A Brief Analysis of Multimodal Medical Image Fusion Techniques

Saleh, Mohammed Ali; Ali, AbdElmgeid A.; Ahmed, Kareem; Sarhan, Abeer M.

doi:10.3390/electronics12010097

Cited by 14 publications

(5 citation statements)

References 80 publications

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“…Therefore, multimodal fusion will become an important research direction to organically combine information from different modalities and provide more comprehensive and accurate medical image analysis results. Deep learning methods can be used to learn the correlations between multimodal data and perform feature extraction and fusion across modalities (Saleh et al, 2023). Finally, deep learning models are typically black boxes, and their decision-making process is difficult to explain and understand.…”

Section: Discussionmentioning

confidence: 99%

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

Chen,

Jin,

Liu

et al. 2023

Front. Artif. Intell.

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BackgroundWith 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.MethodsRetrieve 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.ResultsThe 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.ConclusionThe 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.

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Section: Discussionmentioning

confidence: 99%

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

Chen,

Jin,

Liu

et al. 2023

Front. Artif. Intell.

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“…Because non-smooth and smooth curves divide the many types of tissues in medical pictures, the textural features retrieved from these methods offer information about those tissues. Hybrid approaches, which combine the best aspects of each technique, are known to yield improved system performance [77][78][79][80][81][82][83][84][85][86][87].…”

Section: Feature Extractionmentioning

confidence: 99%

Multidirectional Analysis of Curvelet Against Skin Cancer

Kolekar,

Razak

et al. 2024

Preprint

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An elevated chance of getting another melanoma is associated with a personal history of the disease. Individuals who have already had a melanoma have a 2–5% probability of getting another one later. Compared to individuals whose initial melanoma was superficial spreading melanoma, those whose first melanoma was lentigo maligns melanoma or nodular melanoma are at a greater peril of emerging a secondary dominant cancer. Melanoma risk is double in those with a special antiquity of squamous cell carcinoma. The likelihood of getting melanoma is doubled if you have a particular times past of basal cell carcinoma. In addition, melanoma risk is higher in persons with actinic keratosis than in those without the condition. An automated technique for classifying melanoma, or skin cancer, is proposed in this work. An image of gathered data is used as the input for the proposed system, and various image handling methods remain smeared to improve the picture's characteristics. The curvelet technique is used to separate benign from malignant skin cancer and to collect relevant data from these pictures so that the classifier may be trained and tested. The basic wrapper curvelet's coefficients are the characteristics that are utilized for classification. Curvelet works well with images that have cartoon edges and aligned textures. In a database of digital photos, the three-layer back-propagation neural network classifier with curvelet has 75.6% recognition accuracy.

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“…WNC value for a median filter with different window sizes is 1, which is higher than that of existing techniques. This paper [13], presents a comprehensive overview of medical image fusion techniques, medical imaging modalities, medical image fusion steps and levels, and the MMIF assessment methodology. Numerous imaging techniques exist, including computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and single photon emission computed tomography (SPECT).…”

Section: B Frequency Domain Techniquesmentioning

confidence: 99%

Medical Image Fusion

Maurya,

Swarnkar,

Sharma

et al. 2024

Preprint

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Image fusion is a pivotal technique in medical imaging, particularly in combining complementary information from different modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) to enhance diagnostic accuracy. This paper presents a comprehensive review of CT and MRI image fusion methodologies, focusing on the integration of Wavelet Transform (WT) and Log Gabor Filter with Gaussian and Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement techniques. The Wavelet Transform has gained significant attention due to its multi-resolution analysis capability, enabling the decomposition of images into different frequency bands. Log Gabor filters, on the other hand, excel in capturing texture information with orientation selectivity. Integrating these techniques offers a synergistic approach for extracting and preserving relevant features from CT and MRI images. Moreover, Gaussian and CLAHE enhancement techniques are employed to improve the contrast and visibility of anatomical structures in both CT and MRI images. Gaussian filtering smoothens the images while preserving edges, whereas CLAHE enhances local contrast by adaptive histogram equalization. This review discusses the theoretical foundations, methodologies, and applications of CT and MRI image fusion techniques employing Wavelet Transform and Log Gabor Filter with Gaussian and CLAHE enhancement. Furthermore, the review highlights the significance of image quality assessment metrics in evaluating the performance of fusion techniques, including objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSI), and subjective evaluation through visual inspection by medical experts. At last, the integration of Wavelet Transform and Log Gabor Filter with Gaussian and CLAHE enhancement techniques holds immense potential in CT and MRI image fusion, offering enhanced diagnostic capabilities and improved clinical decision-making in medical imaging applications. Future research directions and challenges in this field are also discussed to stimulate further advancements in image fusion methodologies.

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A Brief Analysis of Multimodal Medical Image Fusion Techniques

Cited by 14 publications

References 80 publications

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

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

Multidirectional Analysis of Curvelet Against Skin Cancer

Medical Image Fusion

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