Large Number Of Medical Images Research Articles

A Review of Semantic Medical Image Segmentation Based on Different Paradigms

In recent years, with the widespread application of medical images, the rapid and accurate identification of these regions of interest in a large number of medical images has received widespread attention. This article provides a review of medical image segmentation methods based on deep learning. Firstly, an overview of medical image segmentation methods was provided in the relevant knowledge, segmentation types, segmentation processes, and image processing applications. Secondly, the applications of supervised, semi supervised, and unsupervised methods in medical image segmentation were discussed, and their advantages, disadvantages, and applicable scenarios were revealed through the application of a large number of specific segmentation examples in practical scenarios. Finally, the commonly used medical image segmentation datasets and evaluation indicators were introduced, and the current medical image segmentation methods were summarized and prospected. This review provides a comprehensive and in-depth understanding for researchers in the field of medical image segmentation, and provides valuable references for the design and implementation of future related work.

CMF defects database: A craniomaxillofacial defects dataset and a data-driven repair method

Due to the large number of medical images generated each year, data-driven machine learning methods hold great promise in the field of medical image diagnosis and analysis. In recent years, the application of deep neural networks in oral and cranio-maxillofacial surgery has gradually increased, helping surgeons to obtain more comprehensive information and develop more accurate treatment planning, including segmentation and recognition of anatomical structures, disease diagnosis, and automatic surgical planning. This paper presents a new dataset of maxillofacial defect images, containing computed tomography (CT) data of 125 normal human cranial skeletal 3D reconstructions and 2625 corresponding craniomaxillofacial bone defects. The data are classified according to seven different types of defects which are anterior skull and forehead, zygomatic arch, zygomatic bone, Naso-orbital-ethmoidal (NOE), wall of the maxillary sinus, mandibular body, mandibular ramus, and condylar respectively. Each kind of defect is further divided into three categories according to the different locations, including left side, right side, and bilateral sides (or defect passes through the midline of the face). There are three main contributions: first, a dataset is publicly available to assist researchers in the development of data-based methods for generating missing portions of the skull; second, a repair method is proposed to evaluate the dataset; third, a review of various related datasets. The release of this dataset aims to advance artificial intelligence methods in the field of oral and cranio-maxillofacial surgery and plastic surgery helping physicians to reduce the difficulty and time consumption while improving the efficiency and accuracy of prosthesis design.

Hot Topics of Molecular and Cellular Biomechanics in 2022

The analysis of biomechanical characteristics plays an important role in mastering the technical characteristics of athletes, providing guidance for the formulation and prevention of sports injury training plans and providing theoretical support for research on injury prevention and stability control in the sports field. With the importance of data analysis, the application scope of artificial intelligence methods is more extensive. For example, intelligent training systems can be used for athletes’ personalized and professional training, real-time monitoring and feedback of training data, and further reduce the risk of sports injury. However, deep learning methods process a large number of medical images to identify and predict diseases such as cancer.

Study on Key Technologies of Cancer Surveillance and Early Warning based on Data Mining

Early detection and accurate judgment are crucial during cancer diagnosis and treatment. Hence, it is of great significance to establish an effective cancer early warning system. However, the arrival of big data provides new opportunities for cancer surveillance and early warning, and data mining technology, as an important data processing method, is being widely used in the field of cancer surveillance and early warning. By analyzing a large number of medical images and medical records, the author identifies some potential risk factors which can improve the early diagnosis rate and treatment effect. Moreover, this paper compares the current status and trends of research on data mining, analyzes the key technologies of cancer surveillance and early warning based on data mining, and summarizes the related research results with the evaluation of each method.

A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images

Background and ObjectivesCardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are amongst the factors that reveal the necessity of automatic image quality assessment (IQA). However, automated IQA requires access to bulk annotated datasets for training deep learning (DL) models. Labelling medical images is a tedious, costly and time-consuming process, which creates a fundamental challenge in proposing DL-based methods for medical applications. This study aims to present a new method for CMR IQA when there is limited access to annotated datasets. MethodsThe proposed generalised deep meta-learning model can evaluate the quality by learning tasks in the prior stage and then fine-tuning the resulting model on a small labelled dataset of the desired tasks. This model was evaluated on the data of over 6,000 subjects from the UK Biobank for five defined tasks, including detecting respiratory motion, cardiac motion, Aliasing and Gibbs ringing artefacts and images without artefacts. ResultsThe results of extensive experiments show the superiority of the proposed model. Besides, comparing the model's accuracy with the domain adaptation model indicates a significant difference by using only 64 annotated images related to the desired tasks. ConclusionThe proposed model can identify unknown artefacts in images with acceptable accuracy, which makes it suitable for medical applications and quality assessment of large cohorts. Code Availabilityhttps://github.com/HosseinSimchi/META-IQA-CMRImages

Region based medical image encryption using advanced zigzag transform and 2D logistic sine map (2DLSM)

A large number of medical images are generated for diagnostic purposes, disease monitoring, research and education, quality control in health services, and so on. The secure transmission and storage of them demand a significant effort. Most of the available encryption schemes are designed for non-medical images, whereas medical images need a higher level of security and robust authentication. Additionally, in certain cases, only a specific part of the image, which may be separated into the region of interest and the region of background, medical images can be divided into these two regions. A region-based medical image encryption using a 2D logistic sine map (2DLSM) and an advanced zig zag transform is used to secure medical images. First, the Region of Interest (ROI) is extracted from the original medical image using basic morphological techniques, including edge detection, dilation, and erosion. Secondly, the ROI is encrypted using a complex zigzag transform and a 2D logistic sine map (2DLSM). Advanced zigzag transform that crosses in both directions while beginning at random points to jumble the image. This new zigzag transform method is more complex than existing zigzag transform techniques because the number of sequence types is equal to the number of pixels in the plaintext image. The confused image is diffused using a random sequence created using the 2D logistic sine map approach after numerous iterations of an advanced zigzag transformation. In order to save time and computational resources, the background region pixels are eliminated during encryption. Experiments and security analyses show that the suggested approach is strong in defending against diverse assaults and can effectively secure ROI of different types and sizes of medical photos.

Medical image segmentation method based on multi-feature interaction and fusion over cloud computing

Medical image segmentation is a crucial task in computer-aided diagnosis. While deep learning has significantly improved this field, relying solely on local computing power makes it challenging to achieve real-time segmentation results. Furthermore, traditional convolutional neural networks (CNNs) lack the ability to extract global features. To address these issues, this paper proposes a cloud-based medical image segmentation method that leverages multi-feature extraction and interactive fusion. Specifically, this method employs cloud computing to process a large number of medical images and overcome local computing power limitations. It also combines Transformer and CNNs to extract global and local features, respectively, and introduces an interactive fusion attention module to improve segmentation accuracy. The proposed approach is validated on multiple medical image datasets, and experimental results demonstrate its effectiveness and progress.

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: 27 March 2023 Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

Noninvasive diagnosis of liver fibrosis in schistosomiasis japonica: Opportunities and challenges

Noninvasive diagnosis of liver fibrosis in schistosomiasis japonica: Opportunities and challenges

Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.

Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method. A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function. In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method. Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x-ray images. Although extensive efforts have been put into this field, there are still manychallenges. Most of the existing fine-grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high-level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine-grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine-grained medical images still has the problem of insufficient sample data with accurate annotationinformation. To address above challenges, we introduced a novel fine-grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x-raydatasets. Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26 and achieves a very efficient training speed which is 100 times faster than the pair-wise loss-based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrievalperformance. The proposed model is capable of learning discriminative representations from chest x-ray datasets, and it achieves better performance compared with other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chestdisease.

RS-FFGAN:Generative adversarial network based on real sample feature fusion for pediatric CXR image data enhancement

With the increasing demand for computer-aided diagnosis technology in the medical field, convolutional neural network (CNN) has been widely used in recent years. The training of CNNs requires a large number of medical images. However, the pediatric CXR data are difficult to obtain and often suffer from imbalance problems. To augment the image data, the researchers augment the dataset with flipping, scaling, and increasing contrast. But this will lead to the problem of single samples, resulting in a large number of samples with no application value. In this paper, we propose a generative adversarial networks based on real sample feature fusion, called RS-FFGAN. It can synthesize realistic pediatric CXR and improve the quality of the generated composite image, solve the problem of unstable training, and more importantly, improve the prediction accuracy of the classification model under the condition of unbalanced data. We detect the quality and diversity of synthetic images by using IS and FID, the RS-FFGAN synthetic image has the lowest FID value, which is 32.73 for the Normal class and 57.55 for the Pneumonia class. The mean IS is the highest with 1.92 for the Normal class and 1.80 for the Pneumonia class. On small data sets and unbalanced data sets, using RS-FFGAN can improve the prediction accuracy of classification model by 5.88% and 15.62% respectively. The results show that using RS-FFGAN to synthesize images can balance the real data distribution and significantly improve the classification performance of CNN.

A plain-image correlative semi-selective medical image encryption algorithm using enhanced 2D-logistic map.

Modern medical examinations have produced a large number of medical images. It is a great challenge to transmit and store them quickly and securely. Existing solutions mainly use medical image encryption algorithms, but these encryption algorithms, which were developed for ordinary images, are time-consuming and must cope with insufficient security considerations when encrypting medical images. Compared with ordinary images, medical images can be divided into the region of interest and the region of background. In this paper, based on this characteristic, a plain-image correlative semi-selective medical image encryption algorithm using the enhanced two dimensional Logistic map was proposed. First, the region of interest of a plain medical image is permuted at the pixel level, then for the whole medical image, substitution is performed pixel by pixel. An ideal compromise between encryption speed and security can be achieved by full-encrypting the region of interest and semi-encrypting the region of background. Several main types of medical images and some normal images were selected as the samples for simulation, and main image cryptanalysis methods were used to analyze the results. The results showed that the cipher-images have a good visual quality, high information entropy, low correlation between adjacent pixels, as well as uniformly distribute histogram. The algorithm is sensitive to the initial key and plain-image, and has a large keyspace and low time complexity. The time complexity is lower when compared with the current medical image full encryption algorithm, and the security performance is better when compared with the current medical image selective encryption algorithm.

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.

A Zero-Watermark Scheme Based on Quaternion Generalized Fourier Descriptor for Multiple Images

Most of the existing zero-watermark schemes for medical images are only appropriate for a single grayscale image. When they protect a large number of medical images, repeating operations will cause a significant amount of time and storage costs. Hence, this paper proposes an efficient zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor (QGFD). Firstly, QGFD is utilized to compute the feature invariants of each color image, then the representative features of each image are selected, stacked, and reshaped to generate a feature matrix, which is then binarized to get a binary feature image. Copyright information can be converted into the copyright image by using QR code technology, which contains more information. Finally, the zero-watermark image is constructed by executing the XOR operation on the copyright image and the feature image scrambled by the Cat map. In the experiment, different parameters are selected to determine the maximum number of images that the proposed scheme can protect simultaneously while achieving good robustness. The experimental results demonstrate that the proposed scheme can effectively resist common attacks, geometric attacks and joint attacks, and effectively improve the operation efficiency of the algorithm, thus effectively decreasing the time and storage cost of copyright protection for lots of medical images.

Region-Based Hybrid Medical Image Watermarking Scheme for Robust and Secured Transmission in IoMT

With the growth in Internet and digital technology, Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information is shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorization, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, medical images are partitioned into Region of Interest (RoI) and Region of Non-Interest (RoNI). To ascertain integrity of RoI, tamper detection and recovery bits are embedded in RoI in the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. QR code of hospital logo, Encrypted EPR, and RoI recovery bits are interleaved in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark. The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. Relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.

Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

AbstractColon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used in the early diagnosis, follow‐up, and after the treatment process of colon cancer. Therefore, manually controlling a large number of medical images and their interpretation is a difficult process and consumes more time. In addition, the interpretation of data with traditional methods in this process can cause misdiagnosis due to human errors. For this reason, computer‐aided systems can be used in the diagnosis of colon cancer in order to both help experts and carry out the process more quickly and successfully. In this study, a novel method named by us, CNN‐based, MA_ColonNET is developed for detecting colon cancer image data. A 45‐layer model in MA_ColonNET has been used to classify. A success (accuracy) rate of 99.75% has been achieved by means of the new model. It is shown that the proposed model can detect colon cancer earlier. In this way, the treatment process can be carried out more successfully.

Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening

Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.

Machine learning based quantitative texture analysis of CT images for diagnosis of renal lesions

Renal cancer is among the prominent causes of an upsurge in death rates around the globe that can be minimized by premature diagnosis. Computed tomography (CT) is the first-line imaging technique employed for analysis of renal lesions in clinical practice. Moreover, it is difficult for the radiologist to analyze a large number of medical images which may lead to misclassification of renal lesions that eventually results in cancer evolution or needless chemotherapy. Thus, there is a requirement to propose machine learning based techniques for classification of renal tissue based on their texture, and these techniques can be considered as a promising second opinion for the clinicians to minimize false-negative results. The present study aims to estimate the diagnosis performance of machine-learning based quantifiable texture examination of CT images to distinguish renal lesions. Texture analysis is performed to quantify the renal parenchyma tissues by employing different texture models, and the paper suggests a feature vector for the categorization of benign and malignant renal lesions based on their texture analysis. Further, dimensionality reduction is performed to recognize the discriminative features using distinct statistical, similarity and information knowledge-based techniques. The effectiveness of the resultant ranked feature vector is evaluated by using a Support vector machine (SVM), naive Bayes, Sequential minimal optimization (SMO), random forest, and K nearest neighbour (KNN) classifiers. The classification accuracy equivalent to 84% is observed by KNN with 5-fold cross-validation. Additionally, the practical implications, generalizability issues along with certain open challenges are highlighted that may open avenues for further research.

Computational approach for content‐based image retrieval of K‐similar images from brain MR image database

AbstractContent‐based medical image retrieval (CBMIR) is a mechanism to handle a huge quantity of image data generated in various medical imaging modalities. In recent years, due to the evolution of computer vision and digital imaging modalities, a large number of medical images are generated. Consequently, the task of retrieving medical images from a large image database becomes more tedious due to variation in the size and shape of the images. Hence, it is necessary to design an appropriate system for medical image retrieval. In this paper a methodology for CBMIR using features of an image such as colour, shape, and texture is proposed to represent and retrieve the images from a large database that are relevant to a given query image. This methodology is evaluated for the application of retrieving the brain MRI images of different planes (coronal, sagittal, and transverse) from a dataset of normal and demented subjects. The features are determined in terms of Grey level co‐occurrence based Haralik's features and histogram based cumulative distribution function (CDF). The image retrieval mechanism is designed using the K‐Nearest Neighbour algorithm by finding the minimum distance between query and database images. The performance parameters such as precision and recall are calculated. The average accuracy of 95.5% are obtained. The results provided ensures the capability to use it as assistive framework for radiologists in radiology image retrieval and classification.