SYSTEM AND METHOD FOR TARGETED OBJECT ENHANCEMENT TO GENERATE SYNTHETIC BREAST TISSUE IMAGES

1 - 20 . (canceled)

21 . A method for processing breast tissue image data, comprising:

processing image data of a patient's breast tissue to generate a set of image slices that collectively depict the patient's breast tissue, wherein at least one image slice in the set of image slices comprises an object of a particular object type;

performing a mode filter on the image slices to enhance the set of image slices;

performing a plurality of object-recognition processes on the image slices, wherein each of the plurality of object-recognition processes is configured to recognize a respective type of object that may be present in the image slices;

based at least in part on performing the plurality of object-recognition processes, recognizing the object of the particular object type in the at least one image slice in the set of image slices; and

generating, using the at least one image slice in the set of image slices, a synthesized image of the patient's breast tissue.

22 . The method of claim 21 , wherein the mode filter is based on an image type of the set of image slices

23 . The method of claim 22 , wherein the mode filter being based on an image type of the set of image slices comprises the mode filter being configured to highlight aspects of each image slice of the set of image slices based on the image type.

24 . The method of claim 22 , wherein the mode filter is automatically configured based on the image type.

25 . The method of claim 24 , wherein the mode filter comprises one or more filters.

26 . The method of claim 24 , wherein the mode filter is configured for at least one of identifying objects, highlighting masses or calcifications, identifying image patterns, and processing for generating a synthesized image.

27 . The method of claim 22 , wherein the mode filter is manually configured.

28 . The method of claim 21 , wherein performing the mode filter on the image slices occurs before performing the plurality of object-recognition processes on the images slices.

29 . The method of claim 28 , wherein the system is configured to allow for adding further object-recognition processes to the plurality of the object-recognition processes in order to recognize and display further types of objects.

30 . The method of claim 28 , wherein the image slices are fed to the respective object-recognizing modules in a sequence.

31 . The method of claim 28 , wherein the object-recognizing modules are applied in parallel on the image slices.

32 . The method of claim 21 , further comprising displaying target object types associated with the plurality of object-recognition processes in a graphical user interface.

33 . The method of claim 32 , wherein the graphical user interface provides options for an end user to select one or more target object types to be recognized and included in the synthesized image.

34 . The method of claim 33 , wherein the graphical user interface provides options for allowing an end user to input a weight factor for each of one or more target object types, and wherein user input weight factors are taken into account for generating and displaying user selected target object types in the synthesized image.

35 . The method of claim 21 , further comprising assigning a respective weight to each of the object-recognizing modules, wherein the assigned weight corresponds to a significance of the type of object recognized by the particular object-recognizing module, wherein the respective weights assigned to the object-recognizing modules determine an order of the object-recognizing modules through which the image slices are fed.

36 . The method of claim 35 , wherein the image slices are fed through a first object-recognizing module having a first weight before being fed through a second object-recognizing module having a second weight higher than the first weight.

37 . The method of claim 36 , further comprising determining whether an object of a first object type recognized by the first object-recognizing module and an object of a second type recognized by the second object-recognizing module are likely to overlap on the displayed synthesized image.

38 . The method of claim 37 , further comprising, if it is determined that the recognized object of the first object type and the recognized object of the second object type are likely to overlap, emphasizing the recognized object of the second object type relative to the recognized object of the first object type in the displayed synthesized image.

39 . The method of claim 21 , further comprising storing the synthesized image.

40 . The method of claim 21 , further comprising displaying the synthesized image.