Efficient Absolute Difference Circuit for SAD Computation On FPGA

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency AC coefficients of the block. Before we insert the secret message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure.

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Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200831104047 ◽

2020 ◽

Vol 13 ◽

Author(s):

Shoaib Amin Banday ◽

Mohammad Khalid Pandit

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Sagittal Plane ◽

Early Stage ◽

Imaging Techniques ◽

Complex Structure ◽

Magnetic Resonance Images ◽

Absolute Difference ◽

Brain Hemispheres ◽

The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

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Mask-R$$^{2}$$CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02430-0 ◽

2021 ◽

Author(s):

Sara Moccia ◽

Maria Chiara Fiorentino ◽

Emanuele Frontoni

Keyword(s):

Fetal Growth ◽

A Priori ◽

Absolute Difference ◽

Ultrasound Images ◽

Fetal Head ◽

Mean Absolute Difference ◽

Distance Field ◽

Feature Pyramid ◽

End To End ◽

Ablation Study

Abstract Background and objectives Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R$$^{2}$$ 2 CNN. It advances our previous work in the field and performs HC distance-field regression in an end-to-end fashion, without requiring a priori HC localization nor any postprocessing for outlier removal. Methods Mask-R$$^{2}$$ 2 CNN follows the Mask-RCNN architecture, with a backbone inspired by feature-pyramid networks, a region-proposal network and the ROI align. The Mask-RCNN segmentation head is here modified to regress the HC distance field. Results Mask-R$$^{2}$$ 2 CNN was tested on the HC18 Challenge dataset, which consists of 999 training and 335 testing images. With a comprehensive ablation study, we showed that Mask-R$$^{2}$$ 2 CNN achieved a mean absolute difference of 1.95 mm (standard deviation $$=\pm 1.92$$ = ± 1.92 mm), outperforming other approaches in the literature. Conclusions With this work, we proposed an end-to-end model for HC distance-field regression. With our experimental results, we showed that Mask-R$$^{2}$$ 2 CNN may be an effective support for clinicians for assessing fetal growth.

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Administrative Coding Methods Impact Surgical Site Infection Rates

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.616 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s111-s112

Author(s):

Mohammed Alsuhaibani ◽

Mohammed Alzunitan ◽

Kyle Jenn ◽

Daniel Diekema ◽

Michael Edmond ◽

...

Keyword(s):

Cesarean Section ◽

Surgical Procedures ◽

Hip Prosthesis ◽

Knee Prosthesis ◽

Surgical Site Infections ◽

Fold Increase ◽

Abdominal Hysterectomy ◽

Absolute Difference ◽

Coding Method ◽

The Impact

Background: Surveillance for surgical site infections (SSI) is recommended by the CDC. Currently, colon and abdominal hysterectomy SSI rates are publicly available and impact hospital reimbursement. However, the CDC NHSN allows surgical procedures to be abstracted based on International Classification of Diseases, Tenth Revision (ICD-10) or current procedural terminology (CPT) codes. We assessed the impact of using ICD and/or CPT codes on the number of cases abstracted and SSI rates. Methods: We retrieved administrative codes (ICD and/or CPT) for procedures performed at the University of Iowa Hospitals & Clinics over 1 year: October 2018–September 2019. We included 10 procedure types: colon, hysterectomy, cesarean section, breast, cardiac, craniotomy, spinal fusion, laminectomy, hip prosthesis, and knee prosthesis surgeries. We then calculated the number of procedures that would be abstracted if we used different permutations in administration codes: (1) ICD codes only, (2) CPT codes only, (3) both ICD and CPT codes, and (4) at least 1 code from either ICD or CPT. We then calculated the impact on SSI rates based on any of the 4 coding permutations. Results: In total, 9,583 surgical procedures and 180 SSIs were detected during the study period using the fourth method (ICD or CPT codes). Denominators varied according to procedure type and coding method used. The number of procedures abstracted for breast surgery had a >10-fold difference if reported based on ICD only versus ICD or CPT codes (104 vs 1,109). Hip prosthesis had the lowest variation (638 vs 767). For SSI rates, cesarean section showed almost a 3-fold increment (2.6% when using ICD only to 7.32% with both ICD & CPT), whereas abdominal hysterectomy showed nearly a 2-fold increase (1.14% when using CPT only to 2.22% with both ICD & CPT codes). However, SSI rates remained fairly similar for craniotomy (0.14% absolute difference), hip prosthesis (0.24% absolute difference), and colon (0.09% absolute difference) despite differences in the number of abstracted procedures and coding methods. Conclusions: Denominators and SSI rates vary depending on the coding method used. Variations in the number of procedures abstracted and their subsequent impact on SSI rates were not predictable. Variations in coding methods used by hospitals could impact interhospital comparisons and benchmarking, potentially leading to disparities in public reporting and hospital penalties.Funding: NoneDisclosures: None

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Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Scientific Reports ◽

10.1038/s41598-021-86436-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ananthakrishna Thalengala ◽

Shyamasunder N. Bhat ◽

H. Anitha

Keyword(s):

Spinal Deformity ◽

Medial Axis ◽

Image Understanding ◽

Spinal Curvature ◽

Absolute Difference ◽

Computer Assisted ◽

Type I ◽

Observer Variability ◽

Curvature Estimation ◽

Accuracy And Repeatability

AbstractAnalysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

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Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients

Diagnosis ◽

10.1515/dx-2021-0020 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Carl T. Berdahl ◽

An T. Nguyen ◽

Marcio A. Diniz ◽

Andrew J. Henreid ◽

Teryl K. Nuckols ◽

...

Keyword(s):

Emergency Department ◽

Body Temperature ◽

Acute Infection ◽

Time Of Day ◽

Pretest Probability ◽

Absolute Difference ◽

Proof Of Concept ◽

Easy Method ◽

Concept Study ◽

Probability Estimates

Abstract Objectives Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. Methods For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results Data from 117 patients were included. The models’ AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI −0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI −0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

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Social Jetlag and Prostate Cancer Incidence in Alberta’s Tomorrow Project: A Prospective Cohort Study

Cancers ◽

10.3390/cancers12123873 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3873

Author(s):

Liang Hu ◽

Andrew Harper ◽

Emily Heer ◽

Jessica McNeil ◽

Chao Cao ◽

...

Keyword(s):

Prostate Cancer ◽

Cancer Risk ◽

Cancer Incidence ◽

Repeated Measures ◽

Prospective Cohort ◽

Prostate Cancer Risk ◽

Absolute Difference ◽

Prostate Cancer Incidence ◽

Social Jetlag ◽

Increased Risk

We investigated the association of social jetlag (misalignment between the internal clock and socially required timing of activities) and prostate cancer incidence in a prospective cohort in Alberta, Canada. Data were collected from 7455 cancer-free men aged 35–69 years enrolled in Alberta’s Tomorrow Project (ATP) from 2001–2007. In the 2008 survey, participants reported usual bed- and wake-times on weekdays and weekend days. Social jetlag was defined as the absolute difference in waking time between weekday and weekend days, and was categorized into three groups: 0–<1 h (from 0 to anything smaller than 1), 1–<2 h (from 1 to anything smaller than 2), and 2+ h. ATP facilitated data linkage with the Alberta Cancer Registry in June 2018 to determine incident prostate cancer cases (n = 250). Hazard ratios (HR) were estimated using Cox proportional hazards regressions, adjusting for a range of covariates. Median follow-up was 9.57 years, yielding 68,499 person-years. Baseline presence of social jetlag of 1–<2 h (HR = 1.52, 95% CI: 1.10 to 2.01), and 2+ hours (HR = 1.69, 95% CI: 1.15 to 2.46) were associated with increased prostate cancer risk vs. those reporting no social jetlag (p for trend = 0.004). These associations remained after adjusting for sleep duration (p for trend = 0.006). With respect to chronotype, the association between social jetlag and prostate cancer risk remained significant in men with early chronotypes (p for trend = 0.003) but attenuated to null in men with intermediate (p for trend = 0.150) or late chronotype (p for trend = 0.381). Our findings suggest that greater than one hour of habitual social jetlag is associated with an increased risk of prostate cancer. Longitudinal studies with repeated measures of social jetlag and large samples with sufficient advanced prostate cancer cases are needed to confirm these findings.

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Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽

10.3390/electronics10101161 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1161

Author(s):

Kuo-Hao Fanchiang ◽

Yen-Chih Huang ◽

Cheng-Chien Kuo

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Monitoring System ◽

Absolute Difference ◽

Generative Adversarial Networks ◽

Economic Losses ◽

Maintenance Cost ◽

Full Time ◽

Noise Interference ◽

In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

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A Blind Spot in the Use of the Weibull Function for Modeling Diameter Distributions

Forest Science ◽

10.1093/forsci/fxaa042 ◽

2020 ◽

Author(s):

Adrian Norman Goodwin

Keyword(s):

Lower Bound ◽

Blind Spot ◽

Forest Growth ◽

Growth And Yield ◽

Diameter Distribution ◽

Absolute Difference ◽

Weibull Function ◽

Distribution Models ◽

Difference Statistic ◽

Diameter Distributions

Abstract Diameter distribution models based on probability density functions are integral to many forest growth and yield systems, where they are used to estimate product volumes within diameter classes. The three-parameter Weibull function with a constrained nonnegative lower bound is commonly used because of its flexibility and ease of fitting. This study compared Weibull and reverse Weibull functions with and without a lower bound constraint and left-hand truncation, across three large unthinned plantation cohorts in which 81% of plots had negatively skewed diameter distributions. Near-optimal lower bounds for the unconstrained Weibull function were negative for negatively skewed data, and the left-truncated Weibull using these bounds was 14.2% more accurate than the constrained Weibull, based on the Kolmogorov-Smirnov statistic. The truncated reverse Weibull fit dominant tree distributions 23.7% more accurately than the constrained Weibull, based on a mean absolute difference statistic. This work indicates that a blind spot may have developed in plantation growth modeling systems deploying constrained Weibull functions, and that left-truncation of unconstrained functions could substantially improve model accuracy for negatively skewed distributions.

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Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis

Sensors ◽

10.3390/s21082858 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2858

Author(s):

Kelly Ka-Lee Lai ◽

Timothy Tin-Yan Lee ◽

Michael Ka-Shing Lee ◽

Joseph Chi-Ho Hui ◽

Yong-Ping Zheng

Keyword(s):

Ultrasound Imaging ◽

Imaging System ◽

Outcome Measurement ◽

Three Dimensional ◽

3D Ultrasound ◽

Mean Difference ◽

Assessment System ◽

Absolute Difference ◽

Data Sets ◽

Low Profile

To diagnose scoliosis, the standing radiograph with Cobb’s method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland–Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.

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