Integrated image and location analysis for wound classification: a deep learning approach.

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

[Regulatory effect of moxibustion on LTB4/MMP-9 in serum of patients with rheumatoid arthritis].

OBJECTIVE: To observe the effects of moxibustion on the contents of leukotriene B4 (LTB4), interleukin-17 (IL-17), tumor necrosis factor-α (TNF-α) and matrix metalloproteinase -9 (MMP-9) in serum, and explore the protection mechanisms of moxibustion in the patients with rheumatoid arthritis (RA). METHODS: A total of 64 patients with RA were randomly divided into treatment group (n=31) and control group (n=33). The patients in the control group were treated with conventional medication for consecutive 5 weeks. Based on the treatment in the control group, the patients in the treatment group were treated with moxibustion at bilateral Shenshu (BL23), Zusanli (ST36) and Ashi points, 3 times a week, for consecutive 5 weeks. Separately, the visual analogue scale (VAS) score, morning stiffness score, the number of tender joints, the number of swollen joints, the score of the disease activity score of 28 joints (DAS28) were observed; the contents of rheumatoid factor (RF), erythrocyte sedimentation rate (ESR) and C-reative protein (CRP) in serum were determined by biochemical method; and the contents of LTB4, IL-17, TNF-α and MMP-9 in serum were detected by using ELISA before and after treatment in the patients of both groups. RESULTS: After treatment, VAS score, morning stiffness score, the number of tender joints, the number of swollen joints, DAS28 score, the contents of serum RF in both groups, and contents of serum CRP, ESR, LTB4, IL-17, TNF-α and MMP-9 in the treatment group were significantly reduced when compared with those before treatment (P<0.01, P<0.05). After treatment, VAS score, morning stiffness score, the number of tender joints, the number of swollen joints, DAS28 score, and the levels of LTB4, IL-17 and MMP-9 in serum were obviously lower in the treatment group when compared with the control group (P<0.01, P<0.05). In the treatment group, the changes before and after treatment in the levels of LTB4, IL-17 and TNF-α were positively correlated with that of MMP-9 (P<0.05, r>0). CONCLUSION: Moxibustion at BL23 and ST36 combined with conventional medication significantly relieves joint pain and reduce disease activity in RA patients, which may be related to the modulation of LTB4, IL-17 and MMP-9 by moxibustion.

Multi-modal wound classification using wound image and location by deep neural network.

Wound classification is an essential step of wound diagnosis. An efficient classifier can assist wound specialists in classifying wound types with less financial and time costs and help them decide on an optimal treatment procedure. This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also developed to prepare the location data, which can help wound specialists tag wound locations more efficiently. Three datasets containing images and their corresponding location information were designed with the help of wound specialists. The multi-modal network was developed by concatenating the image-based and location-based classifier outputs with other modifications. The maximum accuracy on mixed-class classifications (containing background and normal skin) varies from 82.48 to 100% in different experiments. The maximum accuracy on wound-class classifications (containing only diabetic, pressure, surgical, and venous) varies from 72.95 to 97.12% in various experiments. The proposed multi-modal network also showed a significant improvement in results from the previous works of literature.

Multi-Barley Seed Detection Using iPhone Images and YOLOv5 Model.

As a raw material for beer, barley seeds play a critical role in producing beers with various flavors. Unexcepted mixed varieties of barley seeds make malt quality uncontrollable and can even destroy beer flavors. To ensure the quality and flavor of malts and beers, beer brewers will strictly check the appropriate varieties of barley seeds during the malting process. There are wide varieties of barley seeds with small sizes and similar features. Professionals can visually distinguish these varieties, which can be tedious and time-consuming and have high misjudgment rates. However, biological testing requires professional equipment, reagents, and laboratories, which are expensive. This study aims to build an automatic artificial intelligence detection method to achieve high performance in multi-barley seed datasets. There are nine varieties of barley seeds (CDC Copeland, AC Metcalfe, Hockett, Scarlett, Expedition, AAC Synergy, Celebration, Legacy, and Tradition). We captured images of these original barley seeds using an iPhone 11 Pro. This study used two mixed datasets, including a single-barley seed dataset and a multi-barley seed dataset, to improve the detection accuracy of multi-barley seeds. The multi-barley seed dataset had random amounts and varieties of barley seeds in each image. The single-barley seed dataset had one barley seed in each image. Data augmentation can reduce overfitting and maximize model performance and accuracy. Multi-variety barley seed recognition deploys an efficient data augmentation method to effectively expand the barley dataset. After adjusting the hyperparameters of the networks and analyzing and augmenting the datasets, the YOLOv5 series network was the most effective in training the two barley seed datasets and achieved the highest performance. The YOLOv5x6 network achieved the second highest performance. The mAP (mean Average Precision) of the trained YOLOv5x6 was 97.5%; precision was 98.4%; recall was 98.1%; the average speed of image detection reached 0.024 s. YOLOv5x6 only trained the multi-barley seed dataset; the trained performance was greater than that of the YOLOv5 series. The two datasets had 39.5% higher precision, 27.1% higher recall, and 40.1% higher mAP than when just using the original multi-barley seed dataset. The multi-barley seed detection results showed high performance, robustness, and speed. Therefore, malting and brewing industries can assess the original barley seed quality with the assistance of fast, intelligent, and detected multi-barley seed images.

Aberrant interhemispheric functional reciprocities of the default mode network and motor network in subcortical ischemic stroke patients with motor impairment: A longitudinal study.

Purpose: The purpose of the present study was to explore the longitudinal changes in functional homotopy in the default mode network (DMN) and motor network and its relationships with clinical characteristics in patients with stroke. Methods: Resting-state functional magnetic resonance imaging was performed in stroke patients with subcortical ischemic lesions and healthy controls. The voxel-mirrored homotopic connectivity (VMHC) method was used to examine the differences in functional homotopy in patients with stroke between the two time points. Support vector machine (SVM) and correlation analyses were also applied to investigate whether the detected significant changes in VMHC were the specific feature in patients with stroke. Results: The patients with stroke had significantly lower VMHC in the DMN and motor-related regions than the controls, including in the precuneus, parahippocampus, precentral gyrus, supplementary motor area, and middle frontal gyrus. Longitudinal analysis revealed that the impaired VMHC of the superior precuneus showed a significant increase at the second time point, which was no longer significantly different from the controls. Between the two time points, the changes in VMHC in the superior precuneus were significantly correlated with the changes in clinical scores. SVM analysis revealed that the VMHC of the superior precuneus could be used to correctly identify the patients with stroke from the controls with a statistically significant accuracy of 81.25% (P ≤ 0.003). Conclusions: Our findings indicated that the increased VMHC in the superior precuneus could be regarded as the neuroimaging manifestation of functional recovery. The significant correlation and the discriminative power in classification results might provide novel evidence to understand the neural mechanisms responsible for brain reorganization after stroke.

Ability of an altered functional coupling between resting-state networks to predict behavioral outcomes in subcortical ischemic stroke: A longitudinal study.

Stroke can be viewed as an acute disruption of an individual's connectome caused by a focal or widespread loss of blood flow. Although individuals exhibit connectivity changes in multiple functional networks after stroke, the neural mechanisms that underlie the longitudinal reorganization of the connectivity patterns are still unclear. The study aimed to determine whether brain network connectivity patterns after stroke can predict longitudinal behavioral outcomes. Nineteen patients with stroke with subcortical lesions underwent two sessions of resting-state functional magnetic resonance imaging scanning at a 1-month interval. By independent component analysis, the functional connectivity within and between multiple brain networks (including the default mode network, the dorsal attention network, the limbic network, the visual network, and the frontoparietal network) was disrupted after stroke and partial recovery at the second time point. Additionally, regression analyses revealed that the connectivity between the limbic and dorsal attention networks at the first time point showed sufficient reliability in predicting the clinical scores (Fugl-Meyer Assessment and Neurological Deficit Scores) at the second time point. The overall findings suggest that functional coupling between the dorsal attention and limbic networks after stroke can be regarded as a biomarker to predict longitudinal clinical outcomes in motor function and the degree of neurological functional deficit. Overall, the present study provided a novel opportunity to improve prognostic ability after subcortical strokes.

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review.

Significance: Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big datasets over time. Developing novel artificial intelligence (AI) systems can help clinicians diagnose, assess the effectiveness of therapy, and predict healing outcomes. Recent Advances: Rapid developments in computer processing have enabled the development of AI-based systems that can improve the diagnosis and effectiveness of therapy in various clinical specializations. In the past decade, we have witnessed AI revolutionizing all types of medical imaging like X-ray, ultrasound, computed tomography, magnetic resonance imaging, etc., but AI-based systems remain to be developed clinically and computationally for high-quality wound care that can result in better patient outcomes. Critical Issues: In the current standard of care, collecting wound images on every clinical visit, interpreting and archiving the data are cumbersome and time consuming. Commercial platforms are developed to capture images, perform wound measurements, and provide clinicians with a workflow for diagnosis, but AI-based systems are still in their infancy. This systematic review summarizes the breadth and depth of the most recent and relevant work in intelligent image-based data analysis and system developments for wound assessment. Future Directions: With increasing availabilities of massive data (wound images, wound-specific electronic health records, etc.) as well as powerful computing resources, AI-based digital platforms will play a significant role in delivering data-driven care to people suffering from debilitating chronic wounds.

Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks.

Wound prognostic models not only provide an estimate of wound healing time to motivate patients to follow up their treatments but also can help clinicians to decide whether to use a standard care or adjuvant therapies and to assist them with designing clinical trials. However, collecting prognosis factors from Electronic Medical Records (EMR) of patients is challenging due to privacy, sensitivity, and confidentiality. In this study, we developed time series medical generative adversarial networks (GANs) to generate synthetic wound prognosis factors using very limited information collected during routine care in a specialized wound care facility. The generated prognosis variables are used in developing a predictive model for chronic wound healing trajectory. Our novel medical GAN can produce both continuous and categorical features from EMR. Moreover, we applied temporal information to our model by considering data collected from the weekly follow-ups of patients. Conditional training strategies were utilized to enhance training and generate classified data in terms of healing or non-healing. The ability of the proposed model to generate realistic EMR data was evaluated by TSTR (test on the synthetic, train on the real), discriminative accuracy, and visualization. We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application. Using the generated samples in training predictive models improved the classification accuracy by 6.66-10.01% compared to the previous EMR-GAN. Additionally, the suggested prognosis classifier has achieved the area under the curve (AUC) of 0.875, 0.810, and 0.647 when training the network using data from the first three visits, first two visits, and first visit, respectively. These results indicate a significant improvement in wound healing prediction compared to the previous prognosis models.

The disrupted topological properties of structural networks showed recovery in ischemic stroke patients: a longitudinal design study.

INTRODUCTION: Stroke is one of the leading causes of substantial disability worldwide. Previous studies have shown brain functional and structural alterations in adults with stroke. However, few studies have examined the longitudinal reorganization in whole-brain structural networks in stroke. METHODS: Here, we applied graph theoretical analysis to investigate the longitudinal topological organization of white matter networks in 20 ischemic stroke patients with a one-month interval between two timepoints. Two sets of clinical scores, Fugl-Meyer motor assessment (FMA) and neurological deficit scores (NDS), were assessed for all patients on the day the image data were collected. RESULTS: The stroke patients exhibited significant increases in FMA scores and significant reductions in DNS between the two timepoints. All groups exhibited small-world organization (σ > 1) in the brain structural network, including a high clustering coefficient (γ > 1) and a low normalized characteristic path length (λ ≈ 1). However, compared to healthy controls, stroke patients showed significant decrease in nodal characteristics at the first timepoint, primarily in the right supplementary motor area, right middle temporal gyrus, right inferior parietal lobe, right postcentral gyrus and left posterior cingulate gyrus. Longitudinal results demonstrated that altered nodal characteristics were partially restored one month later. Additionally, significant correlations between the nodal characteristics of the right supplementary motor area and the clinical scale scores (FMA and NDS) were observed in stroke patients. Similar behavioral-neuroimaging correlations were found in the right inferior parietal lobe. CONCLUSION: Altered topological properties may be an effect of stroke, which can be modulated during recovery. The longitudinal results and the neuroimaging-behavioral relationship may provide information for understanding brain recovery from stroke. Future studies should detect whether observed changes in structural topological properties can predict the recovery of daily cognitive function in stroke.

Identification and analysis of misclassified work-zone crashes using text mining techniques.

Work zone safety management and research relies heavily on the quality of work zone crash data. However, it is possible that a police officer may misclassify a crash in structured data due to: restrictive options in the crash report; a lack of understanding about their importance; lack of time due to police officers' work load; and ignorance of work zone as one of the crash contributing factors. Consequently, work zone crashes are under representative in crash statistics. Crash narratives contain valuable information that is not included in the structured data. The objective of this study is to develop a classifier that applies text mining techniques to quickly find missed work zone (WZ) crashes through the unstructured text saved in the crash narratives. The study used three-year crash data from 2017 to 2019. The data from 2017 to 2018 was used as training data, and the 2019 data was used as testing data. A unigram + bigram noisy-OR classifier was developed and proven to be an efficient and effective means of classifying work zone crashes based on key information in the crash narrative. The ad-hoc analysis of misclassified work zone crashes sheds light on when, where and the plausible reasons as to why work zone crashes are more likely to be missed.

Multiclass wound image classification using an ensemble deep CNN-based classifier.

Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually. Wound classification is a key step in wound diagnosis that would help clinicians to identify an optimal treatment procedure. Hence, having a high-performance classifier assists wound specialists to classify wound types with less financial and time costs. Different wound classification methods based on machine learning and deep learning have been proposed in the literature. In this study, we have developed an ensemble Deep Convolutional Neural Network-based classifier to categorize wound images into multiple classes including surgical, diabetic, and venous ulcers. The output classification scores of two classifiers (namely, patch-wise and image-wise) are fed into a Multilayer Perceptron to provide a superior classification performance. A 5-fold cross-validation approach is used to evaluate the proposed method. We obtained maximum and average classification accuracy values of 96.4% and 94.28% for binary and 91.9% and 87.7% for 3-class classification problems. The proposed classifier was compared with some common deep classifiers and showed significantly higher accuracy metrics. We also tested the proposed method on the Medetec wound image dataset, and the accuracy values of 91.2% and 82.9% were obtained for binary and 3-class classifications. The results show that our proposed method can be used effectively as a decision support system in classification of wound images or other related clinical applications.

Effect of Moxibustion on ß-EP and Dyn Levels of Pain-Related Indicators in Patients with Rheumatoid Arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic immunodeficiency disease characterized by persistent synovial inflammation, pannus formation, and bone and cartilage destruction, resulting in joint malformations and function decline. OBJECTIVE: The purpose of this study is to evaluate the effect of moxibustion on clinical symptoms and levels of pain-related indicators beta-endorphin (ß-EP) and dynorphin (Dyn) in patients with RA and to explore the potential anti-inflammatory and analgesic mechanisms of moxibustion in RA treatment. METHODS: A total of 64 patients with RA who met the inclusion criteria were randomly and equally classified into the control and treatment groups. The control group received conventional treatment (oral methotrexate, folate, or leflunomide prescribed for a long time). The treatment group was treated with moxibustion at ST36 (Zusanli), BL23 (Shenshu), and Ashi points with respect to the control group. Patients' clinical symptoms and routine inspection indexes (rheumatoid factor [RF], erythrocyte sedimentation rate [ESR], and C-reactive protein [CRP]) were recorded before and after treatment. Serum levels of tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), ß-EP, and Dyn were determined by enzyme-linked immunosorbent assay (ELISA). The software SPSS24.0 was used for statistical analysis. RESULTS: (1) Compared with the pretreatment result, both of the two groups' clinical symptoms and routine inspection indexes (RF, ESR, and CRP) improved (P < 0.05), and the improvement of clinical symptoms in the treatment group outperformed that in the control group (P < 0.05). (2) TNF-α and IL-1ß levels decreased significantly in the treatment group after treatment (P < 0.01), while no significant difference was observed in the control group (P > 0.05). (3) ß-EP and Dyn levels in the treatment group were significantly increased after treatment (P < 0.01, P < 0.01), but the control group showed no significant difference (P > 0.05, P > 0.05). It is worth mentioning that the serum TNF-α, IL-1ß, ß-EP, and Dyn levels between the two groups were significantly different after 8 weeks of treatment (P < 0.05). (4) Differences in the serum ß-EP and Dyn levels in the patients of the treatment group were correlated with TNF-α and IL-1ß levels after treatment, and the correlation was mainly negative (r < 0). CONCLUSION: Moxibustion can improve joint pain in patients with RA using conventional western medicine. One of the mechanisms may affect the serum ß-EP and Dyn levels by downregulating the inflammatory factors to play an anti-inflammatory and analgesic role.

Fully automatic wound segmentation with deep convolutional neural networks.

Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation .

Effect of Moxibustion on the Serum Levels of MMP-1, MMP-3, and VEGF in Patients with Rheumatoid Arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, which will eventually lead to joints deformity and functional damage. The aim of this research is to evaluate the effect of moxibustion on the serum indicators related to bone and cartilage metabolism, matrix metalloproteinase 1 (MMP-1), matrix metalloproteinase 3 (MMP-3), and vascular endothelial growth factor (VEGF) in patients with RA and to explore the mechanism of moxibustion in the treatment of RA. METHODS: We recruited 70 RA patients who met the inclusion criteria, and they were randomly divided into two groups, a treatment group and a control group in equal ratio. The control group took methotrexate, folate, or leflunomide orally, while the treatment group received methotrexate, folate, or leflunomide orally and moxibustion at ST36 (Zusanli), BL23 (Shen shu), and Ashi points. We compared the clinical symptoms, RA serological disease markers and serum contents of interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), MMP-1, MMP-3, and VEGF of RA patients before and after treatment. RESULTS: (1) The clinical symptoms and RA serological disease markers of the two groups improved after treatment (P < 0.05), while the clinical symptoms of the treatment group were significantly improved in comparison with the control group (P < 0.05). (2) The levels of IL-1ß, TNF-α, and VEGF decreased in both groups after treatment (P < 0.05), but the treatment group was significantly decreased compared with the control group (P < 0.05). (3) There were significant differences in MMP-1 and MMP-3 contents after treatment in the treatment group (P < 0.05, P < 0.05), while there were no significant differences in the control group (P > 0.05, P > 0.05). Above all, the contents of IL-1ß, TNF-α, MMP-1, MMP-3, and VEGF in the treatment group decreased more significantly than those in the control group (P < 0.05). CONCLUSION: The improvement effect of moxibustion on the clinical symptoms of RA patients may be related to influence on the contents of IL-1ß, TNF-α, MMP-1, MMP-3, and VEGF, and moxibustion may play a potential role in bone protection.

A bibliometric analysis and visualization of medical data mining research.

BACKGROUND: Data mining technology used in the field of medicine has been widely studied by scholars all over the world. But there is little research on medical data mining (MDM) from the perspectives of bibliometrics and visualization, and the research topics and development trends in this field are still unclear. METHODS: This paper has applied bibliometric visualization software tools, VOSviewer 1.6.10 and CiteSpace V, to study the citation characteristics, international cooperation, author cooperation, and geographical distribution of the MDM. RESULTS: A total of 1575 documents are obtained, and the most frequent document type is article (1376). SHAN NH is the most productive author, with the highest number of publications of 12, and the Gillies's article (750 times citation) is the most cited paper. The most productive country and institution in MDM is the USA (559) and US FDA (35), respectively. The Journal of Biomedical Informatics, Expert Systems with Applications and Journal of Medical Systems are the most productive journals, which reflected the nature of the research, and keywords "classification (790)" and "system (576)" have the strongest strength. The hot topics in MDM are drug discovery, medical imaging, vaccine safety, and so on. The 3 frontier topics are reporting system, precision medicine, and inflammation, and would be the foci of future research. CONCLUSION: The present study provides a panoramic view of data mining methods applied in medicine by visualization and bibliometrics. Analysis of authors, journals, institutions, and countries could provide reference for researchers who are fresh to the field in different ways. Researchers may also consider the emerging trends when deciding the direction of their study.

Effect of Moxibustion on HIF-1α and VEGF Levels in Patients with Rheumatoid Arthritis.

Background: Moxibustion has a therapeutic effect of reducing swelling and relieving pain in patients with rheumatoid arthritis (RA) but its mechanism is uncertain. Objective: To evaluate the effect of moxibustion on serum levels of hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in patients with RA and to explore the possible mechanism of moxibustion. Methods: This study involved 46 RA patients who had fulfilled the inclusion criteria and were randomly assigned to a treatment group and a control group in an equal ratio. The control group was treated with methotrexate or leflunomide, while the treatment group received methotrexate or leflunomide and moxibustion at ST 36 (Zusanli), BL 23 (Shenshu), and Ashi points. Patients' clinical symptoms, RA-associated serum markers, and serum levels of TNF-α, IL-1ß, HIF-1α, and VEGF were compared in the two groups before and after intervention. Statistical analysis was performed using SPSS 21.0 statistical software. Results: 37 of 46 RA patients eventually completed the whole treatment course. Compared with the control group, the treatment group significantly improved the clinical symptoms (P < 0.05) but with no significant differences in RA-associated serum markers (P > 0.05). There were significant differences in TNF-α and IL-1ß among the groups after 8 weeks of treatment (P < 0.05). HIF-1α and VEGF were decreased in the treatment group after therapy (P < 0.05). VEGF was reduced in the control group (P < 0.05), while HIF-1α was not significantly improved (P > 0.05). The reductions of HIF-1α and VEGF in the treatment group were superior to the control group (P < 0.05). Conclusions: Moxibustion enhanced the anti-inflammatory and analgesic effects of conventional medicine and can enhance the effect of conventional medicine, downregulating HIF-1α/VEGF contents to inhibit angiogenesis.

Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach.

Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images.

Slanted support window-based stereo matching for surface reconstruction of microscopic samples.

This work is to address the limitations of 2D Scanning Electron Microscopy (SEM) micrographs in providing 3D topographical information necessary for various types of analysis in biological and biomedical sciences as well as mechanical and material engineering by investigating modern stereo vision methodologies for 3D surface reconstruction of microscopic samples. To achieve this, micrograph pairs of the microscopic samples are acquired by utilizing an SEM equipped with motor controlled specimen stage capable of precise translational, rotational movements and tilting of the specimen stage. After pre-processing of the micrographs by SIFT feature detection/description followed by RANSAC for matching outlier removal and stereo rectification, a dense stereo matching methodology is utilized which takes advantage of slanted support window formulation for sub-pixel accuracy stereo matching of the input images. This results in a dense disparity map which is used to determine the true depth/elevation of individual surface points. This is a major improvement in comparison to previous matching methodologies which require additional post-processing refinement steps to reduce the negative effects of discrete disparity assignment or the blurring artifacts in near the edge regions. The provided results are great representatives of the superior performance of the slanted support window assumption employed here for surface reconstruction of microscopic samples.

Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation.

Scanning Electron Microscope (SEM) as one of the major research and industrial equipment for imaging of micro-scale samples and surfaces has gained extensive attention from its emerge. However, the acquired micrographs still remain two-dimensional (2D). In the current work a novel and highly accurate approach is proposed to recover the hidden third-dimension by use of multi-view image acquisition of the microscopic samples combined with pre/post-processing steps including sparse feature-based stereo rectification, nonlocal-based optical flow estimation for dense matching and finally depth estimation. Employing the proposed approach, three-dimensional (3D) reconstructions of highly complex microscopic samples were achieved to facilitate the interpretation of topology and geometry of surface/shape attributes of the samples. As a byproduct of the proposed approach, high-definition 3D printed models of the samples can be generated as a tangible means of physical understanding. Extensive comparisons with the state-of-the-art reveal the strength and superiority of the proposed method in uncovering the details of the highly complex microscopic samples.

SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples.

Scanning electron microscopy (SEM) imaging has been a principal component of many studies in biomedical, mechanical, and materials sciences since its emergence. Despite the high resolution of captured images, they remain two-dimensional (2D). In this work, a novel framework using sparse-dense correspondence is introduced and investigated for 3D reconstruction of stereo SEM images. SEM micrographs from microscopic samples are captured by tilting the specimen stage by a known angle. The pair of SEM micrographs is then rectified using sparse scale invariant feature transform (SIFT) features/descriptors and a contrario RANSAC for matching outlier removal to ensure a gross horizontal displacement between corresponding points. This is followed by dense correspondence estimation using dense SIFT descriptors and employing a factor graph representation of the energy minimization functional and loopy belief propagation (LBP) as means of optimization. Given the pixel-by-pixel correspondence and the tilt angle of the specimen stage during the acquisition of micrographs, depth can be recovered. Extensive tests reveal the strength of the proposed method for high-quality reconstruction of microscopic samples.