Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

The heart anomalies detection is a significant task in cardiac medical research. The CT, ULTRASOUND, CTA and MRI scans have been used to detect heart diseases but giving false experimental outcomes in longer time of conversion (ToC). Therefore, patients haven’t getting better treatment from doctors. So that in this research work an ultrasound image scan-based heart disease prediction and classification is performed with deep learning technology. The LeNet 10 deep learning classifier has been trained Kaggle dataset using appropriate CNN layers. Proposed CNN LeNet -10 is a 165 layers technology consists of flattened layer, dense layer, convolution layer, max pooling layer and etc. Classification and feature extraction has been performed to loading with LeNet-10 architecture. The real time heart ultrasound test images are collecting from Manipal super specialty hospital Vijayawada, these test features are managed to test.CSV file. In pre-processing step, Ostu segmentation and histogram equalization is applied to make heart ultrasound images to be clear. In Segmentation, edge and region-based convolutional steps are applied such that deep features have been identified. LeNet-10 classification is used to find affected area as well as abnormality location. Finally proposed deep learning with confusion matrix can generating application measures. Implementation has been performed on python 3.9 and DL (Deep learning) packages like TensorFlow, keras, sklearn and etc. The measures like Accuracy 98.37%, sensitivity 97.81%, Recall 98.34% and F1 score 98.98% had been attained, proposed heart disease estimation application is more robust and compete with present technology

A Real and Accurate Ultrasound Fetal Imaging Based Heart Disease Detection Using Deep Learning Technology

The heart anomalies detection is a significant task in cardiac medical research. The CT, ULTRASOUND, CTA and MRI scans have been used to detect heart diseases but giving false experimental outcomes in longer time of conversion (ToC). Therefore, patients haven’t getting better treatment from doctors. So that in this research work an ultrasound image scan-based heart disease prediction and classification is performed with deep learning technology. The LeNet 10 deep learning classifier has been trained Kaggle dataset using appropriate CNN layers. Proposed CNN LeNet -10 is a 165 layers technology consists of flattened layer, dense layer, convolution layer, max pooling layer and etc. Classification and feature extraction has been performed to loading with LeNet-10 architecture. The real time heart ultrasound test images are collecting from Manipal super specialty hospital Vijayawada, these test features are managed to test.CSV file. In pre-processing step, Ostu segmentation and histogram equalization is applied to make heart ultrasound images to be clear. In Segmentation, edge and region-based convolutional steps are applied such that deep features have been identified. LeNet-10 classification is used to find affected area as well as abnormality location. Finally proposed deep learning with confusion matrix can generating application measures. Implementation has been performed on python 3.9 and DL (Deep learning) packages like TensorFlow, keras, sklearn and etc. The measures like Accuracy 98.37%, sensitivity 97.81%, Recall 98.34% and F1 score 98.98% had been attained, proposed heart disease estimation application is more robust and compete with present technology

Enrollment in YFV Vaccine Trial: An Evaluation of Recruitment Outcomes Associated with a Randomized Controlled Double-Blind Trial of a Live Attenuated Yellow Fever Vaccine

This investigation evaluated several factors associated with diverse participant enrollment of a clinical trial assessing safety, immunogenicity, and comparative viremia associated with administration of 17-D live, attenuated yellow fever vaccine given alone or in combination with human immune globulin. We obtained baseline participant information (e.g., sociodemographic, medical) and followed recruitment outcomes from 2005 to 2007. Of 355 potential Yellow Fever vaccine study participants, 231 cases were analyzed. Strong interest in study participation was observed among racial and ethnically diverse persons with 36.34% eligible following initial study screening, resulting in 18.75% enrollment. The percentage of white participants increased from 63.66% (prescreened sample) to 81.25% (enrollment group). The regression model was significant with white race as a predictor of enrollment (OR=2.744, 95% CI=1.415-5.320, p=0.003).In addition, persons were more likely to enroll via direct outreach and referral mechanisms compared to mass advertising (OR=2.433, 95% CI=1.102-5.369). The findings indicate that racially diverse populations can be recruited to vaccine clinical trials, yet actual enrollment may not reflect that diversit

Systems and Network-Based Approaches for Personalized Medicine

Most biological outcomes in a cell arise from a complex interplay between different cellular entities such as proteins, DNA, RNA and metabolites. Therefore, a key challenge for biology in the twenty-first century is to understand the structure and dynamics of the complex web of interactions in a cell that contribute to its proper functioning. Recent years have seen a surge in the amount of “omics” data and an integration of several disciplines which has influenced all areas of life sciences, from molecular biology to medicine. With the emergence of a number of sophisticated tools and technologies as a result of genomics revolution, we are now in a position to view the molecular aspects of diseases at a systems level by incorporating various cellular entities into a network framework. Such systems/network-based approaches are not only enabling us to develop models of disease and wellness in a population but also contributing to our efforts to reverse engineer the molecular networks corresponding to disease states by perturbing using drug cocktails. These multi-scale personalized medicine approaches are likely to significantly re-shape the health care industry in the coming decades and decrease the division that we currently see between medicine and other biotechnology disciplines

A case of severe cardiomyopathy due to COVID-induced myocarditis, completely resolved after colchicine and immunoglobulin therapy

Coronavirus 19 (COVID-19) is well known for causing acute respiratory distress syndrome. Among other systemic complications, myocarditis is a frequently reported presentation as well as complication. One systematic review reported a 14% mortality rate in patients with COVID-19 myocarditis. Endomyocardial biopsy is a definitive diagnostic test but has been a challenge to perform in most cases of COVID myocarditis due to the contagious nature of the disease. Patients presenting with new cardiomyopathy with troponin leak and arrhythmias, supported by recent COVID-19 diagnosis should be suspected for COVID-induced myocarditis. Supportive treatment has been the mainstay of treatment with limited data on immunotherapy and colchicine. Our case is about a male in his 50s who had a cardiac arrest due to ventricular fibrillations, with a positive COVID-19 test. Further workup showed severe non-ischaemic cardiomyopathy with an EF of 15–20%. He was treated with intravenous immunotherapy and colchicine. A repeat echocardiogram 3 days later showed resolution of cardiomyopathy. Our case report highlights the possible beneficial effects of immunotherapy and colchicine in viral myocarditis

A case of acute extensive viral sinusitis secondary to acute Epstein Barr virus

Infectious mononucleosis (IM), the most common presentation of acute Epstein Barr virus (EBV) infection, typically presents with fever, pharyngitis and lymphadenopathy. We describe an unusual case of IM presenting as acute sinusitis. A 25 year-old male presented to the emergency department with worsening right frontal sinus pain along with fever, chills, and greenish nasal discharge for 3 weeks. Laboratory workup showed leukocytosis with high lymphocyte counts as well as transaminitis. Facial computerized tomography (CT) showed extensive right frontal, ethmoidal and maxillary sinusitis and antrochoanal polyp. The patient underwent endoscopy with drainage of purulent material and polyp removal. Unfortunately, cultures of the sample were not sent and bacterial infection could not be ruled out. Broad spectrum antibiotics were continued. Pathology of redundant tissue revealed large atypical lymphocytes with positive EBV-encoded RNA and lack of evidence of extranodal natural killer/T-cell (NK/T-cell) type lymphoma (ENKTCL). Tests for serum EBV IgM antibodies and EBV early Antigen antibodies were positive, indicating acute EBV infection. Lymphocytosis resolved along with significant clinical improvement at the 10-day follow up visit. Even though patient did receive antibiotics, multiple factors including isolated lymphocytosis, pathology positive for EBV with no neutrophilia were more suggestive of sinusitis caused by viral infection, EBV in this case. Lymphocytosis with fever and sore throat should prompt physicians to consider IM. There are no known reports in the literature of EBV as a causal organism for acute viral sinusitis. There are some studies relating EBV with ENKTCL. It is unknown whether this particular patient with a history of EBV sinusitis will be at high risk for nasal type lymphoma in the future. Further studies should be conducted to understand the pathogenesis and relationship between EBV and ENKTCL

Production of mycobacterial cell wall glycopeptidolipids requires a member of the MbtH-like protein family

Background Glycopeptidolipids (GPLs) are among the major free glycolipid components of the outer membrane of several saprophytic and clinically-relevant Mycobacterium species. The architecture of GPLs is based on a constant tripeptide-amino alcohol core of nonribosomal peptide synthetase origin that is N-acylated with a 3-hydroxy/methoxy acyl chain synthesized by a polyketide synthase and further decorated with variable glycosylation patterns built from methylated and acetylated sugars. GPLs have been implicated in many aspects of mycobacterial biology, thus highlighting the significance of gaining an understanding of their biosynthesis. Our bioinformatics analysis revealed that every GPL biosynthetic gene cluster known to date contains a gene (referred herein to as gplH) encoding a member of the MbtH-like protein family. Herein, we sought to conclusively establish whether gplH was required for GPL production. Results Deletion of gplH, a gene clustered with nonribosomal peptide synthetase-encoding genes in the GPL biosynthetic gene cluster of Mycobacterium smegmatis, produced a GPL deficient mutant. Transformation of this mutant with a plasmid expressing gplH restored GPL production. Complementation was also achieved by plasmid-based constitutive expression of mbtH, a paralog of gplH found in the biosynthetic gene cluster for production of the siderophore mycobactin of M. smegmatis. Further characterization of the gplH mutant indicated that it also displayed atypical colony morphology, lack of sliding motility, altered capacity for biofilm formation, and increased drug susceptibility. Conclusions Herein, we provide evidence formally establishing that gplH is essential for GPL production in M. smegmatis. Inactivation of gplH also leads to a pleiotropic phenotype likely to arise from alterations in the cell envelope due to the lack of GPLs. While genes encoding MbtH-like proteins have been shown to be needed for production of siderophores and antibiotics, our study presents the first case of one such gene proven to be required for production of a cell wall component. Furthermore, our results provide the first example of a mbtH-like gene with confirmed functional role in a member of the Mycobacterium genus. Altogether, our findings demonstrate a critical role of gplH in mycobacterial biology and advance our understanding of the genetic requirements for the biosynthesis of an important group of constituents of the mycobacterial outer membrane