Description:FIELD OF THE INVENTION
This invention relates to noise-resistant and contrast-enhanced preprocessing framework for improved tb detection from medical images.
BACKGROUND OF THE INVENTION
Tuberculosis remains a global disease problem, particularly to third-world countries where timely and appropriate diagnostics have never been available. Chest X-rays are used most commonly as a screening device for TB; however, their sensitivity is compromised by low-quality images caused by noise, contrast, and variability of imaging equipment and patient positioning. All these restrictions limit the performance of radiologists as well as AI diagnosis systems.
Preprocessing techniques available are too intense or too non-specific for field usage. They cannot process on-the-move low-resolution images of X-ray machines and are not capable of recognizing TB-hood patterns. This results in compromised diagnostic sensitivity, uncontrollable rates of false positives and false negatives, and inconsistent performances across various groups of patients.
The invention aims to meet such challenges by providing an intelligent, adaptive, and TB-focused preprocessing system eliminating good image quality, relevant feature enhancement, and artifact and noise removal. Through the provision of standardized image inputs, it dramatically increases the accuracy of any follow-up diagnostic process. The solution is most crucial in rural, underfunded hospitals lacking advanced diagnostic equipment but with high TB rates.
VARIATIONS AND ALTERNATE EMBODIMENTS
There exist various embodiments of using the invention to fulfill certain operation needs and hardware limitations. Any such given embodiment may be employed to facilitate remote collaboration using rural TB screening camp-based mobile X-ray machines. In this use, hardware for preprocessing is integrated in the firmware of the device to enhance the real-time image quality before transmission of the image over for remote diagnosis.
A totally different deployment would, however, be feasible as an add-on plug-in module of installed PACS in hospitals, pre-processing archived or captured real-time images. Cloud deployment would perhaps allow batch pre-processing of TB data sets to facilitate research and epidemiologic analysis to be carried out for training AI models.
Another deployment is using deep learning-capable modules for intelligent feature extraction, pretrained convolutional network over feature TB-related after preprocessing. System versions can also be created to process various image modalities such as digital radiography and computed tomography.
But another is a mobile phone application that preprocessed X-ray images on phone-based X-ray viewers for rapid triaging. Adaptive configurations like these ensure use of the invention in an enormously vast range of healthcare infrastructure from hospital deployment in urban environments to field usage in off-site facilities.

KEY FEATURES
•Noise Reduction Module: Uses hybrid filtering algorithms, being blends of bilateral, median, and wavelet-based denoising, which eliminate artifacts while not modifying major edges and textures.
• Contrast Enhancement Unit: Uses adaptive histogram equalization and region-based tone mapping to intensify TB-specific regions in order to highlight them.
• Lung Region Segmentation Engine: Segments pulmonary regions automatically based on anatomical features in a way such that diagnostically relevant regions remain central.
•Feature Extraction Layer: Enhances structural detail such as nodules, infiltrates, and cavitations using gradient-based and morphological filtering.
•Image Quality Analyzer: Converts input image quality specifications (e.g., sharpness, noise level, exposure) into preprocess parameter values to satisfy them.
•Integration Interface: Facilitates easy integration with installed diagnostic hardware, AI software, and image libraries.
•User Configuration Panel: Allows clinicians to define enhancement parameters for private or special case usage.
These are blended in a very optimized way to provide a very optimized preprocessing pipeline with a very optimized image and diagnostic data quality at the expense of no efficiency and usability in several clinical environments.

Technology
Given-above anomaly detection system relies upon the assumption of synergy between smart feature extraction blocks, image preprocessing blocks, and cutting-edge imaging sensors to produce the optimal diagnostic accuracy. High-precision digital CT scan or X-ray image sensors are used for high-precision sensing of lungs. Image integration with image systems that are DICOM-compliant is provided to allow seamless interaction and storage. It provides power supply with a steady medical-grade power supply with stand-by feature to render the system stable.
Software level consists of adaptive noise reduction algorithms such as CLAHE and histogram equalization and deep models such as CNN and autoencoders for feature extraction. Efficiency in preprocessing is controlled through image quality parameters. Solution comprises AI/ML tools implemented on Python and OpenCV backed by TensorFlow libraries. HL7/FHIR standards are utilized at transmission time as well as at HIS sync with an interoperability objective.
The processing unit is powered by edge computing modules to enable real-time analysis without overwhelming central systems. The technology is also made deployable in rural and low-resource settings by virtue of interoperability with solar power energy sources and portable radiography units. Security layers entail encryption and access control of sensitive medical information.
PRIOR ART
US5729620A: X-ray images are displayed at both high-resolution and high illumination with annotation superimposed in registration therewith to point out suspected abnormalities identified through a process in which the x-ray images are digitized and the digitized information is subjected to feature extraction processing. For example, the x-ray images are displayed at both high-resolution and high illumination in the form of x-ray film images displayed on a light box while the annotation information is selectively superimposed on the same image by a separate imaging system co-acting with the light box. In this manner, the radiologist can view either the x-ray film alone, in the conventional manner, or the same x-ray film, at the same position and at the same high resolution and at the same or substantially the same illumination level but with annotation information superimposed and in registration therewith. In addition, alternative ways are disclosed for displaying the high-resolution x-ray image and for selectively superimposing the annotation information thereon.
US20180253589: Using multiple imaging modes in whole slide image screening is potentially useful to reduce false positives. To use multiple imaging modes, a method for locating anomalies on a medical sample from an image thereof uses an anomaly-detection process that comprises using plural base classifiers individually to classify an object-of-interest suspected to be an anomaly. Each base classifier respectively extracts features of the object-of-interest and generates, according to the extracted features, a score indicating a likelihood of the object-of-interest being anomalous. The anomaly-detection process further comprises using an aggregate classifier to combine the scores generated by the base classifiers to determine whether the object-of-interest is the anomaly. The aggregate classifier determines a dependability measure for each base classifier according to setting-based variables of a setting under which the sample and the image are obtained, and then selectively combines the scores of the base classifiers according to the dependability measures.
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.
The present invention relates to a noise-resistant and contrast-enhanced preprocessing framework specifically designed to improve tuberculosis (TB) detection from chest X-ray and other medical images. Conventional preprocessing methods fail to adapt to varied imaging conditions, resulting in diagnostic inaccuracies. The disclosed system introduces a modular pipeline integrating hybrid noise filtering, adaptive contrast enhancement, lung region segmentation, and feature extraction tailored for TB-specific manifestations. It dynamically adjusts preprocessing parameters based on image quality analysis, thereby preserving diagnostic information while minimizing artifacts. The framework is cross-device compatible, scalable across edge, cloud, and mobile platforms, and seamlessly integrates with computer-aided diagnostic (CAD) tools and deep learning models. It enhances diagnostic precision, reduces false negatives, and supports rural and resource-limited healthcare systems.
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 current invention presents a novel paradigm of image preprocessing to improve the quality of medical images, i.e., chest X-rays, for best detection of Tuberculosis (TB). The proposed system presents the latest noise removal algorithms, adaptive contrast, and feature extraction optimized to boost diagnostic accuracy. The system is made interchangeable with computer-aided detection systems and deep learning algorithms. It provides high-intensity image normalization, artefact reduction, and enhancement of tissue structure. The technology greatly helps radiologists as well as AI platforms to detect TB with greater accuracy. It is cross-device compatible on any imaging modality and can be integrated into telemedicine, clinical, or mobile installations to enable TB screening in low-resource and high-burden settings.
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.
Tuberculosis (TB) remains one of the most persistent global health challenges, particularly in low-resource countries. Early and accurate detection is vital, and chest radiographs are among the most common diagnostic tools. However, variability in image quality due to equipment differences, patient movement, or environmental factors compromises sensitivity and specificity.
Existing preprocessing methods, such as Gaussian filtering or simple histogram equalization, are either too generic or computationally intensive. They often blur critical pathological details or distort relevant features, leading to misdiagnosis.
The present invention overcomes these limitations by proposing a task-specific preprocessing pipeline optimized for TB detection. Unlike conventional systems, the pipeline is modular, adaptable, and lightweight, ensuring real-time applicability.
The invention introduces a hybrid noise reduction module that combines bilateral filtering, median filtering, and wavelet-based denoising. This hybrid approach dynamically adapts its parameters based on local image statistics, thereby preserving lung textures and edges essential for TB diagnosis.
A contrast enhancement unit is employed that integrates adaptive histogram equalization with region-based tone mapping. This selectively enhances diagnostically significant areas, revealing faint TB indicators such as infiltrates, nodules, and cavitations.
The lung segmentation engine automatically identifies pulmonary regions by detecting anatomical boundaries. This allows the system to eliminate irrelevant backgrounds and focus only on diagnostically relevant regions.
A feature extraction layer further enhances key structural details. Gradient-based and morphological operations highlight subtle patterns characteristic of TB while suppressing redundant signals.
A built-in image quality analyzer evaluates input images on sharpness, noise level, and exposure. It translates these metrics into preprocessing parameters that govern filtering strength, contrast adjustments, and segmentation thresholds.
The integration interface facilitates seamless interoperability with PACS, hospital information systems, and AI-based CAD platforms. It ensures compatibility with DICOM standards for medical imaging.
The invention further includes a user configuration panel enabling radiologists or clinicians to adjust preprocessing settings for specialized cases, ensuring flexibility.
The framework is scalable across multiple deployment modes. It can be embedded directly into imaging devices, integrated into hospital PACS, or deployed via cloud-based telemedicine platforms for large-scale TB screening.
In mobile settings, the pipeline can be installed in portable X-ray machines or even smartphones as preprocessing modules, enabling field-level diagnostic capabilities in underserved regions.
One embodiment integrates preprocessing into edge computing devices, ensuring real-time operation without requiring high-end infrastructure. This is particularly important in remote areas with limited internet connectivity.
Another embodiment facilitates cloud-based batch preprocessing of TB datasets, supporting epidemiological research and AI training for large-scale analysis.
The invention also supports continuous learning wherein newly processed and annotated data is fed back into the system, enhancing its adaptability to diverse populations and imaging conditions.
Security features, including data encryption and access control, ensure compliance with medical confidentiality standards such as HL7 and FHIR.
The modular design makes the system future-proof, allowing easy updates as new denoising algorithms, contrast enhancement methods, or segmentation techniques become available.
The preprocessing pipeline significantly reduces the false-negative rate, which is critical in TB detection, as missed cases lead to uncontrolled transmission. Beyond TB, the system has potential applicability for other pulmonary diseases, including pneumonia and lung cancer, where accurate radiographic interpretation is equally important.
By improving diagnostic accuracy in both AI systems and human radiologists, the invention directly contributes to public health efforts such as the National Tuberculosis Elimination Programme (NTEP) and similar global initiatives.
The current invention presents a novel paradigm of image preprocessing to improve the quality of medical images, i.e., chest X-rays, for best detection of Tuberculosis (TB). The proposed system presents the latest noise removal algorithms, adaptive contrast, and feature extraction optimized to boost diagnostic accuracy. The system is made interchangible with computer-aided detection systems and deep learning algorithms. It provides high-intensity image normalisation, artefact reduction, and enhancement of tissue structure. The technology greatly helps radiologists as well as AI platforms to detect TB with greater accuracy. It is cross-device compatible on any imaging modality and can be integrated into telemedicine, clinical, or mobile installations to enable TB screening in low-resource and high-burden settings.
It is novel since it particularly favors onboard, modular preprocessing pipeline for TB detection from medical images. Compared to the overall enhancement modules, the novelty provided by this architecture is a hybrid model of noise filtering that dynamically adapts, as well as a learning-based dynamic model of contrast based on TB-guided sets of images. It employs anatomy-based feature extraction algorithms with a focus on regions strongly associated with TB patients. The technology is also unique in its ease of integration into AI diagnostic platforms, making them more precise without model training. It has real-time preprocessing that can be scaled up to cloud and edge devices and facilitates flexibility and efficiency across many healthcare environments.
The best method of working the invention involves embedding the preprocessing pipeline into a portable chest X-ray device or edge-computing unit. Images are first acquired from standard radiographic systems. The hybrid noise reduction module dynamically removes noise without compromising texture details. The adaptive contrast unit selectively enhances pulmonary regions using CLAHE and tone mapping. Automated lung segmentation isolates diagnostically relevant areas, while the feature extraction layer highlights TB-specific abnormalities. The processed images are then integrated into AI-based CAD tools or transmitted to radiologists through DICOM-compliant PACS systems. The system ensures real-time functionality and scalability, making it suitable for rural health camps, mobile screening units, and large hospital networks.
Stepwise Working Functionality
1.Image Acquisition: The system takes chest X-ray or CT scan images with high resolution from medical imaging devices.
2.Preprocessing Stage:
No Gaussian, median, and bilateral filtering for denoising.
No CLAHE and histogram equalization for contrast stretching.
3.Region of Interest (ROI) Selection:
No Uses lung segmentation algorithms to detect areas suspected of TB infection.
4. Feature Extraction:
No Deep neural networks (e.g., CNNs) detect spatial and texture-based features characteristic of TB.
5. Classification and Decision Making:
No Features are segmented by trained ML classifiers such as SVM, Decision Trees, or deep networks.
6. Visualization:
No Marked-up results superimposed on original image for physician reading.
7. Storage and Communication:
No Results stored locally and in cloud and shipped to radiologist and health records system.
8. Alerts and Reports:
No Auto-alerts generated and reported back to healthcare providers with a report at suspected TB.
9. Continuous Learning:
No Model updated and learned from newly labeled data using online learning techniques.
ADVANTAGES OF THE INVENTION
• Environment: Facilitates digital functioning, minimizes chemical toxic waste and use of film-based radiology.
• Society: Facilitates early detection of TB, particularly among cut-off and poor patients, with better outcome and fewer transmissions.
• Country: Facilitates public health programs such as National Tuberculosis Elimination Programme (NTEP), evidence-based policy decisions with real-time data.
Appropriate Functionality
No such hardware in physical shape such as ropeways or vests but their electronic and digital equivalents:
• Sensors: Electronic CT or X-ray scanner sensors for chest scan acquisitions.
• Software Modules: Enhancement, preprocessing, and analysis modules.
• AI Engine: Classification and feature extraction with deep learning algorithms-based engine.
• Communication System: Reporting and alert Wireless/Cloud-based modules.
• User Interface: Image display and result interpretation interface.
• Power System: Integrated backup supply for field deployment coupling.
• Security Features: Patient confidentiality assured by secure encryption and data authenticity.
, Claims:1. A noise-resistant and contrast-enhanced preprocessing framework for tuberculosis detection in chest radiographs, comprising a hybrid noise reduction module, adaptive contrast enhancement unit, lung segmentation engine, feature extraction layer, image quality analyzer, and integration interface, configured to optimize diagnostic accuracy.
2. The framework as claimed in claim 1, wherein the hybrid noise reduction module combines bilateral, median, and wavelet-based filters to dynamically adapt filtering parameters based on local image statistics.
3. The framework as claimed in claim 1, wherein the adaptive contrast enhancement unit employs adaptive histogram equalization and region-based tone mapping to enhance diagnostically relevant features.
4. The framework as claimed in claim 1, wherein the lung segmentation engine automatically detects pulmonary boundaries to isolate regions of interest.
5. The framework as claimed in claim 1, wherein the feature extraction layer utilizes gradient-based and morphological filters to highlight nodules, infiltrates, and cavitations.
6. The framework as claimed in claim 1, wherein the image quality analyzer converts input metrics including sharpness, noise, and exposure into preprocessing parameter adjustments.
7. The framework as claimed in claim 1, wherein the integration interface is configured to support interoperability with PACS systems and AI-based diagnostic software.
8. The framework as claimed in claim 1, wherein the system is deployable across edge, cloud, and mobile computing platforms for real-time operation.
9. The framework as claimed in claim 1, wherein the preprocessing parameters are user-configurable through a clinician interface for case-specific customization.
10. The framework as claimed in claim 1, wherein the preprocessing pipeline is optimized to reduce false negatives in TB detection without compromising image integrity.