Description:FIELD OF THE INVENTION
This invention relates to Hybrid Model for Robust CT Image Denoising Using Deep Neural Networks and Statistical Approaches
BACKGROUND OF THE INVENTION
The widespread usage of Computed Tomography (CT) as medical imaging serves diagnosis and treatment planning because disease identification and therapeutic preparations require it. The existence of noise within low-dose CT scans produces severe deterioration in image quality which leads to diagnostic errors as well as higher levels of uncertainty for radiologists. Edge-preservation is usually at odds with the ability of current denoising algorithms to reduce noise which leads to loss of essential anatomical features.
In order to meet this challenge, the research is proposing the Hybrid Model for Robust CT Image Denoising Using Deep Neural Networks and Statistical Approaches. This model will combine the methodology of deep learning approach for feature learning with statistical ability to remove noises. Thus, the hybrid approach assists in identifying and eliminating noise while also retaining spatial characteristics of images through a two-fold structure. CNNs for feature learning and statistical models for noise level estimation.
The performance of the proposed solution will be measured by quantitative measures like PSNR, SSIM, and qualitative assessments. It is hoped that the model will provide a fast and scalable method for the denoising process and can be implemented on real-time clinical applications in medical imaging.
Current solutions for CT image denoising include traditional image processing algorithms, convolutional neural network (CNN)-based models, and statistical noise reduction techniques. While deep learning models like DnCNN, RED-CNN, and GAN-based approaches provide effective noise suppression, they often compromise fine image details. On the other hand, statistical methods such as non-local means (NLM) and wavelet transform maintain edge details but may struggle with high-level noise. Commercial applications primarily focus on these methods without offering a hybrid approach that leverages both deep learning and statistical analysis for robust denoising.
The shortcomings of existing CT image denoising solutions include excessive noise suppression leading to the loss of critical anatomical details, limited generalization across different datasets, and computational inefficiency for real-time clinical applications. Additionally, most models lack adaptive noise-handling mechanisms and fail to achieve a balance between noise reduction and edge preservation.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invented product Concerns a hybrid model for robust CT image denoising using deep neural networks and statistical approaches intended to improve the quality of CT images by removing noise while preserving the important anatomical features. This hybrid approach leverages the strengths of both deep learning-based feature extraction and statistical noise estimation techniques.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invented product Concerns a hybrid model for robust CT image denoising using deep neural networks and statistical approaches intended to improve the quality of CT images by removing noise while preserving the important anatomical features. This hybrid approach leverages the strengths of both deep learning-based feature extraction and statistical noise estimation techniques.
The model involves CNNs for capturing features in spatial dimensions and detecting noise in CT images. At the same time, for more accurate noise estimation, various complex methods are used moreover for the noise reduction. This makes it possible for the model to retain most of its structural features of medical images making radiological diagnosis quicker, clearer, and accurate.
Moreover, the developed system is intended to perform in the low-dose as well as in the standard-dose modalities, which makes it a promising, computationally effective solution. These evaluation measurements include the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) as well as visual assessments.
This invention is specifically useful in real-time clinical care where noise reduction is mandatory to achieve accurate diagnoses and care planning. The combination of deep learning and statistical strategies allows obtaining a highly reliable diagnosis and assessing the effectiveness of the medical imaging systems more comprehensively.
NOVELTY:
Please provide a one-sentence description of what distinguishes your idea from the prior art. This is a statement of what is new, and not a business case.
A combined deep learning based spatial feature extractor with adaptive noise estimation based statistical model to facilitate better CT image denoising with better anatomical features for diagnosis.
ADVANTAGES OF THE INVENTION
• The combined model is the Deep Reinforcement Learning Model blended with statistical models that seem to produce far less noise and preserves aspects such as anatomical shape.
• There is also an improvement on how it performs on different noise environment in both low-dose and actual-dose CT images.
• The proposed solution is computationally efficient and can therefore be implemented in real time especially in medical imaging, unlike the computationally dense models.
, Claims:1. A hybrid system for CT image denoising, comprising:
a deep neural network module configured to perform spatial feature extraction using convolutional neural networks (CNNs); and
a statistical estimation module configured to estimate and reduce noise using complex noise models;
wherein the hybrid system combines the outputs of both modules to enhance image quality while preserving anatomical features.
2. The system as claimed in claim 1, wherein the statistical estimation module uses statistical noise estimation techniques to accurately model and reduce noise in both low-dose and standard-dose CT modalities.
3. The system as claimed in claim 1, wherein the system evaluates image quality using one or more of the following metrics:
(a) Peak Signal-to-Noise Ratio (PSNR),
(b) Structural Similarity Index (SSIM), and
(c) Visual assessment by radiological experts.
4. The system as claimed in claim 1, wherein the combined use of deep learning and statistical approaches supports real-time clinical applications including accurate diagnoses, care planning, and immediate clinical decision-making.
5. The system as claimed in claim 1, wherein the system improves clinical outcomes by retaining structural fidelity of CT images, thereby enhancing diagnostic accuracy in radiological practice.