Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of artificial intelligence-driven medical image processing and diagnostic systems, more specifically, relates to a cellular logic processing system for analyzing MRI scanned images of human brain for early detection and multi-class classification of neurodegenerative diseases based on the integration of cellular automata algorithms, decision theory-based classification, and advanced pattern recognition techniques.
BACKGROUND OF THE DISCLOSURE
[0002] Neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's disease represent a growing global health challenge, affecting millions of individuals worldwide and imposing significant economic and social burdens on healthcare systems. These progressive and debilitating conditions are characterized by the gradual loss of neurons and cognitive decline, making early diagnosis crucial for effective patient management and treatment intervention. Magnetic Resonance Imaging (MRI) serves as a primary diagnostic tool for visualizing structural brain changes associated with neurodegeneration; however, current diagnostic approaches face substantial limitations in accuracy, efficiency, and multi-class disease differentiation.
[0003] Traditional diagnostic methods rely heavily on manual interpretation of MRI scans by clinicians, a process that is inherently subjective and prone to inter-observer variability. The subtle and overlapping nature of early disease markers across different neurodegenerative disorders further complicates accurate diagnosis, particularly in the initial stages when therapeutic interventions could be most effective. Current automated image analysis systems and machine learning approaches, while showing promise, often struggle with moderate accuracy and primarily focus on binary classification tasks, simply detecting the presence or absence of a single disease rather than distinguishing between multiple neurodegenerative conditions simultaneously.
[0004] Existing image processing methods typically rely on pixel-based or thresholding techniques that fail to fully capture the complex spatial patterns and morphological characteristics found in high-dimensional MRI data. While deep learning approaches demonstrate powerful capabilities, they demand large annotated datasets and substantial computational resources, making their practical application in clinical settings challenging. Additionally, current systems lack the integration of advanced cellular automata-based image processing with formal decision theory frameworks that could enhance diagnostic accuracy while minimizing computational complexity.
[0005] The cellular logic array processing (CLAP) approach, which utilizes cellular automata and Markov algorithms for pattern-driven relational analysis, offers a promising but underexplored methodology for extracting high-precision features from medical images. This technique could potentially bridge the gap between computational efficiency and diagnostic accuracy when properly integrated with AI-based classification systems.
[0006] Current commercial diagnostic solutions, including volumetric analysis tools like NeuroQuant and AI-driven platforms such as QMENTA and Viz.ai, provide limited multi-class diagnostic capabilities and do not incorporate the advanced cellular logic processing methodologies proposed in this invention. These existing systems predominantly focus on specific conditions or volumetric measurements without offering comprehensive decision support for multiple neurodegenerative diseases simultaneously.
[0007] Therefore, there exists a critical need for a comprehensive, scalable system that combines cellular logic-based image processing with decision theory-driven AI classifiers to enhance the sensitivity and specificity of neurodegenerative disease diagnosis. Such a system would support early detection, multi-class classification, continuous patient monitoring, and provide actionable clinical insights to improve patient outcomes and facilitate personalized treatment strategies.
[0008] Thus, in light of the above-stated discussion, there exists a need for a cellular logic processing system for analyzing MRI scanned images of human brain for early detection and multi-class classification of neurodegenerative diseases.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on a cellular logic processing system for analyzing MRI scanned images of human brain for early detection and multi-class classification of neurodegenerative diseases which overcomes the above-mentioned disadvantages and provides users with enhanced diagnostic capabilities.
[0011] The present invention solves the major limitations of conventional MRI-based diagnostic systems through intelligent cellular logic array processing and decision theory-based AI classification frameworks.
[0012] The primary objective of the present disclosure is to provide a system for early detection and classification of neurodegenerative diseases using cellular logic processing of MRI scanned images that integrates pattern-directed cellular automata, Markov algorithms, and decision theory-based multi-class classification.
[0013] Another objective of the present disclosure is to enable high-precision feature extraction from both 2D and 3D MRI data using cellular logic array processing with convex scanning windows for comprehensive brain tissue analysis and pathological region identification.
[0014] Another objective of the present disclosure is to implement decision theory-based AI classifiers that utilize binary and M-ary hypothesis testing to minimize diagnostic risk and improve multi-class disease differentiation accuracy.
[0015] Another objective of the present disclosure is to provide automated preprocessing capabilities including noise reduction, normalization, and spatial alignment of MRI images to standard brain templates for consistent analysis across different datasets.
[0016] Yet another objective of the present disclosure is to integrate intelligent clinical decision support mechanisms that generate personalized treatment strategies and patient monitoring recommendations based on classification results and disease progression analysis.
[0017] Yet another objective of the present disclosure is to enhance computational efficiency by utilizing cellular automata-based processing techniques that reduce complexity compared to traditional numerical methods while maintaining high diagnostic accuracy.
[0018] Yet another objective of the present disclosure is to develop a scalable system capable of handling both T1-weighted and FLAIR MRI imaging data with real-time processing capabilities suitable for clinical workflow integration.
[0019] In light of the above, in one aspect of the present disclosure, a system for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain is disclosed herein. The system comprises an MRI data acquisition interface configured to receive T1-weighted and FLAIR MRI scan data from medical imaging devices, a user interface integrated into a clinical workstation configured to obtain MRI scan inputs and display diagnostic results to healthcare professionals, and a communication network configured to transmit data between all components of the system. The system also includes a processing unit connected to the MRI data acquisition interface and the user interface via the communication network, configured to process MRI imaging data using cellular logic array processing and generate multi-class neurodegenerative disease classifications. The processing unit further comprises a data input module configured to receive MRI scan data, a preprocessing module configured to perform noise reduction, normalization, and spatial alignment, a cellular logic array processing module configured to apply pattern-directed cellular automata rules with Markov algorithm substitution formulas, a feature extraction module configured to extract neuroimaging biomarkers, a classification module configured to implement machine learning algorithms, a disease prediction module configured to generate posterior probabilities, an alert generation module configured to generate diagnostic notifications, a decision support module configured to generate personalized treatment strategies, and an output module configured to transmit processed diagnostic data. The system also includes a medical database connected to the processing unit via the communication network and configured to store processed MRI data and classification models.
[0020] In one embodiment, the MRI data acquisition interface comprises T1-weighted image receptors, FLAIR image receptors, and volumetric data processors configured to handle both 2D slice data and 3D brain imaging data.
[0021] In one embodiment, the user interface is further configured to display real-time diagnostic dashboard, disease classification outputs with probability scores, progression metrics visualization, and clinical management recommendations for comprehensive neurodegenerative disease assessment.
[0022] In one embodiment, the processing unit further comprises a training and testing module configured to implement model training and validation for cellular logic processing optimization and disease classification accuracy enhancement.
[0023] In one embodiment, the cellular logic array processing module is configured to apply convex scanning windows for pixel neighbourhood analysis, calculate pixel intensity differences, and perform threshold-based replacements to highlight brain tissue boundaries and pathological regions.
[0024] In one embodiment, the feature extraction module is configured to analyze neuroimaging biomarkers including brain atrophy patterns, white matter lesions, tissue boundary characteristics, and volumetric measurements from cellular logic processed MRI data.
[0025] In light of the above, in another aspect of the present disclosure, a method for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain is disclosed herein. The method comprises the steps of receiving MRI scan data from medical imaging devices, displaying diagnostic interfaces via a user interface, transmitting data between system components, processing MRI imaging data using cellular logic array processing, receiving MRI scan data via a data input module, preprocessing MRI images through noise reduction and normalization, training and validating processing models, applying pattern-directed cellular automata rules, extracting neuroimaging biomarkers, classifying extracted features using machine learning algorithms, generating posterior probabilities for multiple diseases, generating diagnostic notifications, generating personalized treatment strategies, and transmitting processed diagnostic data to the user interface.
[0026] In one embodiment, the method further comprises implementing slice-by-slice 2D edge detection using convex scanning windows and 3D volumetric integration for comprehensive spatial analysis of brain morphology and degeneration patterns.
[0027] In one embodiment, the method further comprises performing binary and M-ary hypothesis testing based on Bayes' Criterion to reduce diagnostic risk and optimize multi-class classification decisions for distinguishing among multiple neurodegenerative diseases.
[0028] These and other advantages will be apparent from the present application of the embodiments described herein.
[0029] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0030] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0032] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0033] FIG. 1 illustrates a block diagram of a system for early detection and classification of neurodegenerative diseases using MRI scanned images in accordance with an exemplary embodiment of the present disclosure;
[0034] FIG. 2 illustrates a flowchart of a method for early detection and classification of neurodegenerative diseases using MRI scanned images, outlining the sequential steps employed in accordance with an exemplary embodiment of the present disclosure; and
[0035] FIG. 3 illustrates comparative MRI imaging analysis showing (a) posterior cortical atrophy and typical Alzheimer's disease patterns, and (b) detailed brain tissue analysis with healthy patterns, AD features, and various neuroimaging biomarkers including symmetrical atrophy, hippocampus atrophy, temporal lobe atrophy, and expanded fissures in accordance with an exemplary embodiment of the present disclosure.
[0036] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0037] The MRI-Based Neurodegenerative Disease Detection System is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0038] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0039] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0040] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0041] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0042] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0043] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a system 100 for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain, in accordance with an exemplary embodiment of the present disclosure.
[0044] The system 100 comprises an MRI data acquisition interface 102, a user interface 104, a communication network 108, a processing unit 110, and a medical database 132.
[0045] The MRI data acquisition interface 102 is configured to receive T1-weighted and FLAIR MRI scan data from medical imaging devices. The MRI data acquisition interface 102 serves as the primary input gateway for neuroimaging data, enabling seamless integration with various medical imaging equipment and ensuring high-quality data acquisition for subsequent processing.
[0046] In one embodiment of the present invention, the MRI data acquisition interface 102 comprises T1-weighted image receptors 134, FLAIR image receptors 136, and volumetric data processors 138 configured to handle both 2D slice data and 3D brain imaging data. The T1-weighted image receptors 134 are specifically designed to capture anatomical brain structures with high contrast resolution, providing detailed visualization of gray matter, white matter, and cerebrospinal fluid boundaries. The FLAIR image receptors 136 are optimized for detecting white matter lesions and pathological changes by suppressing cerebrospinal fluid signals while enhancing tissue contrast. The volumetric data processors 138 integrate multiple 2D slices into comprehensive 3D brain models, enabling spatial analysis of brain morphology and volumetric measurements.
[0047] In one embodiment of the present invention, the MRI data acquisition interface 102 implements standardized imaging protocols including DICOM format compatibility, ensuring seamless integration with existing medical imaging infrastructure and maintaining data integrity throughout the acquisition process.
[0048] The user interface 104 is integrated into a clinical workstation 106 and configured to obtain MRI scan inputs and display diagnostic results to healthcare professionals. The user interface 104 provides an intuitive and comprehensive platform for clinical interaction, enabling efficient data input, analysis control, and result interpretation.
[0049] In one embodiment of the present invention, the user interface 104 is further configured to display real-time diagnostic dashboard, disease classification outputs with probability scores, progression metrics visualization, and clinical management recommendations for comprehensive neurodegenerative disease assessment. The diagnostic dashboard presents multi-dimensional visualization of brain analysis results, including anatomical overlays, statistical summaries, and comparative analysis with normative databases.
[0050] In one embodiment of the present invention, the user interface 104 incorporates interactive visualization tools that allow clinicians to explore different brain regions, adjust visualization parameters, and access detailed explanations of diagnostic findings. The interface supports multiple display modes including axial, sagittal, and coronal views, as well as 3D brain renderings with pathological region highlighting.
[0051] The communication network 108 is configured to transmit data between all components of the system 100, ensuring secure and efficient data flow throughout the diagnostic pipeline. The communication network 108 facilitates real-time data exchange while maintaining data integrity and security standards required for medical applications.
[0052] In one embodiment of the present invention, the communication network 108 comprises secure medical-grade communication protocols including HL7 FHIR standards, encrypted data transmission channels, and redundant network pathways to ensure continuous system operation and data protection.
[0053] The processing unit 110 is connected to the MRI data acquisition interface 102 and the user interface 104 via the communication network 108, and configured to process MRI imaging data using cellular logic array processing and generate multi-class neurodegenerative disease classifications. The processing unit 110 represents the core intelligence of the system 100, orchestrating advanced image processing and AI-driven diagnostic analysis.
[0054] The processing unit 110 comprises multiple specialized modules working collaboratively: a data input module 112, a preprocessing module 114, a training and testing module 116, a cellular logic array processing module 118, a feature extraction module 120, a classification module 122, a disease prediction module 124, an alert generation module 126, a decision support module 128, and an output module 130.
[0055] The data input module 112 is configured to receive MRI scan data from the MRI data acquisition interface 102. The data input module 112 handles data validation, format verification, and initial quality assessment to ensure optimal input for subsequent processing stages.
[0056] In one embodiment of the present invention, the data input module 112 implements automated data quality assessment mechanisms including signal-to-noise ratio evaluation, motion artifact detection, and spatial resolution verification to ensure optimal processing conditions.
[0057] The preprocessing module 114 is configured to perform noise reduction, normalization, and spatial alignment of MRI images to standard brain templates. This module ensures data consistency and quality across different imaging protocols and equipment.
[0058] In one embodiment of the present invention, the preprocessing module 114 implements advanced noise reduction algorithms including non-local means filtering and anisotropic diffusion techniques, intensity normalization procedures using histogram matching and N4 bias field correction, and spatial alignment through affine and non-rigid registration to standard brain atlases such as MNI152 and Talairach templates.
[0059] The training and testing module 116 is configured to implement model training and validation for cellular logic processing optimization and disease classification accuracy enhancement. This module ensures continuous improvement of diagnostic performance through machine learning adaptation.
[0060] In one embodiment of the present invention, the training and testing module 116 employs cross-validation techniques including k-fold validation and leave-one-out validation, implements automated hyperparameter optimization using grid search and Bayesian optimization methods, and maintains separate datasets for training, validation, and testing to ensure robust model performance evaluation.
[0061] The cellular logic array processing module 118 is configured to apply pattern-directed cellular automata rules with Markov algorithm substitution formulas for slice-by-slice 2D edge detection using convex scanning windows and 3D volumetric integration. This module represents the core innovation of the system, implementing advanced cellular automata techniques for high-precision feature extraction.
[0062] In one embodiment of the present invention, the cellular logic array processing module 118 is configured to apply convex scanning windows for pixel neighborhood analysis, calculate pixel intensity differences, and perform threshold-based replacements to highlight brain tissue boundaries and pathological regions. The module implements various scanning window geometries including 3×3, 5×5, and adaptive window sizes based on local image characteristics.
[0063] In one embodiment of the present invention, the cellular logic array processing module 118 employs pattern-directed cellular automata rules that analyze pixel neighborhoods using Moore and von Neumann neighborhood configurations, applies Markov algorithm substitution formulas for edge enhancement and noise suppression, and integrates multiple 2D processed slices into comprehensive 3D brain models for volumetric analysis.
[0064] The feature extraction module 120 is configured to extract neuroimaging biomarkers and morphological features from cellular logic processed MRI data. This module transforms processed imaging data into quantitative features suitable for AI classification.
[0065] In one embodiment of the present invention, the feature extraction module 120 is configured to analyze neuroimaging biomarkers including but not limited to brain atrophy patterns, white matter lesions, tissue boundary characteristics, and volumetric measurements from cellular logic processed MRI data. The module quantifies morphological changes including cortical thickness measurements, ventricular volume analysis, and regional brain volume assessments.
[0066] In one embodiment of the present invention, the feature extraction module 120 implements advanced feature extraction techniques including texture analysis using gray-level co-occurrence matrices, shape descriptors for anatomical structures, and statistical moment calculations for intensity distribution characterization.
[0067] The classification module 122 is configured to implement machine learning algorithms for multi-class disease classification. This module applies advanced AI techniques to differentiate between multiple neurodegenerative diseases based on extracted features.
[0068] In one embodiment of the present invention, the classification module 122 is configured to classify extracted features into multiple predefined neurodegenerative disease categories including but not limited to Alzheimer's disease, Parkinson's disease, Huntington's disease, and healthy control classifications using machine learning frameworks. The module employs ensemble methods combining support vector machines, random forests, and neural networks for robust classification performance.
[0069] In one embodiment of the present invention, the classification module 122 implements decision theory-based classification using binary and M-ary hypothesis testing frameworks, applies Bayesian decision criteria for risk minimization, and employs confidence scoring mechanisms to quantify classification certainty.
[0070] The disease prediction module 124 is configured to generate posterior probabilities for multiple neurodegenerative diseases and produce confidence scores for diagnostic classifications. This module provides probabilistic assessment of disease likelihood based on imaging evidence.
[0071] In one embodiment of the present invention, the disease prediction module 124 implements Bayesian inference techniques for probability calculation, applies statistical significance testing for diagnostic confidence assessment, and generates risk stratification scores for clinical decision support.
[0072] The alert generation module 126 is configured to generate diagnostic notifications and clinical management recommendations for detected neurodegenerative conditions. This module provides immediate notification of significant findings and suggested clinical actions.
[0073] In one embodiment of the present invention, the alert generation module 126 generates priority-based alerts for urgent findings, provides automated report generation with standardized medical terminology, and implements escalation protocols for critical diagnostic findings.
[0074] The decision support module 128 is configured to generate personalized treatment strategies and patient monitoring recommendations based on classification results and disease progression analysis. This module integrates diagnostic findings with clinical guidelines to provide actionable recommendations.
[0075] In one embodiment of the present invention, the decision support module 128 is configured to analyze classification results and generate targeted recommendations for patient monitoring schedules, treatment intervention strategies, and personalized management protocols based on specific neurodegenerative disease types and progression stages.
[0076] The output module 130 is configured to transmit processed diagnostic data, disease classification results, risk assessments, and clinical recommendations to the user interface 104. This module ensures comprehensive delivery of analysis results in clinically useful formats.
[0077] In one embodiment of the present invention, the output module 130 implements standardized medical reporting formats, provides integration capabilities with electronic health record systems, and supports multiple output formats including PDF reports, DICOM structured reports, and HL7 FHIR resources.
[0078] The medical database 132 is connected to the processing unit 110 via the communication network 108 and configured to store and retrieve processed MRI data, disease classification models, clinical decision support algorithms, and historical patient diagnostic data.
[0079] In one embodiment of the present invention, the medical database 132 implements distributed storage architecture with data encryption, backup and recovery mechanisms, and audit trails for regulatory compliance. The database supports both structured and unstructured data storage for comprehensive patient information management.
[0080] FIG. 2 illustrates a flowchart of the method 200 for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain, in accordance with an exemplary embodiment of the present disclosure.
[0081] The method 200 comprises the following sequential steps: at step 202, receiving T1-weighted and FLAIR MRI scan data from medical imaging devices via an MRI data acquisition interface 102, ensuring high-quality neuroimaging data input for subsequent analysis; at step 204, displaying diagnostic interfaces and receiving clinical queries via a user interface 104 integrated into a clinical workstation 106, providing clinicians with intuitive access to system capabilities; at step 206, transmitting data between all system components via a communication network 108, maintaining secure and efficient data flow throughout the processing pipeline; at step 208, processing MRI imaging data using cellular logic array processing and generating multi-class disease classifications via a processing unit 110 connected to the MRI data acquisition interface 102 and user interface 104; at step 210, receiving MRI scan data from the MRI data acquisition interface 102 via a data input module 112, initiating the diagnostic analysis workflow; at step 212, preprocessing MRI images through noise reduction, normalization, and spatial alignment via a preprocessing module 114, ensuring optimal data quality for analysis; at step 214, training and validating cellular logic processing and classification models via a training and testing module 116, maintaining system accuracy and performance; at step 216, applying pattern-directed cellular automata rules with Markov algorithm substitution formulas and convex scanning windows for slice-by-slice 2D edge detection via a cellular logic array processing module 118, implementing the core feature extraction methodology; at step 218, extracting neuroimaging biomarkers and morphological features from processed data via a feature extraction module 120, quantifying disease-relevant characteristics; at step 220, classifying extracted features using machine learning algorithms via a classification module 122, determining disease probabilities and classifications; at step 222, generating posterior probabilities for multiple neurodegenerative diseases via a disease prediction module 124, providing quantitative diagnostic assessments; at step 224, generating diagnostic notifications and clinical management recommendations via an alert generation module 126, ensuring timely communication of findings; at step 226, generating personalized treatment strategies and monitoring recommendations via a decision support module 128, supporting clinical decision-making; and at step 228, transmitting processed diagnostic data, classification results, risk assessments, and clinical recommendations to the user interface 104 via an output module 130, completing the diagnostic workflow.
[0082] FIG. 3 illustrates comparative MRI imaging analysis in accordance with an exemplary embodiment of the present disclosure. FIG. 3(a) demonstrates the system's capability to analyze different types of brain atrophy patterns, specifically showing posterior cortical atrophy (images A and B) compared to typical Alzheimer's disease patterns (images C and D). The posterior cortical atrophy presentation shows characteristic parietal and occipital cortex involvement with preserved hippocampal structures in early stages, while typical Alzheimer's disease demonstrates the classic pattern of medial temporal lobe atrophy with hippocampal involvement.
[0083] FIG. 3(b) presents a comprehensive analysis framework showing the system's multi-modal diagnostic capabilities. The figure displays healthy brain patterns alongside AD (Alzheimer's Disease) features, demonstrating the system's ability to distinguish between normal and pathological brain states. The figure illustrates various neuroimaging biomarkers including symmetrical atrophy patterns on the frontal lobes, hippocampus atrophy which is a hallmark of Alzheimer's disease progression, temporal lobe atrophy indicating advanced neurodegeneration, and expanded fissures representing cortical volume loss. This comprehensive visualization demonstrates how the cellular logic array processing module 118 effectively identifies and quantifies multiple pathological features simultaneously, enabling accurate multi-class disease classification through the integration of diverse neuroimaging biomarkers.
[0084] The system's analysis of these complex imaging patterns through cellular logic array processing enables precise identification of subtle morphological changes that may be overlooked by conventional analysis methods, supporting early detection and accurate classification of neurodegenerative diseases across their various presentations and stages.
[0085] In the best mode of operation, the system 100 operates through coordinated functioning of all components to deliver optimal diagnostic performance for neurodegenerative disease detection. Upon system initialization, the MRI data acquisition interface 102 establishes connections with medical imaging equipment and begins receiving T1-weighted and FLAIR MRI scan data. The data input module 112 performs initial quality assessment and validation before forwarding data to the preprocessing module 114, which applies noise reduction, intensity normalization, and spatial alignment to standard brain templates. The training and testing module 116 continuously update machine learning models based on new patient data and validation results, ensuring optimal diagnostic accuracy.
[0086] The cellular logic array processing module 118 represents the core innovation, applying pattern-directed cellular automata rules with convex scanning windows to perform slice-by-slice 2D edge detection. This process identifies brain tissue boundaries and pathological regions with high precision, followed by 3D volumetric integration to create comprehensive brain models. The feature extraction module 120 then analyzes the processed data to extract neuroimaging biomarkers including atrophy patterns, white matter lesions, and morphological characteristics.
[0087] The classification module 122 employs machine learning algorithms to perform multi-class disease classification, distinguishing between Alzheimer's disease, Parkinson's disease, Huntington's disease, and healthy controls. The disease prediction module 124 generates posterior probabilities using decision theory-based frameworks, while the alert generation module 126 produces diagnostic notifications for significant findings.
[0088] The decision support module 128 analyzes classification results to generate personalized treatment strategies and monitoring recommendations, integrating diagnostic findings with clinical guidelines. Throughout the process, the medical database 132 stores processed data, trained models, and historical information to support continuous learning and improvement.
[0089] The user interface 104 presents result through an intuitive diagnostic dashboard, displaying disease classification outputs with probability scores, progression metrics visualization, and clinical management recommendations. This comprehensive approach ensures that healthcare professionals receive actionable insights for improved patient care and treatment planning.
[0090] The present invention offers a technologically advanced cellular logic processing system 100 that integrates pattern-directed cellular automata, Markov algorithms, and decision theory-based AI classification to overcome the limitations of conventional MRI-based diagnostic approaches. By utilizing cellular logic array processing techniques, the system 100 ensures enhanced diagnostic accuracy, improved computational efficiency, multi-class disease classification capabilities, and seamless clinical workflow integration across diverse healthcare environments.
[0091] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0092] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0093] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0094] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0095] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A system (100) for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain, the system (100) comprising:
an MRI data acquisition interface (102) configured to receive T1-weighted and FLAIR MRI scan data from medical imaging devices;
a user interface (104) integrated into a clinical workstation (106), the user interface (104) configured to obtain MRI scan inputs and display diagnostic results to healthcare professionals;
a communication network (108) configured to transmit data between all components of the system (100);
a processing unit (110) connected to the MRI data acquisition interface (102) and the user interface (104) via the communication network (108), the processing unit (110) configured to process MRI imaging data using cellular logic array processing and generate multi-class neurodegenerative disease classifications, wherein the processing unit (110) further comprising:
a data input module (112) configured to receive MRI scan data from the MRI data acquisition interface (102);
a preprocessing module (114) configured to perform noise reduction, normalization, and spatial alignment of MRI images to standard brain templates;
a cellular logic array processing module (118) configured to apply pattern-directed cellular automata rules with Markov algorithm substitution formulas for slice-by-slice 2D edge detection using convex scanning windows and 3D volumetric integration;
a feature extraction module (120) configured to extract neuroimaging biomarkers and morphological features from cellular logic processed MRI data;
a classification module (122) configured to implement machine learning algorithms for multi-class disease classification;
a disease prediction module (124) configured to generate posterior probabilities for multiple neurodegenerative diseases and produce confidence scores for diagnostic classifications;
an alert generation module (126) configured to generate diagnostic notifications and clinical management recommendations for detected neurodegenerative conditions;
a decision support module (128) configured to generate personalized treatment strategies and patient monitoring recommendations based on classification results and disease progression analysis; and
an output module (130) configured to transmit processed diagnostic data, disease classification results, risk assessments, and clinical recommendations to the user interface (104).
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a medical database (132) connected to the processing unit (110) via the communication network (108) and configured to store and retrieve processed MRI data, disease classification models, clinical decision support algorithms, and historical patient diagnostic data.
3. The system (100) as claimed in claim 1, wherein the MRI data acquisition interface (102) comprises T1-weighted image receptors (134), FLAIR image receptors (136), and volumetric data processors (138) configured to handle both 2D slice data and 3D brain imaging data.
4. The system (100) as claimed in claim 1, wherein the user interface (104) is further configured to display real-time diagnostic dashboard, disease classification outputs with probability scores, progression metrics visualization, and clinical management recommendations for comprehensive neurodegenerative disease assessment.
5. The system (100) as claimed in claim 1, wherein the processing unit (110) further comprises a training and testing module (116) configured to implement model training and validation for cellular logic processing optimization and disease classification accuracy enhancement.
6. The system (100) as claimed in claim 1, wherein the cellular logic array processing module (118) is configured to apply convex scanning windows for pixel neighbourhood analysis, calculate pixel intensity differences, and perform threshold-based replacements to highlight brain tissue boundaries and pathological regions.
7. The system (100) as claimed in claim 1, wherein the decision support module (128) is configured to analyze classification results and generate targeted recommendations for patient monitoring schedules, treatment intervention strategies, and personalized management protocols based on specific neurodegenerative disease types and progression stages.
8. The system (100) as claimed in claim 1, wherein the feature extraction module (120) is configured to analyze neuroimaging biomarkers including but not limited to brain atrophy patterns, white matter lesions, tissue boundary characteristics, and volumetric measurements from cellular logic processed MRI data.
9. The system (100) as claimed in claim 1, wherein the classification module (122) is configured to classify extracted features into multiple predefined neurodegenerative disease categories including but not limited to Alzheimer's disease, Parkinson's disease, Huntington's disease, and healthy control classifications using machine learning frameworks.
10. A method for early detection and classification of neurodegenerative diseases using MRI scanned images of human brain, the method comprising the steps of:
receiving T1-weighted and FLAIR MRI scan data from medical imaging devices via an MRI data acquisition interface (102);
displaying diagnostic interfaces and receiving clinical queries via a user interface (104) integrated into a clinical workstation (106);
transmitting data between all system components via a communication network (108);
processing MRI imaging data using cellular logic array processing and generating multi-class disease classifications via a processing unit (110) connected to the MRI data acquisition interface (102) and user interface (104);
receiving MRI scan data from the MRI data acquisition interface (102) via a data input module (112);
preprocessing MRI images through noise reduction, normalization, and spatial alignment via a preprocessing module (114);
training and validating cellular logic processing and classification models via a training and testing module (116);
applying pattern-directed cellular automata rules with Markov algorithm substitution formulas and convex scanning windows for slice-by-slice 2D edge detection via a cellular logic array processing module (118);
extracting neuroimaging biomarkers and morphological features from processed data via a feature extraction module (120);
classifying extracted features using machine learning algorithms via a classification module (122);
generating posterior probabilities for multiple neurodegenerative diseases via a disease prediction module (124);
generating diagnostic notifications and clinical management recommendations via an alert generation module (126);
generating personalized treatment strategies and monitoring recommendations via a decision support module (128);
transmitting processed diagnostic data, classification results, risk assessments, and clinical recommendations to the user interface (104) via an output module (130).