A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications

DRCM: a disentangled representation network based on coordinate and multimodal attention for medical image fusion

Recent studies on medical image fusion based on deep learning have made remarkable progress, but the common and exclusive features of different modalities, especially their subsequent feature enhancement, are ignored. Since medical images of different modalities have unique information, special learning of exclusive features should be designed to express the unique information of different modalities so as to obtain a medical fusion image with more information and details. Therefore, we propose an attention mechanism-based disentangled representation network for medical image fusion, which designs coordinate attention and multimodal attention to extract and strengthen common and exclusive features. First, the common and exclusive features of each modality were obtained by the cross mutual information and adversarial objective methods, respectively. Then, coordinate attention is focused on the enhancement of the common and exclusive features of different modalities, and the exclusive features are weighted by multimodal attention. Finally, these two kinds of features are fused. The effectiveness of the three innovation modules is verified by ablation experiments. Furthermore, eight comparison methods are selected for qualitative analysis, and four metrics are used for quantitative comparison. The values of the four metrics demonstrate the effect of the DRCM. Furthermore, the DRCM achieved better results on SCD, Nabf, and MS-SSIM metrics, which indicates that the DRCM achieved the goal of further improving the visual quality of the fused image with more information from source images and less noise. Through the comprehensive comparison and analysis of the experimental results, it was found that the DRCM outperforms the comparison method

A review on the rule-based filtering structure with applications on computational biomedical images

concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases

A multimodal fusion method for Alzheimer’s disease based on DCT convolutional sparse representation

IntroductionThe medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer’s disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer’ s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed.MethodsThe algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images.Results and discussionThrough extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention

Fusion of Images and Videos using Multi-scale Transforms

This thesis deals with methods for fusion of images as well as videos using multi-scale transforms. First, a novel image fusion algorithm based primarily on an improved multi-scale coefficient decomposition framework is proposed. The proposed framework uses a combination of non-subsampled contourlet and wavelet transforms for the initial multi-scale decompositions. The decomposed multi-scale coefficients are then fused twice using various local activity measures. Experimental results show that the proposed approach performs better or on par with the existing state-of-the art image fusion algorithms in terms of quantitative and qualitative performance. In addition, the proposed image fusion algorithm can produce high quality fused images even with a computationally inexpensive two-scale decomposition. Finally, we extend the proposed framework to formulate a novel video fusion algorithm for camouflaged target detection from infrared and visible sensor inputs. The proposed framework consists of a novel target identification method based on conventional thresholding techniques proposed by Otsu and Kapur et al. These thresholding techniques are further extended to formulate novel region-based fusion rules using local statistical measures. The proposed video fusion algorithm, when used in target highlighting mode, can further enhance the hidden target, making it much easier to localize the hidden camouflaged target. Experimental results show that the proposed video fusion algorithm performs much better than its counterparts in terms of quantitative and qualitative results as well as in terms of time complexity. The relative low complexity of the proposed video fusion algorithm makes it an ideal candidate for real-time video surveillance applications

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI