Search results for: cardiac images

Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

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Authors: Devika Tanna

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'Biometrics' means 'life measurement' but the term is usually associated with the use of unique physiological characteristics to identify an individual. It is important to secure the privacy of digital face image that is stored in central database. To impart privacy to such biometric face images, first, the digital face image is split into two host face images such that, each of it gives no idea of existence of the original face image and, then each cover image is stored in two different databases geographically apart. When both the cover images are simultaneously available then only we can access that original image. This can be achieved by using the XM2VTS and IMM face database, an adaptive algorithm for spatial greyscale. The algorithm helps to select the appropriate host images which are most likely to be compatible with the secret image stored in the central database based on its geometry and appearance. The encryption is done using GEVCS which results in a reconstructed image identical to the original private image.

Keywords: adaptive algorithm, database, host images, privacy, visual cryptography

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Authors: Abdellatif Baba

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In order to facilitate medical images analysis by improving their quality and readability, we present in this paper a new dynamic band pass filter as a general and suitable operator for different types of medical images. Our objective is to enrich the details of any treated medical image to make it sufficiently clear enough to give an understood and simplified meaning even for unspecialized people in the medical domain.

Keywords: medical image enhancement, dynamic band pass filter, analysis improvement

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Authors: Chantana Insra

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The research “Buddha Images in Mudras Representing Days of a Week: Tactile Texture Design for the Blind” aims to provide original tactile format to institutions for the blind, as supplementary textbooks, to accumulate Buddhist knowledge, so that it could be extracurricular learning. The research studied on 33 students with both total and partial blindness, the latter with the ability to read Braille’s signs, of elementary 4 – 6, who are pursuing their studies on the second semester of the academic year 2013 at Bangkok School for the Blind. The researcher opted samples specifically, studied data acquired from both documents and fieldworks. Those methods must be related to the blind, tactile format production, and Buddha images in mudras representing days of a week. Afterwards, the formats will be analyzed and designed so that there would be 8 format pictures of Buddha images in mudras representing days of the week. Experts will next evaluate the media and try out.

Keywords: blind, tactile texture, Thai Buddha images, Mudras, texture design

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Authors: Tristan R. Fraser, Christopher D. Steadman, Christopher J. Boos

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Myopericarditis is an inflammation syndrome characterised by clinical diagnostic criteria for pericarditis, such as chest pain, combined with evidence of myocardial involvement, such as elevation of biomarkers of myocardial damage, e.g., troponins. It can rarely be a complication of therapeutics used for dysregulated immune-mediated diseases such as inflammatory bowel disease (IBD), for example, mesalazine. The infrequency of mesalazine-induced myopericarditis adds to the challenge in its recognition. Rapid diagnosis and the early introduction of treatment are crucial. This case report follows a 24-year-old professional footballer with a past medical history of ulcerative colitis, recently started on mesalazine for disease control. Three weeks after mesalazine was initiated, he was admitted with fever, shortness of breath, and chest pain worse whilst supine and on deep inspiration, as well as elevated venous blood cardiac troponin T level (cTnT, 288ng/L; normal: <13ng/L). Myocarditis was confirmed on initial inpatient cardiac MRI, revealing the presence of florid myocarditis with preserved left ventricular systolic function and an ejection fraction of 67%. This was a longitudinal case study following the progress of a single individual with myopericarditis over four acute hospital admissions over nine weeks, with admissions ranging from two to five days. Parameters examined included clinical signs and symptoms, serum troponin, transthoracic echocardiogram, and cardiac MRI. Serial measurements of cardiac function, including cardiac MRI and transthoracic echocardiogram, showed progressive deterioration of cardiac function whilst mesalazine was continued. Prior to cessation of mesalazine, transthoracic echocardiography revealed a small global pericardial effusion of < 1cm and worsening left ventricular systolic function with an ejection fraction of 45%. After recognition of mesalazine as a potential cause and consequent cessation of the drug, symptoms resolved, with cardiac MRI performed as an outpatient showing resolution of myocardial oedema. The patient plans to make a return to competitive sport. Patients suffering from myopericarditis are advised to refrain from competitive sport for at least six months in order to reduce the risk of cardiac remodelling and sudden cardiac death. Additional considerations must be taken in individuals for whom competitive sport is an essential component of their livelihood, such as professional athletes. Myopericarditis is an uncommon, however potentially serious medical condition with a wide variety of aetiologies, including viral, autoimmune, and drug-related causes. Management is mainly supportive and relies on prompt recognition and removal of the aetiological process. Mesalazine-induced myopericarditis is a rare condition; as such increasing awareness of mesalazine as a precipitant of myopericarditis is vital for optimising the management of these patients.

Keywords: myopericarditis, mesalazine, inflammatory bowel disease, professional athlete

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Authors: Mohammadjavad Sotoudeheian, Navid Farahmandian

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Cardiac hypertrophy arises in response to persistent increases in hemodynamic loads. In comparison, diabetic cardiomyopathy is defined by an abnormal myocardial changes without other cardiac-related risk factors. Pathological cardiac hypertrophy and myocardial remodeling are hallmarks of cardiovascular diseases and are risk factors for heart failure. The transcription factor forkhead box class O (FOXOs) can protect heart tissue by hostile oxidative stress and stimulating apoptosis and autophagy. FOXO proteins, as sensitive elements and mediators in response to environmental changes, have been revealed to prevent and inverse cardiac hypertrophy. FOXOs are inhibited by insulin and are critical mediators of insulin action. Insulin deficiency and uncontrolled diabetes lead to a catabolic state. FOXO1 acts downstream of the insulin-dependent pathways, which are dysregulated in diabetes. It regulates cardiomyocyte hypertrophy downstream of IGF1R/PI3K/Akt activation, which are critical regulators of cardiac hypertrophy. The complex network of signaling pathways comprising insulin/IGF-1 signaling, AMPK, JNK, and Sirtuins regulate the development of cardiovascular dysfunction by modulating the activity of FOXOs. Insulin receptors and IGF1R act via the PI3k/Akt and the MAPK/ERK pathways. Activation of Akt in response to insulin or IGF-1 induces phosphorylation of FOXOs. Increased protein synthesis induced by activation of the IGF-I/Akt/mTOR signaling pathway leads to hypertrophy. This pathway and the myostatin/Smad pathway are potent negative muscle development regulators. In cardiac muscle, insulin receptor substrates (IRS)-1 or IRS-2 activates the Akt signaling pathway and inactivate FOXO1. Under metabolic stress, p38 MAPK promotes degradation of IRS-1 and IRS-2 in cardiac myocytes and activates FOXO1, leading to cardiomyopathy. Sirt1 and FOXO1 interaction play an essential role in starvation-induced autophagy in cardiac metabolism. Inhibition of Angiotensin-II induced cardiomyocyte hypertrophy is associated with reduced FOXO1 acetylation and activation of Sirt1. The NF-κB, ERK, and FOXOs are de-acetylated by SIRT1. De-acetylation of FOXO1 induces the expression of genes involved in autophagy and stimulates autophagy flux. Therefore, under metabolic stress, FOXO1 can cause diabetic cardiomyopathy. The overexpression of FOXO1 leads to decreased cardiomyocyte size and suppresses cardiac hypertrophy through inhibition of the calcineurin–NFAT pathway. Diabetes mellitus is associated with elevation of O-GlcNAcylation. Some of its binding partners regulate the substrate selectivity of O-GlcNAc transferase (OGT). O-GlcNAcylation of essential contractile proteins may inhibit protein-protein interactions, reduce calcium sensitivity, and modulate contractile function. Uridine diphosphate (UDP)-GlcNAc is the obligatory substrate of OGT, which catalyzes a reversible post-translational protein modification. The increase of O-GlcNAcylation is accompanied by impaired cardiac hypertrophy in diabetic hearts. Inhibition of O-GlcNAcylation blocks activation of ERK1/2 and hypertrophic growth. O-GlcNAc modification on NFAT is required for its translocation from the cytosol to the nucleus, where NFAT stimulates the transcription of various hypertrophic genes. Inhibition of O-GlcNAcylation dampens NFAT-induced cardiac hypertrophic growth. Transcriptional activity of FOXO1 is enriched by improved O-GlcNAcylation upon high glucose stimulation or OGT overexpression. In diabetic conditions, the modification of FOXO1 by O-GlcNAc is promoted in cardiac troponin I and myosin light chain 2. Therefore targeting O-GlcNAcylation represents a potential therapeutic option to prevent hypertrophy in the diabetic heart.

Keywords: diabetes, cardiac hypertrophy, O-GlcNAcylation, FOXO1, Akt, PI3K, AMPK, insulin

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Authors: Abdul Fatah, Naveenchandra Acharya, Vamshi Krishna, T. Shivaprasad, Ramesh Ramayya

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Background: Significant number of patients with bladder outlet obstruction due to BPH are on anti-platelets and anticoagulants. Prostate surgery in this group of patients either in the form of TURP or Open prostatectomy is associated with increased risk of bleeding complications requiring transfusions, packing of the prostatic fossa or ligation or embolization of internal iliac arteries. Withholding of antiplatelets and anticoagulants may be associated with cardiac and other complications. Efficacy of Thulium Laser in the above group of patients was evaluated in terms of peri-operative, postoperative and delayed bleeding complications as well as cardiac events in peri-operative and immediate postoperative period. Methods: 217 patients with a mean age of 68.8 years were enrolled between March 2009 and March 2013 (36 months), and treated for BPH with ThuLEP. Every patient was evaluated at base line according to: Digital Rectal Examination (DRE), prostate volume, Post-Voided volume (PVR), International Prostate Symptoms Score (I-PSS), PSA values, urine analysis and urine culture, uroflowmetry. The post operative complications in the form of drop in hemoglobin level, transfusion rates, post –operative cardiac events within a period of 30 days, delayed hematuria and events like deep vein thrombosis and pulmonary embolism were noted. Results: Our data showed a better post-operative outcome in terms of, postoperative bleeding requiring intervention 7 (3.2%), transfusion rate 4 (1.8%) and cardiac events within a period of 30 days 4(1.8%), delayed hematuria within 6 months 2(0.9 %) compared other series of prostatectomies. Conclusion: The thulium LASER prostatectomy is a safe and effective option for patients with cardiac comorbidties and those patients who are on antiplatelet agents and anticoagulants. The complication rate is less as compared to larger series reported with open and transurethral prostatectomies.

Keywords: thulium laser, prostatectomy, antiplatelet agents, bleeding

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Authors: Jaqueline Maria Ribeiro Vieira

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Different strategies and tools are available at the oil and gas industry for detecting and analyzing tension and possible fractures in borehole walls. Most of these techniques are based on manual observation of the captured borehole images. While this strategy may be possible and convenient with small images and few data, it may become difficult and suitable to errors when big databases of images must be treated. While the patterns may differ among the image area, depending on many characteristics (drilling strategy, rock components, rock strength, etc.). Previously we developed and proposed a novel strategy capable of detecting patterns at borehole images that may point to regions that have tension and breakout characteristics, based on segmented images. In this work we propose the inclusion of data-mining classification strategies in order to create a knowledge database of the segmented curves. These classifiers allow that, after some time using and manually pointing parts of borehole images that correspond to tension regions and breakout areas, the system will indicate and suggest automatically new candidate regions, with higher accuracy. We suggest the use of different classifiers methods, in order to achieve different knowledge data set configurations.

Keywords: image segmentation, oil well visualization, classifiers, data-mining, visual computer

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Authors: Nancy Elizabeth Naranjo Garcia

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Canoa (1976) by Felipe Cazals is a film that exposes the consequences of power that the Mexican State exercised over the 1968 student movement. The film, in this particular way, approaches the Tlatelolco Massacre from a point of view that takes into consideration the events that led up to it. Nonetheless, the reference to the political tension in Canoa remains ambiguous. Thus, the cinematographic representation refers to an event that leaves space for reflection, and as a consequence leaves evidence of an image that signals the notion of survival as Georges Didi-Huberman points out. In addition to denouncing the oppressive force by the Mexican State, the images in Canoa also emphasize what did not happen in Tlatelolco and its condensation with the student activists. To observe the images that Canoa offers in a new light, this work proposes further exploration with the following questions; How do the images in Canoa narrate? How are the images inserted in the film? In this fashion, a more profound comprehension of the objective and the essence of the images becomes feasible. As a result, it is possible to analyze the images of Canoa with the real killing at San Miguel Canoa in literature. The film visualizes a testimony of the event that once seemed unimaginable, an image that anticipates and structures the proceeding event. Therefore, this study takes a second look at how Canoa considers not only the killing at San Miguel Canoa and the Tlatlelolco Massacre, but goes further on contextualize an unimaginable image.

Keywords: cinematographic representation, student movement, Tlatelolco Massacre, unimaginable image

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Authors: Siti Salbiah Samsudin, Hamzah Arof, Ainuddin Wahid Abdul Wahab, Mohd Yamani Idna Idris

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Standard X-ray spine images produced by conventional screen-film technique have a limited field of view. This limitation may obstruct a complete inspection of the spine unless images of different parts of the spine are placed next to each other contiguously to form a complete structure. Another solution to producing a whole spine image is by assembling the digitized x-ray images of its parts automatically using image stitching. This paper presents a new Medical Image Stitching (MIS) method that utilizes Minimum Average Correlation Energy (MACE) filters to identify and merge pairs of x-ray medical images. The effectiveness of the proposed method is demonstrated in two sets of experiments involving two databases which contain a total of 40 pairs of overlapping and non-overlapping spine images. The experimental results are compared to those produced by the Normalized Cross Correlation (NCC) and Phase Only Correlation (POC) methods for comparison. It is found that the proposed method outperforms those of the NCC and POC methods in identifying both the overlapping and non-overlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about two to five times shorter than those of the POC and NCC methods.

Keywords: image stitching, MACE filter, panorama image, scoliosis

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Stefania Arguelles Reyes, Octavio José Salcedo Parra, Alberto Acosta López

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This paper presents a web application for the improvement of images through recognition. The web application is based on the analysis of picture-based recognition methods that allow an improvement on the physical appearance of people posting in social networks. The basis relies on the study of tools that can correct or improve some features of the face, with the help of a wide collection of user images taken as reference to build a facial profile. Automatic facial profiling can be achieved with a deeper study of the Object Detection Library. It was possible to improve the initial images with the help of MATLAB and its filtering functions. The user can have a direct interaction with the program and manually adjust his preferences.

Keywords: Matlab, make up, recognition methods, web application

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Authors: Manali Jain, Neeta Singh, Raunaq Fatima, Soniya Nityanand, Mandakini Pradhan, Chandra Prakash Chaturvedi

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Isolated Congenital Heart Defect (ICHD) is the major cause of neonatal death worldwide among all forms of CHDs. A significant proportion of fetuses with ICHD die in the neonatal period if no treatment is provided. Recently, stem cell therapies have emerged as a potential approach to ameliorate ICHD in children. ICHD is characterized by cardiac structural abnormalities during embryogenesis due to alterations in the cardiomyogenic properties of a pool of cardiac progenitors/ stem cells associated with fetal heart development. The stem cells present in the amniotic fluid (AF) are of fetal origin and may reflect the physiological and pathological changes in the fetus during embryogenesis. Therefore, in the present study, the cardiomyogenic potential of AF-MSCs derived from fetuses with ICHD (ICHD AF-MSCs) has been evaluated and compared with that of AF-MSCs of structurally normal fetuses (normal AF-MSCs). Normal and ICHD AF-MSC were analyzed for the expression of cardiac progenitor markers viz., stage-specific embryonic antigen-1 (SSEA-1), vascular endothelial growth factor 2 (VEGFR-2) and platelet-derived growth factor receptor-alpha (PDGFR-α) by flow cytometry. The immunophenotypic characterization revealed that ICHD AF-MSCs have significantly lower expression of cardiac progenitor markers VEGFR-2 (0.14% ± 0.6 vs.48.80% ± 0.9; p <0.01), SSEA-1 (70.86% ± 2.4 vs. 88.36% ±2.7; p <0.01), and PDGFR-α (3.92% ± 1.8 vs. 47.59% ± 3.09; p <0.01) in comparison to normal AF-MSCs. Upon induction with 5’-azacytidine for 21 days, ICHD AF-MSCs showed a significantly down-regulated expression of cardiac transcription factors such as GATA-4 (0.4 ± 0.1 vs. 6.8 ± 1.2; p<0.01), ISL-1 (2.3± 0.6 vs. 14.3 ± 1.12; p<0.01), NK-x 2-5 (1.1 ± 0.3 vs. 14.1 ±2.8; p<0.01), TBX-5 (0.4 ± 0.07 vs. 4.4 ± 0.3; p<0.001), and TBX-18 (1.3 ± 0.2 vs. 4.19 ± 0.3; p<0.01) when compared with the normal AF-MSCs. Furthermore, immunocytochemical staining revealed that both types of AF-MSCs could differentiate into cardiovascular lineages and express cardiomyogenic, endothelial, and smooth muscle actin markers, viz., cardiac troponin (cTNT), CD31, and alpha-smooth muscle actin (α-SMA). However, normal AF-MSCs showed an enhanced expression of cTNT (p<0.001), CD31 (p<0.01), and α-SMA (p<0.05), compared to ICHD AF-MSCs. Overall, these results suggest that the ICHD-AF-MSCs have a defective cardiomyogenic differentiation potential and that the defects in these stem cells may have a role in the pathogenesis of ICHD.

Keywords: amniotic fluid, cardiomyogenic potential, isolated congenital heart defect, mesenchymal stem cells

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Authors: Mamadou I. Wade, Henry C. Ogworonjo, Madiha Gul, Mandoye Ndoye, Mohamed Chouikha, Wayne Patterson

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In this paper, we present a novel application of the Paillier cryptographic system to the encryption of RGB (Red Green Blue) images. In this method, an RGB image is first separated into its constituent channel images, and the Paillier encryption function is applied to each of the channels pixel intensity values. Next, the encrypted image is combined and compressed if necessary before being transmitted through an unsecured communication channel. The transmitted image is subsequently recovered by a decryption process. We performed a series of security and performance analyses to the recovered images in order to verify their robustness to security attack. The results show that the proposed image encryption scheme produces highly secured encrypted images.

Keywords: image encryption, Paillier cryptographic system, RBG image encryption, Paillier

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Authors: Margarita Ivanova, Julia Dao, Andrew Friedman, Neil Kasaci, Rekha Gopal, Ozlem Goker-Alpan

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In Fabry disease (FD), α-galactosidase A (α-Gal A) deficiency leads to the accumulation of globotriaosylceramide (Lyso-Gb3 and Gb3), triggering a pathologic cascade that causes the severity of organs damage. The heart is one of the several organs with high sensitivity to the α-Gal A deficiency. A subgroup of patients with significant residual of α-Gal A activity with primary cardiac involvement is occasionally referred to as “cardiac variant.” The cardiovascular complications are most frequently encountered, contributing substantially to morbidity, and are the leading cause of premature death in male and female patients with FD. The deposition of Lyso-Gb-3 and Gb-3 within the myocardium affects cardiac function with resultant progressive cardiovascular pathology. Gb-3 and Lyso-Gb-3 accumulation at the cellular level trigger a cascade of events leading to end-stage fibrosis. In the cardiac tissue, Lyso-Gb-3 deposition is associated with the increased release of inflammatory factors and transforming growth factors. Infiltration of lymphocytes and macrophages into endomyocardial tissue indicates that inflammation plays a significant role in cardiac damage. Moreover, accumulated data suggest that chronic inflammation leads to multisystemic FD pathology even under enzyme replacement therapy (ERT). NF-κB activation plays a subsequent role in the inflammatory response to cardiac dysfunction and advanced heart failure in the general population. TNFalpha/NF-κB signaling protects the myocardial evoking by ischemic preconditioning; however, this protective effect depends on the concentration of TNF-α. Thus, we hypothesize that TNF-α is a critical factor in determining the grade of cardio-pathology. Cardiac hypertrophy corresponds to the expansion of the coronary vasculature to maintain a sufficient supply of nutrients and oxygen. Coronary activation of angiogenesis and fibrosis plays a vital role in cardiac vascularization, hypertrophy, and tissue remodeling. We suggest that the interaction between the inflammatory pathways and cardiac vascularization is a bi-directional process controlled by secreted cytokines and growth factors. The co-coordination of these two processes has never been explored in FD. In a cohort of 40 patients with FD, biomarkers associated with inflammation and cardio hypertrophic remodeling were studied. FD patients were categorized into three groups based on LVmass/DSA, LVEF, and ECG abnormalities: FD with no cardio complication, FD with moderate cardio complication, and severe cardio complication. Serum levels of NF-kB, TNFalpha, Il-6, Il-2, MCP1, ING-gamma, VEGF, IGF-1, TGFβ, and FGF2 were quantified by enzyme-linked immunosorbent assays (ELISA). Among the biomarkers, MCP-1, INF-gamma, VEGF, TNF-alpha, and TGF-beta were elevated in FD patients. Some of these biomarkers also have the potential to correlate with cardio pathology in FD. Conclusion: The study provides information about the role of inflammatory pathways and biomarkers of cardio hypertrophic remodeling in FD patients. This study will also reveal the mechanisms that link intracellular accumulation of Lyso-GB-3 and Gb3 to the development of cardiomyopathy with myocardial thickening and resultant fibrosis.

Keywords: biomarkers, Fabry disease, inflammation, growth factors

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

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Authors: E. Rama Devi Jothilingam

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Diabetes mellitus is now one of the most common non communicable diseases globally. India leads the world with largest number of diabetic subjects earning the title "diabetes capital of the world". In order to reduce the mortality rate, a fuzzy expert system is designed to predict the severity of cardiac and renal problems of diabetic patients using fuzzy logic. Since uncertainty is inherent in medicine, fuzzy logic is used in this research work to remove the inherent fuzziness of linguistic concepts and uncertain status in diabetes mellitus which is the prime cause for the cardiac arrest and renal failure. In this work, the controllable risk factors "blood sugar, insulin, ketones, lipids, obesity, blood pressure and protein/creatinine ratio" are considered as input parameters and the "the stages of cardiac" (SOC)" and the stages of renal" (SORD) are considered as the output parameters. The triangular membership functions are used to model the input and output parameters. The rule base is constructed for the proposed expert system based on the knowledge from the medical experts. Mamdani inference engine is used to infer the information based on the rule base to take major decision in diagnosis. Mean of maximum is used to get a non fuzzy control action that best represent possibility distribution of an inferred fuzzy control action. The proposed system also classifies the patients with high risk and low risk using fuzzy c means clustering techniques so that the patients with high risk are treated immediately. The system is validated with Matlab and is used as a tracking system with accuracy and robustness.

Keywords: Diabetes mellitus, fuzzy expert system, Mamdani, MATLAB

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Authors: Ortega Ivan, De La Plaza Edurne

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Background: Spain and other European countries have implemented Uncontrolled Donation after Cardiac Death (uDCD) programs. After 15 years of experience in Spain, many things have changed. Recent evidence and technical breakthroughs achieved in resuscitation are relevant for uDCD programs and raise some ethical concerns related to these protocols. Aim: To rethink current uDCD programs in the light of recent evidence on available therapeutic procedures applicable to victims of out-of-hospital cardiac arrest (OHCA). To address the following question: What is the current standard of treatment owed to victims of OHCA before including them in an uDCD protocol? Materials and Methods: Review of the scientific and ethical literature related to both uDCD programs and innovative resuscitation techniques. Results: 1) The standard of treatment received and the chances of survival of victims of OHCA depend on whether they are classified as Non-Heart Beating Patients (NHBP) or Non-Heart-Beating-Donors (NHBD). 2) Recent studies suggest that NHBPs are likely to survive, with good quality of life, if one or more of the following interventions are performed while ongoing CPR -guided by suspected or known cause of OHCA- is maintained: a) direct access to a Cath Lab-H24 or/and to extra-corporeal life support (ECLS); b) transfer in induced hypothermia from the Emergency Medical Service (EMS) to the ICU; c) thrombolysis treatment; d) mobile extra-corporeal membrane oxygenation (mini ECMO) instituted as a bridge to ICU ECLS devices. 3) Victims of OHCA who cannot benefit from any of these therapies should be considered as NHBDs. Conclusion: Current uDCD protocols do not take into account recent improvements in resuscitation and need to be adapted. Operational criteria to distinguish NHBDs from NHBP should seek a balance between the technical imperative (to do whatever is possible), considerations about expected survival with quality of life, and distributive justice (costs/benefits). Uncontrolled DCD protocols can be performed in a way that does not hamper the legitimate interests of patients, potential organ donors, their families, the organ recipients, and the health professionals involved in these processes. Families of NHBDs’ should receive information which conforms to the ethical principles of respect of autonomy and transparency.

Keywords: uncontrolled donation after cardiac death resuscitation, refractory cardiac arrest, out of hospital cardiac, arrest ethics

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Authors: Thouraya Majoul

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Obesity is now a real public health issue throughout the world, and it is well-established that obesity leads to cardiovascular diseases. The prevention and treatment of obesity using nutritional supplements has become a realistic and effective approach. This study was carried out to analyze the incidence of a high-fat diet on rat heart metabolism as well as on fatty acids composition, then to investigate the eventual protective effects of a grape seed extract (GSE). The experimental design consisted of three rat groups subjected to three different conditions; standard (SD), high-fat diet (HFD) and HFD+GSE (HG). We showed that GSE counteracted the effect of HFD on fatty acid composition, namely, docosapentaenoic acid, docosahexaenoic acid, arachidonic acid (ARA), palmitic acid (PA) and palmitoleic acid. Besides, GSE treatment restored HFD-altered metabolic pathways through the recovery of some cardiac enzyme activities such as lipase, glucose 6 phosphate dehydrogenase and pyruvate dehydrogenase. The cardiac lactate level and lactate dehydrogenase activity were also analyzed in relation to HFD and GSE administration. To our knowledge, this is the first study showing the anti-obesity and cardioprotective effects of GSE in relation to fatty acid composition and some cardiac enzymes, supporting its role as a therapeutic agent of obesity.

Keywords: Grape seed extract, phenolic, obesity, cardioprotective, lipotoxicity, energy metabolism

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

Abstract:

Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: Constanza A. Barriga, Rafael Bernardi, Amokrane Berdja, Christian D. Guzman

Abstract:

Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariable in the image plane. However, this latter condition is not always satisfied with real optical systems. PSF angular variations cannot be evaluated directly from the observations, neither be corrected at a pixel resolution. We have developed a method for the restoration of images affected by static and anisotropic aberrations using deep neural networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T-80 telescope optical system, an 80 cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image, which has a constant and known PSF across its field-of-view. The method was tested with the T-80 telescope, achieving better results than with PSF deconvolution techniques. We present the method and results on this telescope.

Keywords: aberrations, deep neural networks, image restoration, variable point spread function, wide field images

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Authors: Teja Sai Puligadda

Abstract:

Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Risti Suryantari, Flaviana

Abstract:

The aim of this research is to design calibration method of Thermochromic Liquid Crystal for temperature distribution measurement based on mathematical morphology of hue image A glass of water is placed on the surface of sample TLC R25C5W at certain temperature. We use scanner for image acquisition. The true images in RGB format is converted to HSV (hue, saturation, value) by taking of hue without saturation and value. Then the hue images is processed based on mathematical morphology using Matlab2013a software to get better images. There are differences on the final images after processing at each temperature variation based on visualization observation and the statistic value. The value of maximum and mean increase with rising temperature. It could be parameter to identify the temperature of the human body surface like hand or foot surface.

Keywords: thermochromic liquid crystal, TLC, mathematical morphology, hue image

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Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

Abstract:

Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques.

Keywords: deep-learning, classification, pre-processing, computer vision, image processing, educational data mining

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Authors: Fernando Duarte

Abstract:

The work aims to evaluate the use of classical image processing methodologies towards skin tone classification in dermatological images. The skin tone is an important attribute when considering several factor for skin cancer diagnosis. Currently, there is a lack of clear methodologies to classify the skin tone based only on the dermatological image. In this work, a recent released dataset with the label for skin tone was used as reference for the evaluation of classical methodologies for segmentation and adjustment of color space for classification of skin tone in dermatological images. It was noticed that even though the classical methodologies can work fine for segmentation and color adjustment, classifying the skin tone without proper control of the aquisition of the sample images ended being very unreliable.

Keywords: segmentation, classification, color space, skin tone, Fitzpatrick

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Authors: Amir Moslemi, Amir movafeghi, Shahab Moradi

Abstract:

One of the most important challenging factors in medical images is nominated as noise.Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjected to low quality due to the noise. The quality of CT images is dependent on the absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on the purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete wavelet transform(DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result in good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).

Keywords: computed tomography (CT), noise reduction, curve-let, contour-let, signal to noise peak-peak ratio (PSNR), structure similarity (Ssim), absorbed dose to patient (ADP)

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Authors: Sutinon Yuchomsuk, Satchachon Changthom, Pruet Areesawangvong, Monsiri Jinapen

Abstract:

Background: Esophageal squamous cell carcinoma can be presented with many symptoms, such as dysphagia or weight loss. However, in some circumstances, rare presentations can be found, e.g., dyspnea, which is more common in pulmonary malignancy. And dyspnea is also one of the most common presentations of COVID-19 infection. So, in this case, we can learn from many points in patient symptoms and findings leading to the diagnosis of esophageal squamous cell carcinoma. Method: This research is a case-report study including one patient from Mahasarakham Hospital, Thailand. Data were collected during December 2021. Result: A 55-year-old Thai male patient with an unknown past medical history presented with dyspnea and shortness of breath for the duration of three days prior to admission. His symptom also included cough, fever, and sore throat. Laboratory results indicated that the patient had COVID-19 pneumonia. Further investigation showed that he had cardiac tamponade and suspected pulmonary/esophageal cancer. Lung biopsy and pericardiocentesis were done, which were positive for carcinoma from pericardial effusion but negative for malignancy from the lung biopsy. Later esophagogastroduodenoscopy was done with endoscopic tissue biopsy; the result was positive for squamous cell carcinoma of the esophagus. Conclusion: Most commonly, esophageal cancer is presented with dysphagia or weight loss. However, in some rare cases, patients can also be presented with dyspnea due to cardiac tamponade. And in recent years, COVID-19 has become a pandemic all over the world, sometimes masking symptoms of other diseases. Such as in this case, the patient didn’t improve after the pneumonia was resolved, which led to the final diagnosis of metastatic esophageal cancer.

Keywords: esophageal cancer, cardiac tamponade, metastatic squamous cell carcinoma, COVID-19 infection

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Authors: Belaynesh Chekol, Numan Çelebi

Abstract:

The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

Procedia PDF Downloads 279