An Improved Method of Segmentation Using Fuzzy-Neuro Logic

Images have been playing very important role in human life since beginning of human civilization. Applications of images are found ranging from basic but very effective exchange of ideas and information to some advanced technologies in the industry, society, medical and military field. Image data has become an important field of research with rapid and huge growth of visual information in the number of large-scale and online image repositories. Image data is fuzzy in nature and imprecision and vagueness may exist in both image descriptions and query specifications. Techniques of Fuzzy set theory have been extensively applied to the representation and processing of imprecise and uncertain text and visual data. Soft computing has been applied in image retrieval and image analysis in numerous cases. For the fuzzy content-based image retrieval, fuzzy sets are applied for the extraction and representation of visual features like colors, shapes, textures, for similarity measures and indexing, for representing relevance feedback and for image retrieval. Fuzzy sets are also applied for fuzzy image query processing based on a defined database models and image data management. Image segmentation is used to extract information from complex medical images by partitioning an image into mutually exclusive regions such that each ROI is spatially contiguous and homogeneous; widely used homogeneity criteria being intensity, texture, color, range, surface normal and surface curvatures. In the field of medical imaging, soft computing based segmentation using Fuzzy-Neuro logic have been used to detect various tissues like white matter, gray matter; cerebral spinal fluid and tumor for a given magnetic resonance image data set. Fuzzy-Neuro logic segmentation algorithm is used to provide satisfactory results compared to K-means, Fuzzy C-Means, Neural Network and Fuzzy logic.

At present, image segmentation technology has become increasingly mature. The application of image segmentation technology in the field of medical images is also more and more extensive. However, the growing demand is not enough for current medical image segmentation models. Unet++ is a good basic model in the field of medical image segmentation. On this basis, we add a channel attention module for long skip connections. Solve the problem of serious loss of eigenvalues in the process of long skip connections. This can get better accuracy and better image segmentation. At the same time, our added channel attention module can effectively increase the robustness of the network model. Through our experimental analysis and result judgment, our designed C-Unet++ can play a better role in medical image segmentation.KeywordsSegmentationSkip connectionAttentionDeconvolution

Clustering of data is a method by which large sets of data are grouped into clusters of smaller sets of similar data. Fuzzy c-means (FCM) clustering algorithm is one of the most commonly used unsupervised clustering technique in the field of medical imaging. Medical image segmentation refers to the segmentation of known anatomic structures from medical images. Fuzzy C-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. Fuzzy logic is a multi-valued logic derived from fuzzy set theory. FCM is popularly used for soft segmentations like brain tissue model. And also FCM can provide better results than other clustering algorithms like KM, EM, and KNN. In this paper we presented the medical image segmentation techniques based on various type of FCM algorithms.

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. >

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.

Medical image segmentation (MIS) and 3D reconstruction are crucial research directions in the field of medical imaging, which is of great significance for disease diagnosis, treatment planning and surgical navigation. In recent years, with the rapid development of Deep Learning (DL) technology, DL has made remarkable progress in the field of medical image processing and has become one of the important methods of MIS and 3D reconstruction. In this paper, the application of DL technologies in MIS and 3D reconstruction is systematically studied and discussed. Firstly, the paper introduces the basic concepts and technical challenges of MIS and 3D reconstruction, including image quality, noise interference and edge detection. Secondly, the paper introduces the data acquisition process in detail, including the medical image data set and data preprocessing method. Then, the paper puts forward the DL model framework based on self-attention mechanism, as well as the loss function and optimizer used in the training process. Then, the model is verified by experiments, and the performance of different models in MIS and 3D reconstruction is analyzed. Finally, the experimental results are comprehensively analyzed, and the application prospect and future development direction of DL in MIS and 3D reconstruction are discussed. The research results of this paper provide important theoretical and practical guidance for improving medical image processing technology and promoting the development and clinical application of medical imaging.

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.

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.

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 processing on the GPU has become quite popular recently, since this technology makes it possible to apply more advanced algorithms and to perform computationally demanding tasks quickly in clinical context. Image segmentation in medical imaging is often used to segment brain structures, organs, blood vessels and bones. Combined interactive segmentation and visualization are impeccably suited to the GPU. The data already in GPU memory can be extracted very efficiently. As a consequence, the segmentation process often becomes more complex and time-consuming. This paper proposes the ways to improve the computational speed of watershed segmentation algorithm using GPU Computing. GPUs are used to solve a wide variety modality of problems in the field of medical imaging.

Traditional statistical methods, although having a solid theoretical foundation, have been challenged in terms of their efficiency as well as their generalization ability in the face of the ever-increasing amount of massive data. With the rise of deep learning in recent years, the use of new tools such as convolutional neural networks to get information from data has become a new option. In particular, in the field of imaging, segmentation of medical images is important for tasks such as determining the type of disease and the location of lesions, which are excellent application areas for deep learning. U-Net is a particularly important deep model structure with good results for segmentation of medical images. However, there is a lack of discussions on the application of U-Net in the clinical field. In this paper, we introduce traditional image segmentation methods and U-Net, analyze the advantages of deep learning techniques in the field of image segmentation. In addition, we applied U-Net to the problem of cell segmentation and segmentation of covid-19 CT images, showing the potential of U-Net for clinical applications.

A review of medical ocular image segmentation

Medical Image Segmentation is a critical task in the field of medical imaging, playing a crucial role in diagnostics, treatment planning, and disease monitoring. The emergence of Deep Learning (DL) has ushered in a new era in Artificial Intelligence (AI), propelling remarkable advancements in key domains like language translation, object recognition, and recommendation systems. This evolution has been accompanied by continuous enhancements in computational efficiency and improvements in predictive accuracy. The introduction of sophisticated algorithms, such as convolutional neural networks (CNNs) and transformers, exemplifies these advancements. DL algorithms have demonstrated exceptional efficacy in medical image segmentation tasks, showcasing the potential for AI-driven early diagnostics. However, the deployment of AI systems in clinical environments is often hindered by the substantial computational demands and complexity of cutting-edge DL models. In this research proposal, we explore various methodologies to enrich the visual feature representation for medical images. We focus on integrating global context-oriented techniques, such as attention mechanisms, into the development of parameter-efficient deep learning models. Our goal is to create a generalized, end-to-end medical image segmentation framework that can accurately and efficiently segment medical images across different modalities and conditions. By leveraging advanced deep learning techniques and optimizing model architectures, we aim to enhance the performance and generalization capabilities of medical image segmentation models, ultimately contributing to improved clinical outcomes

With the rapid development of deep learning technology and medical technology, neural networks are widely used in the field of medical image segmentation. Among them, U-Net neural network has gradually become a research hotspot in the field of image segmentation because of its good segmentation performance. It provides doctors with a consistent method of quantifying lesions and is widely used in the field of medical image semantic segmentation. This article studies the U-Net network, learns theoretically from the U-Net network model and its basic principles, and then conducts experiments on three typical medical images of liver medical images, fundus blood vessel images, and lung nodule images to explain various types of medical images. The characteristics of the image and the difficulty of segmentation, and the performance of the U-Net network in the relevant image segmentation is verified. Finally, the problems existing in U-Net network are discussed, and the future development is prospected.

Medical image segmentation is an essential part in many medical applications such as automatic measurement of tumour size, volume of organs and content-based image retrieval, etc. Various fully convolutional architectures have been proposed in the literature to tackle this problem. Lack of generalization is one of the main challenge in the field of medical imaging and all existing fully convolutional architectures involve huge number of parameters which make them prone to overfit the data. In this study, we proposed an efficient convolutional architecture called Dilated Residual Network (DRN) for medical image segmentation. By the design of DRN architecture, we have reduced number of parameters involved drastically, making the architecture less prone to overfitting hence by improving the generalization ability. We demonstrate that DRN outperforms the previous state of the art architecture called U-Net in medical image segmentation on various datasets in terms of training time, Dice score and Jaccard score. The source code (based on Keras with Tensorflow as the background) of the DRN and sample train and test image results are available at https://github.com/LokeshBonta/Dilated-Residual-Networks.