Generating an anatomical model using a rule-based segmentation and classification process

A computer-implemented network structure, comprising:

• a data network including a first set of voxel values, a second set of voxel values, a first object and a second object, wherein the first set of voxel values and the second set of voxel values are part of a digital image slice of a human body, wherein each of the first set of voxel values is linked to the first object, wherein each of the second set of voxel values is linked to the second object, wherein the first object exhibits a first property that depends on the first set of voxel values, and wherein the second object exhibits a second property that depends on the second set of voxel values;
• a class network including a first class and a second class, wherein a first membership function associated with the first class determines that the first object belongs to the first class, wherein a second membership function associated with the second class determines that the second object belongs to the second class, wherein the second membership function determines that the second object belongs to the second class based on a relation between the first property and the second property;
• and
• a process hierarchy including a first process step and a second process step, wherein the first process step designates the first class, wherein the second process step designates the second class and has an algorithm, wherein the first process step is performed before the second process step is performed, and wherein the algorithm measures the second property.

A computer-implemented network structure, comprising:

• a data network including a first voxel value, a second voxel value, a first link, a second link, a first object and a second object, wherein the first voxel value and the second voxel value are part of a digital image slice of a human body, wherein the first link links the first voxel value to the first object, and wherein the second link links the second voxel value to the second object;
• a class network including a first class and a second class;
• and
• a process hierarchy including a first process step and a second process step, wherein the first process step determines that the first object belongs to the first class, wherein the second process step determines that the second object belongs to the second class based on a relation between the second object and the first object, wherein the first process step is performed before the second process step is performed, wherein the second process step has an algorithm, and wherein the algorithm measures a property of voxels that belong to the second class.

A method comprising:

• specifying a first class of a class network;
• specifying a second class of the class network;
• specifying a third class of the class network, wherein objects of an object network are classified by a first membership function as belonging to the first class, wherein objects of the object network are classified by a second membership function as belonging to the second class, and wherein the first class is associated with a first human organ, the second class is associated with a second human organ, and the third class is associated with a third human organ,
• specifying a process hierarchy, wherein the process hierarchy includes a first process step, a second process step and a third process step;
• performing the first process step to detect the first human organ using the first membership function;
• performing the second process step to detect the second human organ using the second membership function;
• and
• performing the third process step to detect the third human organ using the third membership function, wherein the third membership function classifies objects of the object network as belonging to the third class based on either a first relation between objects in the third class and objects in the first class or based on a second relation between objects in the third class and objects in the second class, and wherein the third membership function classifies objects based on the second relation when the second membership function determines a better fit for the objects classified as belonging to the second class than a fit determined by the first membership function for the objects classified as belonging to the first class.