Medical Imaging Methods Research Articles

Acute appendicitis in adults: only clinical and laboratory diagnostics, only imaging, or both?

The presented article is devoted to the discussion of the necessity and expediency of using medical visualization diagnostics — radiological imaging — of acute appendicitis as an independent or complex approach in combination with clinical and laboratory data. Until now, the clinical diagnosis of each individual case of acute appendicitis is complicated, and its formulation requires combining clinical, laboratory and imaging data, since it includes a wide range of differential diagnoses depending on age and gender. The literature provides data that the frequency of negative appendectomies based on the results of a clinical examination can be up to 28.2%. Radiological imaging is used as an adjunct to clinical diagnostics using ultrasound examination, computed tomography and magnetic resonance imaging, but the role of the latter is limited by the duration of the study, increased cost, unavailability around the clock, as well as the presence of absolute contraindications. Sonography and computed tomography in diagnostics have a number of advantages and disadvantages, and the question of choosing a specific method still causes scientific controversy. We also provide illustrative clinical examples demonstrating echographic and computed tomographic imaging manifestations of acute appendicitis, as well as discuss visualization signs. For a patient with suspected acute appendicitis, in order to increase the accuracy of diagnosis, reduce complications or the need of undergoing unnecessary diagnostic and therapeutic procedures, a multidisciplinary and multimodal approach is necessary, taking into account age, gender and constitution, however, the question of choosing the optimal method of medical imaging in the diagnosis of acute appendicitis between ultrasound and computed tomography remains debatable.

The use of medical imaging methods for percutaneous radiofrequency ablation of hepatic metastases of colorectal cancer

The use of medical imaging methods for percutaneous radiofrequency ablation of hepatic metastases of colorectal cancer

Few-shot classification of ultrasound breast cancer images using meta-learning algorithms

Medical datasets often have a skewed class distribution and a lack of high-quality annotated images. However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML) algorithms as meta-learning methods. The breast ultrasound images (BUSI) dataset, which has three classes and is difficult to use in meta-learning, was used for meta-testing in a cross-domain approach along with other datasets for meta-training. Our proposed approach yielded an accuracy range of 0.882–0.889, achieved by implementing the ResNet50 backbone with ProtoNet in a 10-shot setting. These results represent a significant improvement ranging from 6.27 to 7.10% over the baseline accuracy of 0.831. The results showed that ProtoNet outperformed the MAML method for all k-shot settings. In addition, the use of ResNet models as the backbone network for feature extraction was found to be more successful than the use of a four-layer convolutional model. Our proposed method is the first attempt to apply meta-learning for few-shot classification in the BUSI dataset while providing higher accuracy compared to deep learning methods for medical images with small-scale datasets and few classes. The methodology used in this study can be adapted to other datasets with similar problems.

Review of materials and technological solutions for creating phantoms used in computed tomography

The use of computed tomography during diagnostic examinations makes it a source of additional radiation exposure to patients. In this regard, the development of test objects (phantoms) that simulate the X-ray properties of tissues, including for preliminary assessment of the ionizing radiation distribution, becomes relevant. These test objects play an important role in quality control and the development of new medical imaging methods in conditions where test scans of patients are not possible. Although a range of ready-made solutions is available on the market, there is a lack of prototypes with a certain set of properties to test scientific and practical hypotheses in solving specific clinical and technical problems. Finding materials for a fast and inexpensive production process and studying their properties could provide insight into the effectiveness of their use in making phantoms. The purpose of the work is to search and analyze materials for creating phantoms used in computed tomography. The article discusses materials for the production of non-anthropomorphic and anthropomorphic phantoms, including those printed on a 3D printer. The development of three-dimensional printing has facilitated the transition from simple test objects to high-precision anthropomorphic phantoms made from tissue-mimicking materials that have equivalent signals on computer tomograms. Plastics, silicones, polyvinyl chloride, resins, liquids are used for visualizations identical to soft tissues; plastics, gypsum, photopolymers, potassium hydrogen orthophosphate, calcium hydroxyapatite, plexiglass — for hard tissues. Commercial phantoms are made from materials with reproducible, stable properties, but these same materials must be retested to create test objects specific to a particular clinical task.

Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients. The commonly-used medical imaging methods have limitations in distinguishing lung nodules to date. A new approach to this problem may be provided by electrical properties of lung nodules. Nevertheless, difference identification is the basis of correct distinction. So, this paper aims to investigate the differences in electrical properties between various lung nodules. At variance with existing studies, benign samples were included for analysis. A total of 252 specimens were collected, including 126 normal tissues, 15 benign nodules, 76 adenocarcinomas, and 35 squamous cell carcinomas. The dispersion properties of each tissue were measured over a frequency range of 100 Hz to 100 MHz. And the relaxation mechanism was analyzed by fitting the Cole-Cole plot. The corresponding equivalent circuit was estimated accordingly. Results validated the significant differences between malignant and normal tissue. Significant differences between benign and malignant lesions were observed in conductivity and relative permittivity. Adenocarcinomas and squamous cell carcinomas are significantly different in conductivity, first-order, second-order differences of conductivity, α-band Cole-Cole plot parameters and capacitance of equivalent circuit. The combination of the different features increased the tissue groups' differences measured by Euclidean distance up to 94.7%. In conclusion, the four tissue groups reveal dissimilarity in electrical properties. This characteristic potentially lends itself to future diagnosis of non-invasive lung cancer.

Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering.

With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies-pulse-coupled neural network based on the structure tensor and maximum based-are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method's outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.

GASTROINTESTINAL STROMAL TUMORS. COURSE PREDICTION AND DIAGNOSTIC ALGORITHMS

The purpose of the review is to compare modern algorithms for predicting the course of GIST.Material and methods. An information search was conducted using the PubMed, OVID, EMBASE, SRC BOLD databases using keywords that corresponded to the MESH terms «gastro-i ntestinal stromal tumors» AND «prognosis» AND «diagnosis». The search depth is 10 years. From the total array of 66 publications, 20, most relevant to the search subject, were selected. Information analysis was carried out in accordance with the PRISMA criteria.The results. A comparison of various medical imaging methods and models for predicting clinical outcomes in GIST was carried out. The main prognostic criteria are the size of the tumor, its localization and mitotic index. Criteria for assessing the response to treatment during MRI and PET were revised. The diff erences, advantages, and limitations of various prognosticators are discussed Conclusions. Gastrointestinal stromal tumors are a rare pathology, which, however, often has a progressive course. Existing models for predicting the course of GIST do not take into account genetic variants. The use of a multidisciplinary approach has signifi cant prospects in the selection of SPSHKT therapy.

BRAIN TUMOR DETECTION USING DEEP TRANSFER LEARNING

One of the leading causes of cancer-related death in humans is brain tumors. The process of diagnosing brain tumors and helping patients choose their course of treatment depends heavily on early identification. An innovative medical imaging method called Magnetic Reasoning Imaging (MRI) aids radiologists in locating the tumor spot. Manually testing the MRI pictures is a laborious process that needs experience. The development of computer-assisted diagnosis, machine learning, and deep learning in particular has made it possible for radiologists to more accurately diagnose brain tumors these days. The project employs Convolutional Neural Networks (CNNs) the classification of MRI images targeting four distinct types of brain tumors: gliomas, meningiomas, pituitary adenomas, and no tumor. By harnessing the power of deep learning, the model aims to enhance the precision and speed of tumor identification, contributing to early diagnosis and improved patient outcomes. But the traditional CNN has limitations, such as the need for large labeled datasets, employment of more layer of neurons, need for large computational resources, more time consumption and the risk of overfitting. To overcome these challenges, the project leverages transfer learning, a technique that allows the model to leverage knowledge gained from pre-trained networks on large datasets. This project involves the use of EfficientNetB0 architecture for the detection of brain tumor. This methodology demonstrates superior classification performance compared to conventional Neural Network. Therefore, this project presents a robust framework for brain tumor detection utilizing deep transfer learning, offering a promising avenue for improving diagnostic accuracy in the realm of neuroimaging. The adoption of transfer learning mitigates the drawbacks associated with CNNs, propelling the development of effective and reliable method for early detection and intervention in brain tumor cases. Key Words: MRI, CNN, Brain tumor, Transfer Learning, EffiecientNetB0.

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances.

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

Brain MRI and CT Image Fusion Using Multiscale Local Extrema and Image Statistics

In medical applications such as radiotherapy and guided-image surgery, data fusion for diagnostic imaging has emerged as a critical issue. Because the objective of medical image fusion is to improve patient diagnosis accuracy, the fused image is created by preserving the source images' prominent details and features. It has been demonstrated that the Multi-Level Local Extrema representation has numerous advantages over traditional image modeling approaches. We propose an innovative MLE-based fusion method for multimodal medical images in this paper. In the MLE schema, the proposed algorithm decomposes source images into coarse and detailed layers, then fuses the source images using weights calculated from these detail images using image statistics. We visually and quantitatively compared the efficacy of the suggested approach to that of existing methods using five different types of medical images from various sources. The experimental results showed that the proposed scheme outperforms other current typical schemes in terms of both qualitative image quality and objective evaluation.

Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks. Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

A target region extraction method for ultrasound medical images based on improvedPRIDNetandUCTransNet

AbstractComputer‐aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region extraction method combining the improved PRIDNet and UCTransNet for ultrasound medical images is proposed. The method sequentially enhances and segments the target region of the image, thereby solving the problem of inconspicuous target region caused by noise and artifacts in ultrasound images. There are three key characteristics: (i) The feature extraction part of the enhancement network (improved PRIDNet) is redesigned for speckle noise to improve the network's ability to extract information and highlight the feature information of the target region in ultrasound images. (ii) The segmentation network with the addition of the underlying information on UCTransNet would effectively improves Channel‐wise Cross fusion Transformer (CCT) and decoder feature fusion capability. (iii) By combining the enhancement network with the segmentation network, we can further improve the segmentation accuracy of the target region in the presence of noise interference. The experiments conducted on both UFSU that we prepared and on some public datasets including BUSI, FHC, and CT2US have demonstrated that The proposed method attains MIoU, DSC, Acc, and HD of 96.34%, 98.12%, 99.35%, and 8.64 in CT2US, respectively. The method significantly surpasses those certain state‐of‐the‐art methods, demonstrating its potential to offer valuable guidance for clinical treatment. The code will be publicly released athttps://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images.

Deep learning-based tooth segmentation methods in medical imaging: A review.

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

Explainable deep learning diagnostic system for prediction of lung disease from medical images

Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of the emerging technologies is the utility of Artificial Intelligence (AI) based on computer vision for processing wide varieties of medical imaging but AI methods without explainability are often treated as a black box. Based on a view to demystifying the rationale influencing AI decisions, this paper designed and developed a novel low-cost explainable deep-learning diagnostic tool for predicting lung disease from medical images. For this, we investigated explainable deep learning (DL) models (conventional DL and vision transformers (ViTs)) for performing prediction of the existence of pneumonia, COVID19, or no-disease from both original and data augmentation (DA)-based medical images (from two chest X-ray datasets). The results show that our experimental consideration of the DA that combines the impact of cropping, rotation, and horizontal flipping (CROP+ROT+HF) for transforming input images and then passed as input to an Inception-V3 architecture yielded a performance that surpasses all the ViTs and other conventional DL approaches in most of the evaluated performance metrics. Overall, the results suggest that the utility of data augmentation schemes aided the DL methods to yield higher classification accuracies. Furthermore, we compared five different class activation mapping (CAM) algorithms (GradCAM, GradCAM++, EigenGradCAM, AblationCAM, and RandomCAM). The result shows that most of the examined CAM algorithms were effective in identifying the attention region containing the existence of pneumonia or COVID-19 from the medical images (chest X-rays). Our developed low-cost AI diagnostic tool (pilot system) can assist medical experts and radiographers in proffering early diagnosis of lung disease. For this, we selected five to seven deep learning models and the explainable algorithms were deployed on a novel web interface implemented via a Gradio framework.

Individual age determination based on computed tomography knee analysis using artificial neural networks and computer vision: Preliminary results

BACKGROUND: Currently, studies have focused on the modernization of existing methods of forensic age assessment (bone and skeletal) through the active use of modern methods of medical imaging (e.g., computed tomography) and artificial intelligence for their analysis. This approach enables the creation of new methods for assessing biological age, which is characterized by increased accuracy and reproducibility.
 AIM: To develop and test an algorithm for predicting the biological age of an individual based on computed tomography analysis of the knee joint using artificial neural networks and computer vision.
 MATERIALS AND METHODS: This observational retrospective transverse (one time) study analyzed computed tomography scans (334) of the knee joint performed in the Departments of Radiation Diagnostics of the Priorov Central Institute for Trauma and Orthopedics, Vreden National Medical Center for Traumatology and Orthopedics, between 2018 and 2021. The study enrolled persons of both sexes aged 13–45 years. Cases of developmental abnormalities, knee injuries, signs of general connective tissue pathology were excluded. Research methods include the use of intelligent information technologies (a formalized set of mathematical and software solutions).
 RESULTS: Based on the experiments conducted, an algorithm for assessing age according to the computed tomography scans of the knee joint has been developed. The main components of the developed system are as follows: a preprocessing module, an intelligent computing core, a data analysis module, a three-dimensional reconstruction module, a property extraction module, and a final age assessment module. The essence of the proposed method is the simultaneous use of artificial neural networks and clearly formalized mathematical procedures for calculating the properties of the epiphyseal line. To obtain the results and conduct primary experimental studies that confirmed the feasibility, correctness, and operability of the method, software using the YOLOv5 neural network was developed. The result of the error matrix analysis after training shows a probability of correct recognition of the order of 80%. Verification of experimental studies was performed on 46 cases. At present, the age estimation error is approximately 1 year for children and adolescents.
 CONCLUSIONS: The experimental results have confirmed the adequacy of the age estimates obtained to the actual age of the individual and, consequently, the applicability of the proposed method in forensic medical institutions. The proposed method is currently implemented as a set of software components with subsequent manual integration of automatically calculated data. The plan was to supplement the database of computed tomography images to increase the training sample and the accuracy of age prediction.

STBI-GAN: An adversarial learning approach for data synthesis on traumatic brain segmentation

Automatic brain segmentation of magnetic resonance images (MRIs) from severe traumatic brain injury (sTBI) patients is critical for brain abnormality assessments and brain network analysis. Construction of sTBI brain segmentation model requires manually annotated MR scans of sTBI patients, which becomes a challenging problem as it is quite impractical to implement sufficient annotations for sTBI images with large deformations and lesion erosion. Data augmentation techniques can be applied to alleviate the issue of limited training samples. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to synthesize the deformation and lesions in traumatic brains, which limits the performance of the subsequent segmentation task. To address these issues, we propose a novel medical image inpainting model named sTBI-GAN to synthesize labeled sTBI MR scans by adversarial inpainting. The main strength of our sTBI-GAN method is that it can generate sTBI images and corresponding labels simultaneously, which has not been achieved in previous inpainting methods for medical images. We first generate the inpainted image under the guidance of edge information following a coarse-to-fine manner, and then the synthesized MR image is used as the prior for label inpainting. Furthermore, we introduce a registration-based template augmentation pipeline to increase the diversity of the synthesized image pairs and enhance the capacity of data augmentation. Experimental results show that the proposed sTBI-GAN method can synthesize high-quality labeled sTBI images, which greatly improves the 2D and 3D traumatic brain segmentation performance compared with the alternatives. Code is available at .

Cetacean Optimization Based Medical Image Contrast-Enhancement Technique for Improving Disease Diagnosis in Cardiac MRI

Conventional preprocessing methods in medical imaging can effectively reduce the combination of Gaussian and Impulsive noise, while still maintaining the crucial features such as edges within the images. However, these techniques are found to lag in the optimizing the certain image parameters which play a crucial rule in enhancing the input images and thus may lead to failing in preserving the edges that may adversely affect the early diagnosis of diseases. An attempt has been made to enhance the contrast of cardiac MR (Medical Resonance) images using Cetacean Optimization Algorithm (COA) for improving disease diagnosis. Input cardiac images have been acquired from the free online available databases viz., SCMR consensus and AMRG Atlas. The methodology includes integration of the cost function with contrast measure based on certain metrics to create a newer transformation function. It results in enhanced search pattern using a transformation function in spatial domain and improves the pixel intensity to increase the resolution of an image. The developed algorithm’s performance was evaluated by computing various parameters including peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), mean square error (MSE), and normalized absolute error (NAE). Analysis of the results reveals that the proposed method achieves the highest PSNR (72 dB) and SSIM (0.99) values, along with the lowest NAE (0.16%) and MSE (0.22%) compared to both the particle swarm optimized texture-based histogram equalization (PSOTHE) method and the modified sunflower optimization (MSFO) method. The results indicate that the suggested strategy might improve the intricate details of the input cardiac MRI images. This enhancement can contribute to the efficient identification of cardiac disorder-specific biomarkers.

Integrated Boundary-Overlap-Size Metric for Local Assessment of Deep Learning Methods in Medical Microwave Imaging

Integrated Boundary-Overlap-Size Metric for Local Assessment of Deep Learning Methods in Medical Microwave Imaging