHY someone should use it can be elaborated with higher detail. Describes a scenario in an image processing workflow where this logic can be used to solve the resulting problem. Also, wikipages, publications or anything else describing the theory behind an algorithm should be linked here, if applicable. | + | |