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92 articles for “automated segmentation”
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Q-Metrics for Early Detection of Cervical Cancer
Abstract: The most prevalent form of cancer in women worldwide is uterine cervical cancer. Through screening programs aimed at detecting precancerous lesions most cases of cervical cancer can be prevented. In this article, Q-metrics has been proposed for carrying out automated image segmentation of uterine cervical cancer. The validation of detection of cervical lesions is an important issue in medical image processing because it has a specific impact on surgical planning. …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 1, Issue 1, 2014 · pp. 26–30 Read article
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Automated Vessel Segmentation of Retinal Vessels using Hybrid Region Information
Abstract: AbstractRecent researches state that the categorization of retinal vessels into artery/vein (A/V) is a critical phase for automating the detection of vascular adjustments and for the calculation of function symptoms related to numerous systemic illnesses which includes diabetes, high blood pressure, and different cardiovascular situations. This paper affords an automatic method for A/V class, primarily based on the evaluation of a graph extracted from the retinal vasculature. The proposed method …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 28–36 Read article
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License Plate Detection using CNN
Abstract: Tag recognition is a picture preparing innovation used to distinguish vehicles by their tags. This advancement is used in various security and traffic applications. Deep learning employs complex mechanisms to extract features from samples This paper proposes a system trained using the MobileNetV2 convolutional neural network (CNN) model to detect the characters and digits from vehicle license plates. Our approach is highly influenced by the recent advancements made in the …
Published in Journal Of Network security · Vol. 9, Issue 2, 2021 · pp. 21–27 Read article
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A Split and Merge UNet: A Deep Learning Assisted UNet Model to Segment Corpus Callosum of Brain for Automatic Autism Detection
Abstract: In recent years, deep learning techniques have shown remarkable performance in various image analysis applications, particularly in the domain of medical image processing. Among these, image segmentation plays a critical role, as it helps in isolating and analyzing specific regions within medical images. The proposed study focuses on segmenting the corpus callosum, a vital structure in the human brain, using a novel optimization technique known as the Split and Merge …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 1–9 Read article
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Automated Microstructure Classification with Class-Specific Segmentation for Titanium Based Composite Materials
Abstract: In engineering, characterisation of microstructure is required to determine and forecast behaviour of titanium alloys. Our proposal in this work has been a deep-learning-based framework in the automatic classification and segmentation of Titanium Based Composite Material. The framework then uses EfficientNetB0 backbone, where we have chosen the backbone to scale the performance of classification and the computational efficiency with the assistance of the transfer learning and the compound scaling. In …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 424–433 Read article
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Automatic Number Plate Recognition Based Image Processing
Abstract: In most countries, traffic control and vehicle owner identification have become important problems. It is almost impossible to identify vehicle owners who break rules of the traffic, especially those driving at high speeds. Another problem inhibits traffic officers from catching the offenders in most cases because solving traffic offenders involves retrieving license plate numbers from fast-moving vehicles. Automatic Number Plate Recognition (ANPR) systems have been developed as an effective solution …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 33–40 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Automatic annotation of reading using Speech Recognition: A pilot study
Abstract: Speech is a time-varying continuous stimulus. Analyzing a speech sample is generally done by transcribing it which can be considered to be a time-consuming process. A time annotated transcription is required for a detailed evaluation of the speech sample to study the language and prosodic features. The focus of this paper is to develop a tool to automatically segment audio recordings into silences and chunks of speech corpus and further …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 5, Issue 2, 2018 · pp. 25–29 Read article
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A Comprehensive Review of CNN-Based Framework for Multi-Sign Detection of Diabetic Retinopathy in Fundus Images Using Public Datasets
Abstract: Diabetic retinopathy (DR) is one of the main causes of vision impairment. Blindness prevention and effective treatment depend on early detection. A thorough deep learning-based framework for the automatic segmentation and simultaneous detection of exudates, hemorrhages, and microaneurysms – three important DR indicators – from retinal fundus images is presented in this work. These three pathological signs’ corresponding annotated image patches, along with background (no-sign) areas, were used to train …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 14–23 Read article
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Intelligent Document Scanner
Abstract: AbstractThere is an irresistible trend in the present world for scanning the paper documents and convert it into a digitalized format. Most of these scanning treat the whole document as an entire image. In this scenario, we propose a novel “Intelligent Document Scanner” which automatically segment and classify the contents of the image document including texts, tables and images and store it as a PDF document with three sections that …
Published in Journal of Computer Technology & Applications · Vol. 7, Issue 2, 2016 · pp. 47–57 Read article
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Advancing Brain Tumor MRI Segmentation
Abstract: Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 2, 2025 · pp. 28–33 Read article
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article
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Region Segmentation and Annotation with Vehicle Detection Validation Application in Airborne Images
Abstract: In this work, the authors propose an automatic image segmentation and annotation system for airborne images. Initial region segmentation using existing region segmentation methods is applied to airborne images first. To deal with over-segmentation on the initial region segmentation results, the authors performed graph-based region merging by constructing an undirected-graph based on 8-connected local neighborhood. For each region, the authors extracted low-level features and used the Support Vector Machine (SVM) …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 1, Issue 1, 2014 · pp. 9–19 Read article
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Autonomous Vehicles: The Future of Transportation
Abstract: AbstractAutonomous vehicles are anticipated to be the future of automobile transportation which is efficient safe and cost effective, several automobile companies have put their foot in the vast segment of autonomous vehicles and are currently working towards manufacturing driverless vehicles. From Internet giant Google Inc. to major Automobile manufacturing companies like BMW, Mercedes, Toyota, Tesla motors and world’s largest Taxi firm Uber, all major and minor companies are putting a …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 7, Issue 1, 2020 · pp. 8–13 Read article
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Analysis of White Matter, Gray Matter, and Cerebrospinal Fluid Alterations in Neurological Disorders: A Deep Learning Approach
Abstract: This paper investigates the role of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) alterations in the pathophysiology of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, and epilepsy. By leveraging advanced deep learning methodologies, we aim to automate the segmentation and analysis of brain structures from MRI scans, enabling a more detailed and precise evaluation of their roles in disease progression. These techniques allow for the identification …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 3, 2024 · pp. 21–27 Read article
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Automatic Detection of Air Holes Inside The Esophagus in CT Images
Abstract: Automatic esophagus segmentation is a challenging problem. The wall of the esophagus consists of muscle tissue, which has a low contrast to vessels, other muscles and lymph nodes. Shape and appearance can vary a lot. It appears solid if it is empty, but it can also be filled with air, remains of orally given contrast agent, or both. Even for a human, it is often impossible to accurately delineate the …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 1, Issue 1, 2011 · pp. 24–28 Read article
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A Comparative Machine Learning Framework for Early Prediction of Liver Cancer Using Clinical Attributes
Abstract: One of the main causes of cancer-related death globally is liver cancer, and improving patient outcomes depends heavily on early detection. However, low contrast, noise, organ similarity, and tumor shape and size variability make it difficult to accurately identify and segment liver tumors from medical imaging. Automated liver cancer diagnosis, segmentation, and prognosis have been greatly improved by recent developments in artificial intelligence (AI), especially deep learning. This work presents …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 · pp. 39–47 Read article
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Automatic License Plate Recognition using Video Sequences as Input and Artificial Neural Network as Recognition Technique
Abstract: AbstractALPR system is used to automatically extract the number plate of a vehicle. This can be achieved by using different techniques that are employed in the field of image processing. ALPR method consists of 4 basic stages. The first stage of the system is the acquisition of an image of the vehicle. After that, the next step is license plate localization and then segmentation of the extracted license plate is …
Published in Current Trends in Signal Processing · Vol. 8, Issue 2, 2018 · pp. 25–38 Read article
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Iris Recognition System Using Principal Component Analysis based Back-Propagation Learning Neural Networks
Abstract: This paper deals with the iris recognition system using Back-Propagation learning neural network algorithm where Principal Component Analysis technique has been used to reduce the dimension of the iris feature vector. Automated iris localization and segmentation methods have applied to effectively isolate the iris region from pupil and sclera. Circular Hogue transform has used to detect the iris/sclera boundary and pupil/iris boundary. To remove the upper and lower eyelids effects …
Published in Current Trends in Information Technology · Vol. 3, Issue 3, 2013 · pp. 5–11 Read article
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Fuzzy C-Means Clustering for Effective Segmentation and Classification of Brain Tumors in MRI Scans
Abstract: The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 23–28 Read article