Multimodal Remote Sensing Image Matching Method Based on Improved Self‐Similarity Index Map and Absolute Phase Direction

The evolution and impact of the Society for Vascular Surgery Resident and Student Program

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks.

Histogram of the orientation of the weighted phase descriptor for multi-modal remote sensing image matching

Match-to-sample (MTS) has been demonstrated in dolphins using echolocation and across sensory modalities (e.g., visual to echolocation and vice-versa). To assess potential for bistatic MTS, a pilot study was conducted in which a dolphin listened to one of three recorded playbacks of echoes obtained from targets of different shape and material composition and was then asked to echo locate on a set of physical alternatives and choose the corresponding target. Training began with two alternatives and 50% errorless trials, where only the matching physical target was presented. The proportion of errorless trials was progressively reduced to 25%, 12.5%, and then 0% as a criterion of three sessions with >80% correct matching was met. Thereafter, all three alternatives were presented with 0% errorless trials. The dolphin consistently performed above chance over 34 sessions—average correct matching rate was 76% (range 67%–96%)—even though the projected sample echoes did not faithfully reproduce echoes obtained when the dolphin insonified the targets with echolocation clicks. Future testing with novel stimulus sets would be required to determine if and how a dolphin might spontaneously generalize between projected echo facsimiles and real world targets.Match-to-sample (MTS) has been demonstrated in dolphins using echolocation and across sensory modalities (e.g., visual to echolocation and vice-versa). To assess potential for bistatic MTS, a pilot study was conducted in which a dolphin listened to one of three recorded playbacks of echoes obtained from targets of different shape and material composition and was then asked to echo locate on a set of physical alternatives and choose the corresponding target. Training began with two alternatives and 50% errorless trials, where only the matching physical target was presented. The proportion of errorless trials was progressively reduced to 25%, 12.5%, and then 0% as a criterion of three sessions with >80% correct matching was met. Thereafter, all three alternatives were presented with 0% errorless trials. The dolphin consistently performed above chance over 34 sessions—average correct matching rate was 76% (range 67%–96%)—even though the projected sample echoes did not faithfully reproduce echoes obtained whe...

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixelto- pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

Three-dimensional reconstruction of aerosols based on spectral radiance matching

Unmanned Aerial Vehicle Remote Sensing (UAVRS) have developed rapidly driven mainly for military reconnaissance, earth observation and scientific data collection between military and civilian users over the past decade. However, automatic registration of UAVRS images has become a problem of blocks for the wide applications. In this paper, an algorithm based on both Random Sample Consensus (RANSAC) and least-squares method is proposed to improve the image registration performance of SIFT algorithm. On the one hand, RANSAC can remove inaccurate feature point pairs that SIFT detected. On the other hand, given all rough feature matches based on SIFT features, least-squares match is used to carry out precise matching. The experiment results show that our proposal can effectively estimate matching error with an average correct matching rate of 92.8%. And also the new algorithm had faster matching rate for the same number of images under the same experimental platform. As a result, the algorithm can improve greatly the accuracy of matching, but also to reduce the computation load based on the experiment results. Automatic registration of UAVRS images can be obtained in real time. After pre-matching by SIFT feature matching algorithm, the least squares matching is used to match accurately, which can be satisfied for the relative orientation of low-altitude remote sensing images automatically.

Robust registration of multi-modal remote sensing images based on multi-dimensional oriented self-similarity features

Relevance. The intraoral scanner is a device that provides an alternative to traditional dental impressions. Intraoral scanners are effective even in complex clinical cases; however, their application in patients with complete edentulism remains challenging.Objective. The study aims to evaluate the effectiveness of various scanning protocols for edentulous jaws using intraoral scanner.Materials and methods. The study utilized the Pumpa intraoral scanner to scan edentulous jaw models (Frasaco B-3). A reference scan was obtained using the Medit T710 laboratory scanner. Three scanning techniques were applied: Three-point scanning technique: Maxilla – the average match rate with the laboratory scanner was 77.20%; Mandible – 72.46%. Rocking motion-based technique: Maxilla – the average match rate with the laboratory scanner was 86.56%; Mandible – 65.44%. The second scanning technique for the mandible proved to be the riskiest in terms of results, as its use yielded a scan with a match rate with the laboratory scanner only 56%, which is unacceptable for further treatment planning and prosthetic rehabilitation. Combined technique: Maxilla – average match rate with the laboratory scanner was 89.00%; Mandible – 87.11%.Results. The combined scanning technique (third method) demonstrated the highest accuracy and is recommended for clinical use.Conclusion. The combined scanning technique is the most precise and should be adopted in clinical practice for scanning edentulous jaws.

BackgroundThis study elucidates recent trends in application and match rates in the Cardiovascular Disease Fellowship Match. We hypothesized that (1) match rates have increased with time; (2) match rates are highest for US allopathic graduates; and (3) most candidates match at 1 of their top 3 ranked fellowship choices.Methods and ResultsThis was a retrospective cohort study of all applicants in the Cardiovascular Disease Fellowship Match from 2010 to 2021 (n=14 674). Chi‐square tests were used to compare trends over time and match rates by applicant archetype (US allopathic graduates and non‐US allopathic graduates). The annual number of applicants increased from 1184 to 1575 (33% increase) while training positions increased 718 to 1045 (46% increase) over the study period. The percentage of applicants that matched increased from 61% in 2010 to 66% in 2021 (P=0.090). The average match rate was 70% over the study period. During each year, US allopathic graduates had higher match rates than non‐US allopathic graduates (P<0.001), but this disparity narrowed with time (83% versus 41% in 2010 and 83% versus 54% in 2021). Most applicants matched at 1 of their top 3 choices (first, 37%; second, 12%; third, 7%). Applicants matching at 1 of their top 3 choices decreased from 51% in 2010 to 48% in 2021 (P=0.704).ConclusionsThe Cardiovascular Disease Fellowship Match has remained equally competitive over the past decade. US allopathic graduates have an advantage over non‐US allopathic graduates. Most applicants match at 1 of their top 3 ranked fellowship choices.

To tackle the nonlinear radiation distortions (NRDs) in multimodal remote sensing images, this letter proposes a multimodal remote sensing image registration method that utilizes phase symmetry and rank-based local self-similarity (LSS). First, the local phase information of the images is utilized to construct a phase symmetry map, which is then used to detect features using the Features from Accelerated Segment Test (FAST) algorithm. Subsequently, a new feature descriptor that combines rank-based local self-similarity and phase congruency, is generated. Finally, the Fast Sample Consensus (FSC) method is employed to eliminate outliers. Experimental results on publicly available multimodal remote sensing image datasets demonstrate that the proposed method outperforms the state-of-the-art methods in terms of number of correct matches, precision, and root mean squared error (RMSE).

Multi-modal remote sensing image (MRSI) matching has always been a challenging task. Traditional image matching methods often fail to obtain satisfactory results in most cases due to temporal differences, complex geometric distortions, and non-linear radiometric differences (NRDs). The key to addressing MRSI matching lies in mitigating NRDs to achieve robust extraction and description of features. This paper proposes a multiscale phase-orientation guided feature transform (MS-POFT) for multi-modal image matching. Two novel strategies are investigated and integrated into MS-POFT to improve the matching performance. A phase-structured adaptive detection is designed by the complementation of phase stretching transform and adaptive sliding windows, which ensures stable feature point extraction across different scales. Then, a new feature descriptor suitable for multi-modal images, called MS-PGLOH, is constructed based on phase and gradient principal direction in multiscale space. We performed comparison experiments on various multimodal datasets from remote sensing, natural sceneries, night surveillance, medical and temporal changes. Our experimental results both in qualitative and quantitative ways show that our proposed MS-POFT outperforms other comparison methods. MS-POFT successfully matched all given image pairs, achieving satisfactory results in terms of the number of correct matches (NCM), proportion of corrections ratio (PCR), and a reduced root-mean-square error (RMSE) of approximately 1.36.

This study is to analyze academic success metrics of otolaryngology cancer research laboratory trainees. This is a retrospective analysis of trainee academic outcomes from 2000 to 2020. We examined careers of 99 trainees of the senior author's cancer research laboratory, including undergraduates, medical students, otolaryngology residents/fellows, and NIH T32 postdocs. We compared medical school matriculation rates, otolaryngology and head and neck surgery (OHNS) residency match rates, fellowship match rates, publication rates, and academic positions with publicly available data from the American Association of Medical Colleges (AAMC), National Resident Matching Program (NRMP), and others. Nine undergraduates achieved a 100% medical school matriculation rate compared with the AAMC rate of 43.4%. Of 22/53 medical students who applied to OHNS residency, students achieved a 100% match rate, compared with the NRMP match rate of 82.1%. Of 33 medical students who completed training, 45.4% became academicians, compared with 44.9% in an NIH R25E program cohort, which is notable. Interestingly, medical student trainees overall had more publications compared with incoming OHNS residents. Our residents achieved a 100% fellowship match rate compared with 79.0% average match rate across OHNS fellowship programs. Twelve of 25 (48%) residents earned advanced degrees (10 MS, 2 PhD), 11 of which were directly related to oncology. This was statistically significant compared to incoming otolaryngology residents via the NRMP data. Sixty percent (3/5) of surgical fellows entered academics. We observed multiple benefits from participation in our research laboratory at all levels of academic cancer training, specifically medical school and otolaryngology residency matriculation and academic faculty placement of former trainees.

Currently, multi-target survey telescope arrays play an important role in the build-up and maintenance of space object catalog databases, collecting massive observational data without attributing information. However, the matching process of massive observational data poses significant challenges to traditional prediction methods. To address the issues of low matching success rates and prolonged computation times in traditional methods, this paper proposes a multi-threaded data starting point matching method. First, orbital elements from the Space Surveillance and Tracking (SST) database are extracted for two days before and after the observation moment. A set of orbital elements closest to the observation epoch is filtered to form the primary candidate catalog containing the maximum number of objects. A matching error threshold is set. Second, multi-threaded traversal of the primary candidate catalog is performed to calculate observation residuals with the data starting point using an orbit prediction procedure. Orbital elements meeting the triple matching error threshold are selected to form the secondary candidate catalog, which is used in the entire data arc segment-matching calculation. Finally, the root mean square error (RMSE) of observation residuals for the entire data arc segment is computed point by point. The orbital elements satisfying the matching threshold are identified as matching results based on the principle of optimality. Experimental results demonstrate that with a matching error threshold of 1°, the proposed method achieves an average matching success rate of 97.62% for data arc segments with nearly 10,000 passes per day over 8 consecutive days. In the SST database containing an average of 25,720 targets, this method processes an average of 2164 data arc segments per minute, improving matching efficiency by 115 times compared to traditional prediction methods.

Background Most studies rely on clustered analyses to study how the characteristics of health facilities influence individual outcomes. Our aim was to perform a probabilistic linkage between individual and health facility data to enable individual-level analyses. Methods We linked data from the most recent female questionnaire from 11 countries monitored by the Performance Monitoring for Action 2020 to a master health facility dataset (appending all rounds of surveys). Only women that reported which type of facility they visited were considered in the analysis. A probabilistic linkage was performed using 13 blocking variables (e.g., facility type and cluster of residence/location of the woman/facility) and 11 matching variables (e.g., types of contraceptive methods used/offered by the women/facility). Each concordant matching variable received a + 1 score, or a 0 score otherwise. We assessed linkage quality by pooled odds ratio of non-matches according to wealth tertiles (richest vs. poorest) and area of residence (urban vs. rural) using a meta-analytical approach. Results A total of 21,102 women and 7,056 facilities were considered in the linkage process. The average match rate was 57.9%, ranging from 42.5% in Indonesia to 69.1% in Burkina Faso. The pooled odds of non-match were 74% higher for the richest women compared to the poorest, and 67% higher for women living in urban areas compared to rural areas. Conclusions High match rates were achieved in countries with sufficient information on public and private facilities. The lack of information about private facilities contributed to the higher odds of non-match among the better off. Key messages We performed a probabilistic linkage approach to link individual and health facility data, making it possible to understand how the characteristics of health facilities can influence individual-level outcomes. Our findings also bring light to the importance of sampling both public and private facilities, aiming to maximise match rates and reduce differences on match rates according to socio demographic characteristics of the sample.