Method of forming a probability map

Abstract

A method of using a moving window to form a probability map is disclosed. According to one embodiment, a method may include obtaining measures of imaging parameters for stops of a moving window on an image. Probabilities of an event associated with the stops of the moving window are obtained, for example by matching the measures of the imaging parameters to a classifier.

What is claimed is: 
     
         1 . A method for generating a probability map, comprising:
 generating a moving window for application to a computation region of a medical image;   applying the moving window to the computation of the medical image;   obtaining measures of imaging parameters for stops of the moving window, wherein at least two neighboring stops of the moving window partially overlap; and   obtaining first probabilities of an event for each of the stops of the moving window.   
     
     
         2 . The method of  claim 1 , wherein obtaining the first probabilities of the event comprises matching the measures of the imaging parameters to a classifier for the event. 
     
     
         3 . The method of  claim 2 , wherein the classifier comprises a Bayesian classifier. 
     
     
         4 . The method of  claim 2 , wherein the classifier is created based on information associated with multiple measures of the imaging parameters for biopsy tissues and diagnoses for the biopsy tissues. 
     
     
         5 . The method of  claim 1 , wherein the imaging parameters comprise at least four types of magnetic resonance imaging (MRI) or other imaging parameters. 
     
     
         6 . The method of  claim 1 , further comprising creating a probability map based on the first probabilities, wherein the probability map comprises a plurality of voxels that provide an indication of a likelihood of the event occurring with a portion of the medical image associated with a respective voxel, and wherein the two neighboring stops of the moving window are shifted from each other by a distance substantially equal to a side length of one of the voxels. 
     
     
         7 . The method of  claim 6 , wherein the moving window overlaps areas associated with multiple voxels of the probability map at each stop. 
     
     
         8 . The method of  claim 1 , wherein said event is occurrence of a cancer. 
     
     
         9 . The method of  claim 1 , further comprising obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities. 
     
     
         10 . The method of  claim 9 , wherein obtaining the second probabilities of the event comprises calculating multiple assumed probabilities for respective voxels of the probability map based on the first probabilities of the event covering the respective voxels. 
     
     
         11 . The method of  claim 1 , wherein the medical image comprises a magnetic resonance imaging (MRI) image. 
     
     
         12 . The method of  claim 1 , wherein the moving window has a size defined based on a volume of a biopsy tissue. 
     
     
         13 . The method of  claim 12 , wherein the moving window has a volume defined based on a volume of a biopsy tissue. 
     
     
         14 . The method of  claim 1 , wherein the moving window has a circular shape. 
     
     
         15 . The method of  claim 1 , further comprising calculating a third probability of the event for a voxel of a probability map based on the first probability and a second probability of the first event. 
     
     
         16 . The method of  claim 1 , further comprising:
 obtaining a third probability of a second event for a first stop of the moving window by matching the first measure to a first classifier;   obtaining a fourth probability of the second event for a second stop of the moving window by matching second measures of the imaging parameter to the first classifier;   calculating a fifth probability of the first event based on the first and second probabilities of the first event;   calculating a sixth probability of the second event based on the third and fourth probabilities of the second event; and   creating a composite probability map based on information associated with the fifth probability of the first event and the sixth probability of the second event.   
     
     
         17 . The method of  claim 16 , wherein the first event is that a cancer occurs, and the second event is associated with a Gleason score. 
     
     
         18 . The method of  claim 1 , further comprising reducing the measures of the imaging parameters into a parameter set for each step of the moving window. 
     
     
         19 . The method of  claim 18 , wherein the obtaining the first probabilities of the event comprises matching the parameter set to a classifier for the event at each stop of the moving window. 
     
     
         20 . The method of  claim 18 , wherein the obtaining the first probabilities of the event comprises matching the parameter set to a biomarker library having a plurality of stored parameters associated with various events.