Method and a system for combining automated psychiatric profiling from combined input images of brain scans with observed expert and automated interpreter using a neural network

Abstract

A method for psychiatric profiling comprising: (a) obtaining a 3-D brain scan image (and its enhancement parameters) and obtaining a psychiatric profile analysis; (b) extracting the edges of the scan, pinpointing reference points on it, positioning, standardizing, and aligning it; (c) autocropping, extracting a plurality of features and/or regions within the scan; (d) correlating said regions or features with database images and parameters; (e) searching a message memory for messages that make up an individual's profile (said messages correspond to feature/s of a database), outputting each message to form a first profile set of messages; (f) obtaining a second set of feature detections and message statements; (g) accepting output detections and related messages in the first set to form a third profile set (a subset of the first set), combining the second and third profile to form a fourth set, alternatively allowing the fourth set to equal the first set, alternatively allowing the third set to equal the second set; (h) storing in the message memory the fourth set, corresponding to the brain scan image or storing the fourth set which corresponds to new feature/s on the brain scan image, providing a corrected output based on said corrected fourth set. A system for providing human profiling using the method is also disclosed.

1 . A method for providing human psychiatric profiling using a process of analysis and classification of brain scan images comprising the steps of; a) obtaining a 3-D brain scan image and the result of a psychiatric profile analysis and parameters used to enhance the image of the scan; b) extracting the edges of the brain scan image, pinpointing reference points on it, positioning, standardizing its size, and aligning it; c) autocropping, extracting and masking a specified plurality of features and regions and/or parameters within the brain scan; d) voting, matching or correlating extracted regions, images and parameters of a plurality of features of the scan with database template images and parameters; e) searching in a message memory for a plurality of messages that make up the profile of an individual, wherein each message corresponds to the respective feature or combination of features of a database, outputting each one of the said plurality of messages concurrently to form a first profile set of messages; f) obtaining a second set of feature detections and related message statements; g) accepting some output detections and related messages in the first set to form a third profile set of features and related messages that is a subset of the first set, combining the third profile set of messages with the second set to form a fourth set, alternatively allowing the fourth set to equal the first set, alternatively allowing the third set to equal the second set; h) storing in the said message memory the fourth set of detections and related messages corresponding to the said brain scan image or storing in the said message memory the fourth set of detections and related messages corresponding to a new combination of features on the brain scan image, providing a corrected output based on said corrected fourth set of detections and related messages.  
     
     
         2 . A method according to  claim 1  wherein the said features of the brain scan is one or a combination of general anatomic structures including CSF, gray matter, ventricular fluid, and lesioned tissue white matter, neurological mapping of activity to specified stimuli (such as specific sight, sound, vocal, smell, touch, taste, suggested imagination or other).  
     
     
         3 . A method according to  claim 1  wherein the said second set is composed of none, one or a combination of the elements of the set of feature detections and related message statements that form a human profile made by an expert interpreter.  
     
     
         4 . A method according to  claim 1  wherein the said second set is composed of none, one or a combination of the elements of the set of feature detections and related message statements that form a self profile of a person under analysis.  
     
     
         5 . A method according to  claim 1  where the input image is from an MRI scanner, fMRI, MRS, PET, or other.  
     
     
         6 . A method according to  claim 1  in which the input image is provided in a form of a computer memory of 2-D slices forming a 3-D map or alternatively of a complete 3-D image.  
     
     
         7 . A method according to  claim 1  in which the pinpointing of reference points is done by use of a matching template images.  
     
     
         8 . A method according to  claim 1  in which known reference points are built into the input image.  
     
     
         9 . A method according to  claim 1  in which areas and features are extracted using referencing to known given or calculated reference points.  
     
     
         10 . A method according to  claim 1  wherein the psychiatric analysis results are the profile results provided by readings of hand and foot palms.  
     
     
         11 . A method according to  claim 1  in which a standardized normalized image is determined using a generic algorithm that uses the scanner image enhancement parameters as input parameters provided into the generic algorithm procedure.  
     
     
         12 . A method according to claims  3  and  4  wherein said detections and related messages accepted from the first output set are selected according to their likelihood of correct output detection reporting and analysis.  
     
     
         13 . A method according to  claim 1  in which said edge extractor or the position registration circuit, or the feature extractor, comprises a neural network or in which the said pinpointing of reference points on the brain scan is done after and as a result of the said position registration using a neural network, or in which the said voting, matching and correlating extracted regions and images of features with database template images is done using a neural network or in which the said storing of the fourth set of detected features and related messages is in a form of a neural network or in which detection is performed by brain scan detector comprising a neural network.  
     
     
         14 . A method according to  claim 13  in which the said neural network is a multi layer peceptron neural network.  
     
     
         15 . A method according to  claim 1  wherein the said pinpointing of reference points is done by setting the palm, hand or foot in an encompassing fixed shell before imaging thereby referencing from the outer shell.  
     
     
         16 . A method according to  claim 1 , additionally comprising a device for measuring hardness and softness of specific mounts and areas of the skin, the bending angle of the fingers and finger formations on closed or clapped hands.  
     
     
         17 . A method according to claims  1  and  16  wherein a mechanically driven and controlled blunt pin element is used to press automatically on the skin and palm mounts.  
     
     
         18 . A method according to claims  1  and  16  wherein the pressure applied is controlled and measured, rebound rate of the skin and palm mount is measured using a laser scanner.  
     
     
         19 . A method according to  claim 1  in which auto-cropping and voting is performed by a generic algorithm in which auto-cropping and voting parameters are automatically optimized using a generic algorithm that maximizes fitness.  
     
     
         20 . A system for providing human profiling using the method as defined in any of the preceding claims comprising of: a) A mechanically driven blunt-pointed element adjoining an apparatus for measuring the angle of finger bending; b) a mechanically driven plate used for measuring the maximum allowed bending angle of the finger adjoining the apparatus; c) RAM memory storage; d) an microprocessor; e) input drive; f) a high resolution color printer; g) a computer operating system; h) Internet and network connection.