Hybrid Machine Learning System for Noise Reduction and Super Resolution in Biomedical Imaging and method thereof

Abstract

The present invention discloses a hybrid machine learning framework for noise reduction and super-resolution in biomedical imaging. The framework integrates a denoising module that uses a denoising autoencoder to reduce noise, and a super-resolution module that employs a GAN or CNN architecture to enhance image resolution. This hybrid approach adapts to various imaging modalities, including MRI, CT, ultrasound, and microscopy, and supports both supervised and unsupervised learning techniques. By improving the quality of biomedical images, the invention aids in accurate diagnosis and clinical analysis by providing noise-reduced, high-resolution images that reveal finer anatomical details. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The present invention relates to the fields of biomedical imaging, machine learning, and image processing. Specifically, this invention provides a hybrid machine learning framework designed to enhance image quality in biomedical imaging by reducing noise and increasing resolution. The invention addresses the challenges of imaging quality in biomedical applications, aiming to improve diagnostic accuracy by generating clearer, high-resolution images suitable for analysis in areas such as radiology, pathology, and microscopy.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Biomedical imaging technologies, such as MRI, CT, ultrasound, and microscopy, are essential for diagnosing and analyzing various health conditions. However, these images often suffer from noise and limited resolution, which can obscure important details and hinder accurate diagnosis. Noise in medical images typically arises due to limitations in imaging equipment, environmental conditions, and patient movement, among other factors. Traditional image processing methods for noise reduction and super-resolution often require substantial computational resources and may not generalize well across different types of imaging.
[004] Machine learning, particularly deep learning, has shown significant promise in addressing these limitations. While individual machine learning techniques, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have been applied for either noise reduction or super-resolution, a combined approach could offer superior results. This invention proposes a hybrid framework that leverages the strengths of multiple machine learning models to reduce noise and enhance resolution in biomedical images, thus offering a more comprehensive solution for image quality enhancement.
[005] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides a Hybrid Machine Learning System for Noise Reduction and Super Resolution in Biomedical Imaging and method thereof. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[006] This invention introduces a hybrid machine learning framework for improving the quality of biomedical images through noise reduction and super-resolution. The framework combines various machine learning models, including denoising autoencoders, convolutional neural networks (CNNs), and generative adversarial networks (GANs), to achieve optimal results. The system operates by first applying noise reduction to the input image using a denoising autoencoder. Next, the image is processed through a super-resolution module based on GANs or CNNs, which enhances the resolution by reconstructing finer details.
[007] The hybrid framework is designed to handle different imaging modalities, such as MRI, CT, ultrasound, and microscopy, making it adaptable across various biomedical fields. Moreover, it is capable of learning from both labeled and unlabeled data, improving its robustness and effectiveness in diverse clinical settings. This combination of models in a sequential, hybrid approach provides a solution that addresses both noise and resolution limitations in biomedical images, resulting in clearer and more detailed images that can improve diagnostic accuracy and aid in clinical decision-making.
[008] The integration of these components enables the system to deliver a contextually aware, highly responsive AR experience. For example, in applications like healthcare training simulations, the system can adjust image resolution and augment specific anatomical areas based on trainee interactions. In outdoor gaming scenarios, it can dynamically scale virtual characters and scenery to match user movements and maintain immersion. This adaptability ensures that the AR experience remains smooth, engaging, and practical across a wide range of devices and environments.
[009] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[010] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[011] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
FIG. 1: Block diagram of the hybrid machine learning framework for noise reduction and super-resolution in biomedical imaging.
FIG. 2: Flowchart of the denoising module, detailing the denoising autoencoder and its processing steps.
FIG. 3: Flowchart of the super-resolution module, detailing the processing steps of GANs and CNNs for enhancing image resolution.
FIG. 4: Schematic diagram showing the hybrid framework's adaptability across different biomedical imaging modalities.


DETAILED DESCRIPTION OF THE INVENTION
[012] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[013] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[014] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR.
System Architecture (FIG. 1)
[015] The hybrid machine learning framework consists of two main modules: a denoising module and a super-resolution module. The denoising module utilizes a denoising autoencoder that preprocesses the input image to remove noise, while preserving essential structural details. Following this, the super-resolution module enhances the processed image's resolution by reconstructing finer details through a combination of GAN and CNN architectures.
[016] Denoising Module (FIG. 2):
This module comprises a denoising autoencoder that receives the input biomedical image and removes various types of noise, including Gaussian, salt-and-pepper, and Poisson noise. The denoising autoencoder is trained on large datasets of noisy and clean biomedical images, allowing it to effectively differentiate between noise and signal.
Once noise is removed, the denoising autoencoder passes the noise-free image to the super-resolution module for further processing.
[017] Super-Resolution Module (FIG. 3):
a) The super-resolution module utilizes a GAN-based network that has been trained to reconstruct high-resolution images from lower-resolution inputs. This GAN architecture consists of a generator and a discriminator. The generator network creates a high-resolution image, while the discriminator evaluates the image quality by comparing it to actual high-resolution images.
b) Additionally, a CNN-based approach may be employed in cases where computational efficiency is prioritized. The CNN applies various layers of convolutions to enhance resolution by learning spatial features, texture details, and edge information.
[018] Integration and Adaptability (FIG. 4):
a. The system is adaptable across various biomedical imaging modalities. For example, it is compatible with MRI images, where signal-to-noise ratios vary significantly, as well as with microscopy images, which require high levels of detail.
b. The framework is capable of both supervised and unsupervised learning. In supervised scenarios, labeled pairs of noisy and high-resolution images are used to train the model, while in unsupervised cases, unlabeled images allow the framework to learn based on inherent image patterns and textures.
[019] Output and Evaluation (FIG. 5):
The final output of the framework is a high-quality biomedical image with reduced noise and enhanced resolution. Comparative studies show significant improvements in visual clarity and detail, providing better diagnostic information for clinical professionals.
Workflow
[020] Data Preprocessing:
The input image undergoes preprocessing to normalize intensity values, which helps standardize the data before feeding it into the machine learning models.
[021] Noise Reduction Process (FIG. 2):
The denoising module first uses the denoising autoencoder to remove noise from the image. This autoencoder has been trained to identify and eliminate various types of biomedical imaging noise while preserving important anatomical structures.
[022] Super-Resolution Process (FIG. 3):
i. The denoised image is then processed through the super-resolution module, where a GAN or CNN model reconstructs a high-resolution version of the image.
ii. The GAN-based approach allows for high-detail reconstruction through adversarial learning, where the generator creates realistic high-resolution images, and the discriminator ensures these images are comparable to actual high-resolution images.
[023] Output Image Evaluation:
The final high-resolution image is compared against ground-truth high-resolution images in cases of supervised learning. Metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are calculated to evaluate performance.
[024] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[025] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[026] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. A hybrid machine learning framework for enhancing biomedical images, comprising a denoising module and a super-resolution module, where the denoising module uses a denoising autoencoder for noise reduction and the super-resolution module employs a GAN or CNN architecture for increasing image resolution.
2. The framework of claim 1, wherein the denoising module is configured to remove Gaussian, salt-and-pepper, and Poisson noise from biomedical images.
3. The framework of claim 1, wherein the super-resolution module employs a GAN comprising a generator network and a discriminator network, where the generator reconstructs a high-resolution image, and the discriminator assesses the image quality.
4. The framework of claim 1, wherein the denoising module and the super-resolution module are trained using a combination of supervised and unsupervised learning techniques.
5. The framework of claim 1, wherein the super-resolution module is adaptable to different biomedical imaging modalities, including MRI, CT, ultrasound, and microscopy.
6. The framework of claim 1, further comprising an evaluation module that calculates performance metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), to assess the output image quality.
7. The framework of claim 3, wherein the super-resolution module employs convolutional neural networks to increase image resolution in a computationally efficient manner.

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