Model-based coupled denoising and segmentation of medical images

In medical science, diagnosis and prognosis is one of the most difficult and challenging task because of restricted subjectivity of the experts and presence of fuzziness in medical images. In observing the severity of several diseases, different professional experts may result in wrong diagnosis. In order to perform diagnosis intuitively in the medical images, different image processing methods have been explored in terms of neutrosophic theory to interpret the inherent uncertainty, ambiguity and vagueness. This paper demonstrates the use of neutrosophic theory in medical image denoising and segmentation where the performance is observed to be much better.

The rapid advancements in medical imaging technologies have significantly enhanced diagnostic accuracy and clinical decision-making in modern healthcare. Image segmentation and deep learning have emerged as transformative tools among these advancements. This article explores the pivotal role of image segmentation and deep learning in medical imaging, detailing their methodologies, applications, challenges, and future directions. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized medical imaging by automating the analysis of complex datasets and improving diagnostic precision. Image segmentation, a fundamental component of medical imaging, allows for delineating specific structures such as organs, tissues, and pathological regions. Together, these technologies have been applied in diverse fields, including oncology, cardiology, neurology, and ophthalmology, enabling applications such as tumor detection, organ segmentation, disease progression monitoring, and treatment planning. However, despite its transformative potential, the integration of deep learning into medical imaging faces several challenges. These include data scarcity, privacy concerns, interpretability issues, and regulatory hurdles. The article discusses various strategies to address these challenges, such as data augmentation, transfer learning, and the development of explainable AI models to ensure transparency and trustworthiness. Evaluation metrics, such as accuracy, sensitivity, specificity, and Dice Similarity Coefficient (DSC), are essential for assessing model performance. Rigorous clinical validation and regulatory approval are crucial to integrating deep learning systems into clinical workflows effectively. Looking ahead, the future of deep learning in medical imaging holds immense promise. Innovations like multimodal imaging, personalized medicine, and AI-driven automation are set to further revolutionize the field, enhancing the efficiency and accuracy of diagnostics. Collaborative efforts between clinicians, researchers, and AI developers will play a vital role in overcoming current limitations and driving progress. This article concludes by emphasizing the transformative potential of deep learning and image segmentation in medical imaging, highlighting their ability to improve diagnostic accuracy, streamline clinical workflows, and ultimately, enhance patient care. By addressing current challenges and continuing to innovate, these technologies are poised to redefine the landscape of medical diagnostics and treatment in the years to come.

Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms.

In this paper, we propose and present a novel algorithm for medical image segmentation (MIS). By analyzing the current state-of-the-art related algorithms, we introduce the multi-band active contour model based limit function to make the multilayer segmentation available. With the development of image segmentation technology, the development of medical image segmentation technology also got very big, because there is no find common, accepted effect ideal is suitable for medical image segmentation method, almost existing each kind of segmentation method has application in the field of medical image segmentation. Furtherly, with the optimized aims of being robust to the noise and avoiding the bad effluence on the result, we adopt the kernel method and new initialization curve. This model suffers from low noise robustness, and model algorithm is difficult to achieve. Integrated segmentation technology refers to two or more technology is used, combined with their own advantages, so they can on the accuracy or efficiency to achieve better performance than when using a single. A new penalty term is introduced to improve numerical stability and the step length is increased to improve efficiency. As far as the robustness and effectiveness are concerned, our method is better than the existing medical image segmentation algorithms. Experimental analysis verifies the success of our method.

This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image segmentation research papers and Color Image based Segmentation Techniques. With the growing research on image segmentation, it has become important to categorise the research outcomes and provide readers with an overview of the existing segmentation techniques in each category. In this paper, different image segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open source software packages for image segmentation and standard databases are provided. Finally, summaries and review of research work for image segmentation techniques along with quantitative comparisons for assessing the segmentation results with different parameters are represented in tabular format, which are the extracts of many research papers. Index Terms—Graph based segmentation technique, medical image segmentation, color image segmentation, watershed (WS) method, F-measure, computerized tomography (CT) images I. Introduction Image segmentation is the process of separating or grouping an image into different parts. There are currently many different ways of performing image segmentation, ranging from the simple thresholding method to advanced color image segmentation methods. These parts normally correspond to something that humans can easily separate and view as individual objects. Computers have no means of intelligently recognizing objects, and so many different methods have been developed in order to segment images. The segmentation process in based on various features found in the image. This might be color information, boundaries or segment of an image. The aim of image segmentation is the domain-independent partition of the image into a set of regions, which are visually distinct and uniform with respect to some property, such as grey level, texture or colour. Segmentation can be considered the first step and key issue in object recognition, scene understanding and image understanding. Its application area varies from industrial quality control to medicine, robot navigation, geophysical exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the quality of the segmentation. In this review paper we will discuss on graph based segmentation techniques, color image segmentation techniques and medical image segmentation, which is the real time application and very important field of research. The mathematical details are avoided for simplicity.

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people's time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially useful for medical imaging-related tasks where training datasets are usually limited in size and heavily imbalanced against the diseased class. We present a systematic review, following the PRISMA guidelines, of recent GAN architectures used for medical image analysis to help the readers in making an informed decision before employing GANs in developing medical image classification and segmentation models. We have extracted 54 papers that highlight the capabilities and application of GANs in medical imaging from January 2015 to August 2020 and inclusion criteria for meta-analysis. Our results show four main architectures of GAN that are used for segmentation or classification in medical imaging. We provide a comprehensive overview of recent trends in the application of GANs in clinical diagnosis through medical image segmentation and classification and ultimately share experiences for task-based GAN implementations.

DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

A review of medical ocular image segmentation

Segmentation of medical images plays a central role in intelligent image analysis and understanding. This paper presents a novel evolution oriented semi-supervised (EOS) approach for segmentation and labelling of medical images. The segmentation method is based on a semi supervised classifier. The classifier, which can evolve with the introduction of new classes and can accommodate corrections made by human experts in the existing class, is developed using adaptive K-means clustering and ripple down rule (RDR) concepts. For classifying pixels of the image to obtain homogeneous segments of a specific class we use feature vectors derived from DCT coefficients. We tested the method on high resolution computed tomography (HRCT) lung images which contain patterns of emphysema and ground glass opacities.

Segmentation of medical images is a challenging problem in the field of image analysis. Several diagnostics are based on proper segmentation of the digitized image. Segmentation of medical images is needed for applications involving estimation of the boundary of an object, classification of tissue abnormalities, shape analysis, contour detection and texture segmentation. Despite the existence of several techniques, segmentation of specific medical images still remains a crucial problem due to the complex nature of most medical images. A multiresolution image representation approach is used for better analyzing the information present in an image. We use multiresolution wavelet decomposition to reconstruct the original image such that it contains all the salient features relevant to segmentation and is devoid of the low frequency noise and texture information that can be ignored while segmenting the image. An unsupervised neural network with fuzzy learning rules is then used to segment the reconstructed image. >

Wavelet transforms and other multiscale analysis functions have been used for compact signal and image representations in denoising, compression, and feature detection processing problems. The wavelet transform itself offers great design flexibility. Basis selection, spatial-frequency tiling, and various wavelet threshold strategies can be optimized for best adaptation to a processing application, data characteristics, and feature of interest. Fast implementation of wavelet transforms using a filter-bank framework enables real-time processing capability. Instead of trying to replace standard image processing techniques, wavelet transforms offer an efficient representation of the signal, finely tuned to its intrinsic properties. By combining such representations with simple processing techniques in the transform domain, multiscale analysis can accomplish remarkable performance and efficiency for many image processing problems.KeywordsWavelet transform using MatlabImage edge detectionSegmentationRegistrationDe-noisingLossless image compressionDigital imaging and communications in medicine (DICOM)Security issue in transmissionTransmission of medical imagesMeasuring lossless compression effectiveness parametersCompression algorithm

ResBCU-Net: Deep learning approach for segmentation of skin images

Accurate medical image processing is essential for clinical diagnosis, as it helps physicians identify conditions early and provide timely treatment. Among its components, medical image segmentation is a particularly important step. However, many existing clustering-based segmentation methods treat image enhancement, segmentation, and spatial refinement as separate tasks. This fragmented approach often results in suboptimal segmentation and reduced anatomical consistency. This study addresses this limitation by introducing an integrated hybrid framework for X-ray image enhancement and segmentation. The proposed approach combines adaptive preprocessing with multi–color-space analysis, applies K-means clustering for initial segmentation, uses Fuzzy C-Means (FCM) to model soft class memberships, and incorporates fuzzy connectivity to refine spatial relationships while preserving anatomical continuity. Experiments on real clinical X-ray images show that K-means offers high computational efficiency, while FCM provides better boundary delineation in areas with unclear tissue transitions. Incorporating fuzzy connectivity further improves segmentation performance by reducing fragmentation and strengthening spatial coherence. Overall, the results demonstrate that the proposed hybrid approach outperforms standalone clustering methods, producing more consistent and anatomically meaningful segmentation results. The developed Python-based graphical user interface facilitates interactive visualization and analysis, highlighting the practical applicability of the framework for research, education, and potential clinical decision-support systems.

Medical image segmentation is a problem of fundamental importance in medical image processing. The accurate segmentation of a medical image can provide important information for the diagnosis and treatment of many diseases. Since a medical image often contains noises and the objects in it are inherently complex in general, methods that can accurately segment an arbitrary medical image are still unavailable. In this paper, a new approach that combines convolutional operators and an adaptive Hidden Markov Model is developed for the segmentation of medical images. Specifically, the features associated with each pixel in a medical image are obtained with a set of convolutional operators. The semantic and spatial correlations among pixels in the image are then progressively captured by an adaptive Hidden Markov Model. The labels of the pixels can be efficiently obtained with a dynamic programming algorithm in linear time. Our experimental results show that this approach can achieve segmentation results with improved accuracy on a set of brain medical images.