Medical and Natural Image Segmentation Algorithm using M-F based Optimization Model and Modified Fuzzy Clustering: A Novel Approach

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.

Deep learning techniques are recently exploited for segmentation of medical images. These deep learning techniques have accomplished state-of-art conduct for automatic medical image segmentation. Image segmentation helps in quantitative and qualitative analysis of the medical images, which leads to diagnosis of various diseases. Manual segmentation of medical images is a laborious task that prevents early diagnosis of diseases. For these particular reasons, automated techniques play a major role in medical image segmentation. The recent research is focused on deep learning algorithms for efficient automatic medical image segmentation. Deep learning algorithms are classified as supervised as well as unsupervised learning. Supervised learning consists of convolutional neural networks (CNNs) and unsupervised learning consists of stacked auto-encoder, restricted Boltzmann’s machine (RBM), and deep belief networks. CNNs comprise convolutional layer, pooling layer, dropout layer, and fully connected layer. The convolutional layer carries out the convolution operation with a set of kernels along with weights and added biases individually creating a new feature map on the input image at each layer. Stacked auto-encoder models are designed by insertion of different layers termed to be auto-encoder layers in the form of stack. These layers take image as an input and extracts different features in the form of feature maps in an unsupervised mode lacking labeled data. It is a model that takes input data, gathers feature representations from this, and then uses these feature representations to restructure output data. According to the literature survey, auto-encoder layers are trained independently after which the full network is used to make a prediction by fine-tuning it using supervised training. The neurons present in deep belief nets are densely connected which helps in rapid and accurate learning of a good set of parameters. RBMs are a type of Markov random fields, consisting of input layer or visible layer and a hidden layer that brings hidden feature representation. There are bidirectional connections between the nodes, so latent feature representation extracted from an input vector and vice versa. This chapter provides an outline on the state of deep learning algorithms for medical image segmentation, highlighting those facets that are frequently useful for brain tumor segmentation. In addition, comparative analysis of the deep learning algorithms is discussed. This concludes that with the different algorithms for segmentation tumor regions from brain MRI images, deep learning has proven to be the most effective in the recent trends.

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we investigate the segmentation of lung nodule images. We address the above-mentioned problems of medical image segmentation algorithms and conduct research on medical image fusion algorithms based on a hybrid channel-space attention mechanism and medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks (CNN) and Visual Transformer. To the problem that medical image segmentation algorithms are difficult to capture long-range feature dependencies, this paper proposes a medical image segmentation model SW-UNet based on a hybrid CNN and Vision Transformer (ViT) framework. Self-attention mechanism and sliding window design of Visual Transformer are used to capture global feature associations and break the perceptual field limitation of convolutional operations due to inductive bias. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on the LUNA16 lung nodule image dataset validate the algorithm and show that the proposed network can achieve efficient medical image segmentation on a lightweight scale. In addition, to validate the migratability of the model, we performed additional validation on other tumor datasets with desirable results. Our research addresses the crucial need for improved medical image segmentation algorithms. By introducing the SW-UNet model, which combines CNN and ViT, we successfully capture long-range feature dependencies and break the perceptual field limitations of traditional convolutional operations. This approach not only enhances the efficiency of medical image segmentation but also maintains model scalability and adaptability to small medical datasets. The positive outcomes on various tumor datasets emphasize the potential migratability and broad applicability of our proposed model in the field of medical image analysis.

A Bibliometric of Publication Trends in Medical Image Segmentation: Quantitative and Qualitative Analysis

PurposeMedical images are important in diagnosing disease and treatment planning. Computer algorithms that describe anatomical structures that highlight regions of interest and remove unnecessary information are collectively known as medical image segmentation algorithms. The quality of these algorithms will directly affect the performance of the following processing steps. There are many studies about the algorithms of medical image segmentation and their applications, but none involved a bibliometric of medical image segmentation.MethodsThis bibliometric work investigated the academic publication trends in medical image segmentation technology. These data were collected from the Web of Science (WoS) Core Collection and the Scopus. In the quantitative analysis stage, important visual maps were produced to show publication trends from five different perspectives including annual publications, countries, top authors, publication sources, and keywords. In the qualitative analysis stage, the frequently used methods and research trends in the medical image segmentation field were analyzed from 49 publications with the top annual citation rates.ResultsThe analysis results showed that the number of publications had increased rapidly by year. The top related countries include the Chinese mainland, the United States, and India. Most of these publications were conference papers, besides there are also some top journals. The research hotspot in this field was deep learning‐based medical image segmentation algorithms based on keyword analysis. These publications were divided into three categories: reviews, segmentation algorithm publications, and other relevant publications. Among these three categories, segmentation algorithm publications occupied the vast majority, and deep learning neural network‐based algorithm was the research hotspots and frontiers.ConclusionsThrough this bibliometric research work, the research hotspot in the medical image segmentation field is uncovered and can point to future research in the field. It can be expected that more researchers will focus their work on deep learning neural network‐based medical image segmentation.

URCA: Uncertainty-based region clipping algorithm for semi-supervised medical image segmentation

Medical Image segmentation plays a major role in medical image processing. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than the other. The proposed method utilizes clustering with distance based segmentation approach for Computer tomography image segmentation. This paper provides new hybrid segmentation method based on K-Means, Medoid shift and Signature Quadratic Form Distance algorithm for computer tomography images. We validate the Hybrid segmentation approach with the parameters in terms of sensitivity, specificity, accuracy and number of fragments. The Real time dataset is used to evaluate the performance of the proposed method. The results obtained from the experimentation show that the proposed approach attains reliable segmentation accuracy and also clear that it is more efficient, robust and more appropriate for organ classification.

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

Medical image segmentation algorithm based on feedback mechanism convolutional neural network

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive because of its advantages, such as always being able to construct an entire division of the image. On the other hand, its disadvantages include over segmentation and sensitivity to false edges. Approach: In this study we proposed a methodology that integrates K-Means clustering with marker controlled watershed segmentation algorithm and integrates Fuzzy C-Means clustering with marker controlled watershed segmentation algorithm separately for medical image segmentation. The Clustering algorithms are unsupervised learning algorithms, while the marker controlled watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. Results: In this study, we compared K-means clustering and marker controlled watershed algorithm with Fuzzy C-means clustering and marker controlled watershed algorithm. And also we showed that our proposed method produced segmentation maps which gave fewer partitions than the segmentation maps produced by the conservative watershed algorithm. Conclusion: Integration of K-means clustering with marker controlled watershed algorithm gave better segmentation than integration of Fuzzy C-means clustering with marker controlled watershed algorithm. By reducing the amount of over segmentation, we obtained a segmentation map which is more diplomats of the several anatomies in the medical images.

Medical images have made a great effect on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Medical Image Segmentation is the development of programmed or semi-automatic detection of limitations within a 2D or 3D image. In medical field, image segmentation is one of the vital steps in Image identification and Object recognition. Image segmentation is a method in which large data is partitioned into small amount of data. If the input MRI image is segmented then identifying the lump attacked region will be easier for physicians. In recent days, many algorithms are proposed for the image segmentation. In this paper, an analysis is made on various segmentation algorithms for medical images. Furthermore, a comparison of existing segmentation algorithms is also discussed along with the performance measure of each.

The main objective of medical image segmentation is to extract and characterize anatomical structures with respect to some input features or expert knowledge. Traditional two-dimensional Otsu method for medical image segmentation is time-consuming computation and become an obstacle in real time application systems. This paper describes a way of medical image segmentation using optimized two-dimensional Otsu method based on improved genetic algorithm (GA). In proposed algorithm, the probability-ties of crossover are adaptively varied depending on the ranking value of individuals instead of fitness, and dyadic mutation operator was presented to take the place of the traditional one. The experimental results show that the new optimized method dramatically reduces the operating time in medical image segmentation while ensures the final image segmentation quality.

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

Traditional medical image segmentation methods have problems such as low segmentation accuracy and low adaptive ability. Therefore, many scholars have proposed a medical image segmentation method based on deep learning, which has achieved good results in the field of medical image segmentation. However, this type of method has the following problems in the application process: (1) Medical image segmentation target boundary positioning problem. Constrained by factors such as medical image contrast, heterogeneity, and boundary resolution, existing convolution models still cannot accurately locate boundaries. (2) Deep adaptability of deep learning network structure to medical images. Because medical images have more distinct and different feature information than natural images, the current deep learning-based medical segmentation methods have not fully considered this feature. In view of this, this paper proposes a multi-level boundary-aware RUNet segmentation model. The network structure consists of a U-Net-based segmentation network and a multi-level boundary detection network. It can solve the problem of boundary positioning. At the same time, in order to solve the problem of poor adaptability of deep learning network structures to medical images, this paper proposes to introduce a new interactive self-attention module into deep learning models. It can make the feature map get global information, and realize the effective extraction of medical image feature information. It solves the problem of weak matching between the deep learning network structure and medical images. Based on the above ideas, this paper proposes an image segmentation algorithm based on a multi-layer boundary perception-self-attention mechanism deep learning model. This method and other mainstream segmentation algorithms are used to perform experiments on related medical databases. The results show that the proposed method not only improves the segmentation effect significantly compared with traditional machine learning methods, but also improves it to a certain extent compared with other deep learning methods.

Before the deep learning algorithm, there are many traditional image segmentation methods. However, with the rapid development of artificial intelligence technology, the requirements for the accuracy and efficiency of image segmentation are getting higher and higher, so the image segmentation algorithm based on deep learning arises as the times require. But it is worth noting that these algorithms are natural image processing, and medical image format diversification, the difference of pixel value range, the presence of noise and artifacts, and so on, using the general image segmentation algorithms cannot meet medical demand scenarios medical image segmentation. On the basis of U-Net model, this paper improves its sub-modules, and proposes four sub-module structures: Path direct connection, Dropout direct connection, Conv direct connection, and Constant scale. Dataset uses Skin lesions of melanoma, and accuracy, sensitivity, specificity, precision, F-Measure, IOU, Dice coefficient and comprehensive score were selected as model performance evaluation indexes. After model training, validation and testing, the conclusion was drawn: In the segmentation task of melanoma skin disease image, the performance of Constant scale model was the best among all experimental models.