Medical Image Processing Research Research Articles

Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage.

In recent years, intracerebral hemorrhage (ICH) has garnered significant attention as a severe cerebrovascular disorder. To enhance the accuracy of ICH detection and segmentation, this study proposed an improved fuzzy C-means (FCM) algorithm and performed a comparative analysis with both traditional FCM and advanced convolutional neural network (CNN) algorithms. Experiments conducted on the publicly available CT-ICH dataset evaluated the performance of these three algorithms in predicting ICH volume. The results demonstrated that the improved FCM algorithm offered notable improvements in computational time and resource consumption compared to the traditional FCM algorithm, while also showing enhanced accuracy. However, it still lagged behind the CNN algorithm in areas such as feature extraction, model generalization, and the ability to handle complex image structures. The study concluded with a discussion of potential directions for further optimizing the FCM algorithm, aiming to bridge the performance gap with CNN algorithms and provide a reference for future research in medical image processing.

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.

In neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic. We introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability. AFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision. The proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing.

Medical Images Analysis Using Deep Learning Technique

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. This review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonics, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large-scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. This research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. The primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications.

Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023.

With the rapid development of the internet, the improvement of computer capabilities, and the continuous advancement of algorithms, deep learning has developed rapidly in recent years and has been widely applied in many fields. Previous studies have shown that deep learning has an excellent performance in image processing, and deep learning-based medical image processing may help solve the difficulties faced by traditional medical image processing. This technology has attracted the attention of many scholars in the fields of computer science and medicine. This study mainly summarizes the knowledge structure of deep learning-based medical image processing research through bibliometric analysis and explores the research hotspots and possible development trends in this field. Retrieve the Web of Science Core Collection database using the search terms "deep learning," "medical image processing," and their synonyms. Use CiteSpace for visual analysis of authors, institutions, countries, keywords, co-cited references, co-cited authors, and co-cited journals. The analysis was conducted on 562 highly cited papers retrieved from the database. The trend chart of the annual publication volume shows an upward trend. Pheng-Ann Heng, Hao Chen, and Klaus Hermann Maier-Hein are among the active authors in this field. Chinese Academy of Sciences has the highest number of publications, while the institution with the highest centrality is Stanford University. The United States has the highest number of publications, followed by China. The most frequent keyword is "Deep Learning," and the highest centrality keyword is "Algorithm." The most cited author is Kaiming He, and the author with the highest centrality is Yoshua Bengio. The application of deep learning in medical image processing is becoming increasingly common, and there are many active authors, institutions, and countries in this field. Current research in medical image processing mainly focuses on deep learning, convolutional neural networks, classification, diagnosis, segmentation, image, algorithm, and artificial intelligence. The research focus and trends are gradually shifting toward more complex and systematic directions, and deep learning technology will continue to play an important role.

An Enhanced Deep Learning Model for Brain Tumor Prediction

Brain tumour diagnosis & prediction is an challenging issue and important area of research. perversely, convolutional neural networks can support this (CNNs). They have mastered computer vision problems as well as other issues like segmenting, detecting, and recognizing visual objects. By enhancing the brain images with help of segmentation methods that are extremely challenging related to noise and cluster size sensitivity issues, as well as automated region of Interest detection (ROI), it helps with the diagnosis of brain tumours. The reality that CNNs have achieved high level of accuracy and it does not require manual extraction of features. Finding a brain tumour and correctly classifying it are challenging tasks. CNN outperforms rivals due to its extensive use in image recognition. Brain tumour segmentation is the most significant and challenging problems in the field of medical image processing research because human assisted manual categorization may lead to inaccurate prediction and diagnosis. In addition, when there is a huge amount of data existing to support in the process, it is challenging. Extraction of tumour areas from images becomes challenging due to the wide variety of appearances of brain tumours and the similarity of tumour and normal tissues.

IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks

Cardiovascular diseases currently present a key health concern, contributing to an increase in death rates worldwide. In this phase of increasing mortality rates, healthcare represents a major field of research, and the knowledge acquired from this analysis of health information will assist in the early identification of disease. The retrieval of medical information is becoming increasingly important to make an early diagnosis and provide timely treatment. Medical image segmentation and classification is an emerging field of research in medical image processing. In this research, the data collected from an Internet of Things (IoT)-based device, the health records of patients, and echocardiogram images are considered. The images are pre-processed and segmented, and then further processed using deep learning techniques for classification as well as forecasting the risk of heart disease. Segmentation is attained via fuzzy C-means clustering (FCM) and classification using a pretrained recurrent neural network (PRCNN). Based on the findings, the proposed approach achieves 99.5% accuracy, which is higher than the current state-of-the-art techniques.

Acute lymphoblastic leukemia prediction using covering-oriented rough k-means segmentation algorithm

Acute Lymphoblastic Leukemia (ALL) is a type of dreadful cancer that has an effect on the bone marrow and blood. In recent decades, leukemia mainly affects youngsters and grown-ups. Timely diagnosis of leukemia is crucial to give right treatment to patients, predominantly in the case of children. Computational tecniques for medical image analysis have incredible use and become the focus of research in medical image processing. The major objective of this research work is to predict the acute lymphoblastic leukemia cells using the proposed segmentation algorithms with the assistance of machine learning classifiers. In this study, hybrid clustering based segmentation methods Covering-orientedRough K-Means (CRKM) algorithm is proposed to segment the image of the leukemia nucleus. Then, the performance of the segmentation algorithms were analyzed using different machine learning classifiers. From the experimental analysis, it is observed that the machine learning classification algorithms achieved better prediction accuracy when compared to existing segmentation algorithms.

Automated Multimodal Fusion Based Hyperparameter Tuned Deep Learning Model for Brain Tumor Diagnosis

As medical image processing research has progressed, image fusion has emerged as a realistic solution, automatically extracting relevant data from many images before fusing them into a single, unified image. Medical imaging techniques, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc., play a crucial role in the diagnosis and classification of brain tumors (BT). A single imaging technique is not sufficient for correct diagnosis of the disease. In case the scans are ambiguous, it can lead doctors to incorrect diagnoses, which can be unsafe to the patient. The solution to this problem is fusing images from different scans containing complementary information to generate accurate images with minimum uncertainty. This research presents a novel method for the automated identification and classification of brain tumors using multi-modal deep learning (AMDL-BTDC). The proposed AMDL-BTDC model initially performs image pre-processing using bilateral filtering (BF) technique. Next, feature vectors are generated using a pair of pre-trained deep learning models called EfficientNet and SqueezeNet. Slime Mold Algorithm is used to acquire the DL models’ optimal hyperparameter settings (SMA). In the end, an autoencoder (AE) model is used for BT classification once features have been fused. The suggested model’s superior performance over other techniques under diverse measures was validated by extensive testing on the benchmark medical imaging dataset.

TE-YOLOF: Tiny and efficient YOLOF for blood cell detection

Blood cell detection in microscopic images is an essential branch of medical image processing research. The research and application of computer vision algorithms in this field are more concerned about the trade-off between accuracy and model complexity. The FED detector modified by YOLOv3 is a representative light-weight model to detect blood cell objects such as red blood cells, white blood cells and platelets. But the detection precision of red blood cells in the FED model is relatively low compared with platelets and white blood cells due to the imbalance distribution of different types of cells. In this research, we propose a light-weight model based on YOLOF to address the relatively low precision of red blood cell detection problem, in order to achieve the overall improvement of detection precision. This object detector is called TE-YOLOF, Tiny and Efficient YOLOF. Model light-weighting is accomplished with the excellent feature extraction capabilities of EfficientNet as backbone and the ability of the Depthwise Separable Convolution to reduce the number of parameters while maintaining precision. Furthermore, the Mish activation function is employed to increase the precision. Extensive experiments on the BCCD dataset prove the effectiveness of the proposed model, which can achieve higher precision with less parameters than FED. TE-YOLOF is also effective on other cross-domain blood cell detection experiments.

Improved Dominance Soft Set Based Decision Rules with Pruning for Leukemia Image Classification

Acute lymphoblastic leukemia is a well-known type of pediatric cancer that affects the blood and bone marrow. If left untreated, it ends in fatal conditions due to its proliferation into the circulation system and other indispensable organs. All over the world, leukemia primarily attacks youngsters and grown-ups. The early diagnosis of leukemia is essential for the recovery of patients, particularly in the case of children. Computational tools for medical image analysis, therefore, have significant use and become the focus of research in medical image processing. The particle swarm optimization algorithm (PSO) is employed to segment the nucleus in the leukemia image. The texture, shape, and color features are extracted from the nucleus. In this article, an improved dominance soft set-based decision rules with pruning (IDSSDRP) algorithm is proposed to predict the blast and non-blast cells of leukemia. This approach proceeds with three distinct phases: (i) improved dominance soft set-based attribute reduction using AND operation in multi-soft set theory, (ii) generation of decision rules using dominance soft set, and (iii) rule pruning. The efficiency of the proposed system is compared with other benchmark classification algorithms. The research outcomes demonstrate that the derived rules efficiently classify cancer and non-cancer cells. Classification metrics are applied along with receiver operating characteristic (ROC) curve analysis to evaluate the efficiency of the proposed framework.

Analysis on the Research Approach and Trends of Medical Image Processing in China

In order to explore the research approach and hotspots in the field of medical image processing in China, the authors retrieved related literature in the field of medical image processing in China from 2009 to 2019 in CNKI database, and preprocessed the retrieved data. Then Citespace was used to analyze the retrieved data of 1363 documents from 2009 to 2019, the paper counted the basic situation of the article’s publication time, subject, and analyzed the high frequency keywords, cited literatures with high frequency. The results show that the domestic medical image processing field has a high degree of attention to image segmentation, registration, and fusion. Deep learning, convolutional neural networks and transfer learning have been applied to medical image processing in recent years. This paper revealed domestic research trends in medical image processing, with a view to providing reference for research in medical image processing.

Analysis On Efficient Brain MRI Detection And Classification Strategy Using Medical Image Segmentation Techniques

During last few years, segmentation of brain MRI based images has gaining more popular research topic in medical image processing research domain. The segmentation, detection and extraction of the tumour affected region of MR images are very much difficult and time consuming task for the medical professional and radiologists. Their experience are very much important for enhancing accuracy. So, using computer based segmentation and classification, the performance become more better , hazards of manual work is eliminated and accuracy is more enhanced . In this work, Computer based detection is done based on efficient algorithm consisting stages are edge detection, segmentation, thresholding. After that, for improvement of the accuracy with quality factor and the Fuzzy-C-Means based classifier, are used for the classification and feature extraction of each segmented tumour. The work consisting of several segmentation methodology as edge based, threshold based and this approach for segmenting the accurate region of disease. The experimental performance of our proposed approach have been demonstrated here for quality and performance analysis on brain tumour MR images, based on accuracy, sensitivity, specificity, False Positive Rate(FPR).The experimental results achieved more than eighty percent of classifier accuracy, for the technique for classification of the tumor in MR images with accurately with analysing the effectiveness of the methodology. Also for experimental study, the image quality parameters as PSNR and MSE values are producing better outcomes than the other approaches.

A Local Type-2 Fuzzy Set Based Technique For He Stain Image Enhancement

The proposed approach in the paper comes under “Advanced Soft Computing Based Medical Image Processing Research” and the work has been conducted by Dr. Dibya Jyoti Bora (Assistant Professor), School of Computing Sciences, The Assam Kaziranga University, Jorhat, Assam in the year 2018-2019.
 Introduction: HE stain images, although considered as the golden standard for medical image diagnosis, are still found to suffer from poor contrast and degradation in color quality. In this paper, a Type-2 fuzzy set-based enhancement technique is proposed for HE stain image enhancement with special care towards color-based computations and measurements.
 Methods: This paper introduces a new approach based on Type-2 fuzzy set for HE stain image enhancement where Bicubic Interpolation plays an important part. Unsharp Masking is also employed as a post enhancement factor.
 Results: From the results, it is clearly visible that cell nuclei and other cell bodies are easily distinguishable from each other in the enhanced result produced by our proposed approach. It implies that vagueness in the edges surrounding the objects in the original image is removed to an acceptable level.
 Conclusions: The proposed approach is found to be, through both subjective and objective evaluations, an efficient preprocessing technique for a better HE stain image analysis.
 Originality: The ideas involved in this paper are original. If work by other researchers are mentioned in any part of the paper, then they are cited properly.
 Limitation: The relatively high time complexity is the only limitation associated with the proposed approach.

A Review on Medical Image Segmentation

Medical image is the process and technique of creating visual representations of the interior body for easy diagnosis. Imaging techniques are encompasses with the field of radiology, nuclear medicine, Optical imaging and image guided intervention. To identify the abnormalities medical imaging also establishes the database of normal anatomy and physiology. Medical image processing has experience in dramatic expansion and interdisciplinary research fields. The high quality of information can be produced for disease diagnosis and treatment by analyzing a significant volume of processing the data. The main objective of this paper is to provide basic concepts, techniques for medical imaging and to promote a further study and research in medical image processing.

Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications.

Medical Image Processing: A Challenging Analysis

Medical image processing is an multifaceted field at the intersection of computer science, electrical engineering, physics, mathematics and medicine. Medical Image processing has developed versatile computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The prominent and important motive of Medical Image Processing is to extract clinically relevant information or knowledge from medical images. MIP focuses on the computational analysis of the images, not their acquisition. The methods can be grouped categorized as: image segmentation, image registration, image-based physiological modeling, Research in Medical Image Processing (MIP) is mainly driven by a technology oriented point of view. MIP research should always be able to give an answer to the question of what is the potential benefit of a solved MIP problem or a newly developed MIP-based system supporting a diagnostic or therapeutic process, in terms of outcome criteria like, e.g., Quality Adjusted Life Years (QALYs) for the patient or cost savings in health care. This paper presents the concept and the strategy of how the Most Relevant MIP problems (MRMIP) shall be identified and assessed in the context of improving evaluation of MIP solutions.

Fuzzy Speed Function Based Active Contour Model for Segmentation of Pulmonary Nodules

Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules is an important and active area of research in medical image processing. At present, the classical active contour models (ACM) for segmentation of pulmonary nodules may cause the problem of boundary leakage. In order to solve the problem, a new fuzzy speed function-based active model for segmentation of pulmonary nodules is proposed in this paper. The fuzzy speed function incorporated into the ACM is calculated by the degree of membership based on intensity feature and local shape index. At the boundary of pulmonary nodules, the fuzzy speed function approaches zero and the evolution of the contour curve will stop, so the accurate segmentation of pulmonary nodules can be obtained. Experimental results on juxta-vascular nodules and GGO nodules show that the proposed ACM can achieve accurate segmentation.

Multimodality image registration using local linear embedding and hybrid entropy

Robust registration of multimodality medical images has become an active area of research in medical image processing and applications. Although mutual information (MI) has been successfully applied to image registration, it is worth noting that MI-based registration measure only takes statistical intensity information into account and does not use spatial features. In this paper, we propose a local linear embedding (LLE) and hybrid entropy based registration method that combines spatial information into registration measure. Due to the robustness to absolute intensity information of image pixels and stability in noisy environment, the ordinal features (OFs) with different orientations are extracted to represent spatial information in medical images. For high dimensional OFs, the LLE algorithm is used to dimensionality reduction and the inverse mapping of LLE is used to fuse complementary information of OFs together. Then a novel similarity measure based on hybrid entropy which integrates intensity with OF is defined to register multimodality images. The experimental results show that the proposed registration algorithm can effectively suppress and eliminate the influence of noise in images. Compared with some existing methods, the proposed algorithm is of higher precision and better robustness.

Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models

Accurate segmentation of a pulmonary nodule is an important and active area of research in medical image processing. Although many algorithms have been reported in literature for this problem, those that are applicable to various density types have not been available until recently. In this paper, we propose a new algorithm that is applicable to solid, non-solid and part-solid types and solitary, vascularized, and juxtapleural types. First, the algorithm separates lung parenchyma and radiographically denser anatomical structures with coupled competition and diffusion processes. The technique tends to derive a spatially more homogeneous foreground map than an adaptive thresholding based method. Second, it locates the core of a nodule in a manner that is applicable to juxtapleural types using a transformation applied on the Euclidean distance transform of the foreground. Third, it detaches the nodule from attached structures by a region growing on the Euclidean distance map followed by a procedure to delineate the surface of the nodule based on the patterns of the region growing and distance maps. Finally, convex hull of the nodule surface intersected with the foreground constitutes the final segmentation. The performance of the technique is evaluated with two Lung Imaging Database Consortium (LIDC) data sets with 23 and 82 nodules each, and another data set with 820 nodules with manual diameter measurements. The experiments show that the algorithm is highly reliable in segmenting nodules of various types in a computationally efficient manner.

EFMI reference image database initiative: Concept, state and related work

The Working Group on Medical Image Processing (WG MIP, www.efmi-wg-mip.net) of the European Federation for Medical Informatics (EFMI) has started in 2002 an initiative with the aim to trigger the establishment of a reference image database for medical image processing research and development groups in order to support good validation and comparability of methods and systems. This paper summarizes the concepts of the initiative, describes its current state and presents an actual overview of related work.