FIELD OF THE INVENTION
The present invention generally relates to a field of image processing and data image classification. More particularly, the present invention relates to a system and a method for detecting, processing and classifying biometric images using digital images.
BACKGROUND OF THE INVENTION
This invention uses a computer based technique to predict brain disease, brain degenerative disease and atrophy as well as other psychiatric illnesses before their onset. The brains of people with Alzheimer show early atrophy before onset of diseased symptoms. Schizophrenia patients show minor changes even before their first psychotic episode. That raises the possibility of screening and early diagnosis for the disease and early intervention for people at risk.
This invention is an automated tool comprising a computed algorithm for the sake of providing automated early diagnosis of disease and psychiatric conditions.
There are many tools and procedures for obtaining brain scan images. Likewise, there are countless algorithms and methods intended to improve scan images using image processing techniques. However, all automated diagnostic tools for brain scan images have one thing in common. They must all contain within their algorithms a method of data classification and storage as well as a method for training the classifier using an expert interpreter. This invention patents the use of a neural network (and as such includes a fuzzy logic type of classifier).
Brain scan images are provided via an internet or network connection and are analyzed by the procedures described.
Early treatment with behavioral therapy or drugs could prevent, or at least mitigate, the full onset of Alzheimer or even schizophrenia. The longer the disease or psychosis goes untreated, the worse the outcome. Alzheimer and Schizophrenia is probably the most expensive diseases for the National Health Service of any country. If it can be prevented by early detection, the implications are vast.
Magnetic resonance imaging (MRI) in brain scans showed significant differences between healthy brains versus those of patients. The brain changes began some time before the Alzheimer or schizophrenic patients first suffered dementia or a psychotic episode.
Over the clinical course of Alzheimer, patients demonstrate progressive declines in functional ability that correlate with MMSE scores. In the preclinical phase, also called MCI, patients with MMSE score greater than 23 will demonstrate minimal impairment—generally, mild memory loss—while functioning normally and independently.
Though sensitivity issues are less of a problem in diagnosing dementia per se, Specificity issues differentiating Alzheimer from ordinary age related dementia proves a main hurdle. MRI perfusion scan image with additional MRI structural imagery proves to be an effective base image system to diagnose early stages of Alzheimer using the Neural Network Computed method described. Both Voxel-Based Morphometry and volumetric changes, structural and functional variations are recorded on the database for analysis using the neural network classifier. The advantages of using a Neural network/Fuzzy logic type of analysis is that structural atrophy can be classified not only by volumetric single or small parameter system but by a multi parameter classifier of normalized images having a multitude of variation of 3-D shapes in a time dependent (age or durational progression of the disease) axis. The spatial normalization step aims to map each structural MRI to a template in standard 3-D and stereotactic space.
Atrophy rates for brain temporal lobe, cortex, Amygdalae, temporal gyrus, hippocampus, and entorhinal cortices are significantly increased in patients compared with controls. Linear extrapolation backward suggested medial temporal lobe atrophy commenced 3.5 years before onset of symptoms, when all patients were asymptomatic. Medial temporal lobe atrophy rates are an early and distinguishing feature of Alzheimer. Atrophy rates for brain, temporal lobe, hippocampus, and entorhinal cortices are significantly increased in patients compared with controls.
Schizophrenia patients have significant deficits in cortical gray matter and in temporal lobe gray matter. The temporal lobes of the brain are linked with speech and the experience of hallucinations. There were also significant differences in whole brain volume, as well as significant enlargement of the lateral and third ventricles. Structural deviations were found in both untreated and minimally treated subjects. No relationships were found between any brain matter volumes and positive or negative symptoms. Structural brain abnormalities were distributed throughout the cortex with particular decrement evident in gray matter. This feature is consistent with altered cell structure and disturbed neuronal connectivity, which accounts for the functional abnormality of psychosis. These brain abnormalities were not specific to schizophrenia; they were also present in the brains of people suffering from other kinds of psychosis, such as bipolar disorder. It is assumed that many mental illnesses begin with the same changes in brain structure and chemistry and that an initial common pathway diverges into different forms of mental illness. This means that treating anyone showing signs of the brain abnormalities should prevent the onset of other mental diseases as well.
Additionally, researchers in a pilot program at the Israel “Nes-Ziona” psychiatric hospital found that it was possible to determine psychiatric illnesses using the methods disclosed in Israeli Patent Application No. 138975, especially emphasizing the measurements of hardness of specific mounts and areas of the skin, the bending angle of the fingers, spacing between the fingers, relative finger lengths, the mounts on fingers, finger formations on closed or clapped hands as well as other features of the palms disclosed in Israeli Patent Application No. 138975.
The process of decoding and analyzing brain scan images so as to provide an accurate psychiatric profile of individuals is difficult to provide under human evaluation.
Therefore, the main objective of this invention is to provide a method and system to diagnose and profile dementia (especially Alzheimer) and psychiatric illness using images of brain scans. Using MRI or other tools with brain scan analysis, the present invention uses the creation of a neural network or a multi-layer perceptron (MLP) neural network (NN) in which a centralized data bank combines brain scan images with experience from expert psychiatric advice and diagnosis placing emphasis on medical and psychiatric history of individuals being analyzed. The computer algorithms involved in this procedure have already proved themselves clinically in other applications such as that described in US patent Roger et al. (U.S. Pat. Nos. 6,205,236 and 5,999,639 and 6,115,488) where very similar Neural Network based algorithms are currently used.
Evolutionary development of the human brain occurred at the same time as the palms and during the first tool creation era of the first humans. Human brain and palm morphologies resultantly bear correlations. Therefore, another objective of the present invention is to complement the above-mentioned method of diagnosis and profiling with that disclosed in Israeli Patent Application No. 138975 whereby particular emphasis is made to certain features of the hand and foot, mentioned above. These two objectives together provide for more accurate psychiatric profiling. It is intended to find correlations between palm hand and foot features, and features on brain scans. This may provide insight into psychiatric, psychological and character profiling. This is important in brain research as well as in providing more accurate diagnostics.
Another objective of the present invention is the classification of brain scans using MRS and fMRI (Magnetic resonance Spectroscopy and Functional MRI) indicating functional characteristics of the brain (i.e neural activity).
It is assumed that different classifications of character, personality, psychological and psychiatric profiles would have a different spread of neural activity for similar neural stimuli, such as specific sight, sound, vocal, smell, touch, taste, suggested imagination or other. It is intended to find and use unique specific neural activity associated with each of these specified classifications indicating the link between the neural activity and the classification. Finding such a link and classifying it in the form of a computed neural network will aid in the psychiatric diagnosis, making it more accurate.
Using brain scan technology, we are now able to identify the content of a person's thought, albeit in a very limited context. However, it is assumed that although, the basic pattern of neural firing is maintained in the general population, significant variations on the general pattern apply. These variations are dependant amongst factors that include the psychiatric profile of the person.
In many previous studies have shown that brain areas can be selective for processing a particular type of visual information. In the cortical brain regions associated with mental processing, the fusiform face area responds strongly to faces while the para-hippocampus place area responds strongly to indoor and outdoor scenes depicting the layout of local space. It was also found that the magnitude of activity in these two brain areas is much livelier or stronger when one is seeing the picture (physically present in front of them) compared with just imagining it.
Portable scanning technique (such as laser scanners) could be used to gain some insight into what is happening in the minds of people who are unable to communicate because they are suffering from an injury or disorder that makes speech impossible. However, it is assumed that it will be possible to predict and analyze thought patterns with almost 100% accuracy if adjustment is made for the thought pattern analysis by taking into consideration the psychiatric profile of the individual being analyzed. Therefore, another objective of this patent is to categorize neural functional activity (agitated by specified stimuli) according to the psychiatric profile thereby providing for a method and system for analyzing thoughts. This procedure has special emphasis for the need of prostheses limbs in order to function.
A computed neural network is used to correlate sequenced brain neural activity with memorized sequences of template scan images recorded in a central database of template scan images that have been classified according to their psychiatric profile.
Other objectives and advantages of the invention will be apparent from the following detailed description that follows.
In the present invention, the terms “psychiatric profiling” or “diagnosis” are intended to include profiling such as medical, psychiatric, genetic, psychological and character profiling.
SUMMARY OF THE INVENTION
There is thus provided in the present invention 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 and extracting 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.
According to one preferred embodiment of the method, 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).
According to a preferred embodiment of the method, 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.
In one embodiment, 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. In such case, in one embodiment the detections and related messages accepted from the first output set are selected according to their likelihood of correct output detection reporting and analysis.
In another embodiment, the input image is from an MRI scanner, fMRI, MRS, PET, CAT, SPECT, EEG, laser, or other.
In another embodiment 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.
In another embodiment the pinpointing of reference points is done by use of a matching template images.
In another embodiment, known reference points are built into the input image.
In another embodiment areas and features are extracted using referencing to known given or calculated reference points.
In another embodiment the psychiatric analysis results are the profile results provided by readings of hand and foot palms.
In another embodiment 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.
In another embodiment 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.
In such case, In one embodiment the said neural network is a multi layer peceptron neural network.
In another embodiment 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.
In one embodiment the method is 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. In such case, in one embodiment, a mechanically driven and controlled blunt pin element is used to press automatically on the skin and palm mounts. In another embodiment the pressure applied is controlled and measured, rebound rate of the skin and palm mount is measured using a laser scanner.
In one embodiment auto-cropping and voting are performed by a generic algorithm in which auto-cropping and voting parameters are automatically optimized using a generic algorithm that maximizes fitness.
There is also provided in the present invention 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.