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Aim: The contemplations and actions of medical undergraduates concerning their oral well-being mirror their perception of the significance of interdental cleaning aids in the maintenance of oral hygiene. The current study aimed to evaluate understanding and discernment of the practice of interdental aids as well as their role in the maintenance of oral hygiene among medical undergraduates in the district of Sheikhupura, Pakistan. Methods: A questionnaire-based cross-sectional analysis was performed from June 2021 to December 2021 at Amna Inayat Medical College in the district of Sheikhupura, Pakistan on a total of 220 medical undergraduates who were enrolled by convenient sampling method. Data were assembled using a standardized questionnaire that was circulated amongst students of 1st Year MBBS to Final Year MBBS. Associations between awareness regarding interdental cleaning and oral hygiene practice among both genders and all the years of study were assessed through the Pearson Chi-Square test. Results: The study comprised 123 females and 97 males with an average age range of 18 - 25 years. Most of the students i.e. 47.3% knew that the interproximal tooth surface is the most difficult to clean and cannot be effectively cleansed with a toothbrush only. Significant differences were seen in gender regarding the cleaning of teeth (P ≤ 0.001), the challenging surface of the tooth to clean (P ≤ 0.01), reasons for bad breath (P ≤ 0.003), knowledge of gum disease (P ≤ 0.000) and information regarding interdental cleaning (P ≤ 0.000). Practical Implication: Imparting knowledge regarding the maintenance of oral hygiene is crucial due to the connection between the oral cavity and the rest of the body. In a developing country like Pakistan, awareness, and understanding of oral hygiene is not up to the mark even in the medical population. Hence, improvement in oral hygiene can be brought about by creating public awareness regarding interdental aids in addition to brushing alone. Conclusions: Outcomes of the existing research propose that the overall understanding and awareness amongst medical undergraduates related to interdental aids was satisfactory. Established on these conclusions, additional communal-focused oral health promotions are requisite to piloting the degree of the dwelling of medical undergraduates. Keywords: Awareness, Dental, Dentist, Interdental area, Gingivitis, Knowledge, Medical Undergraduates, Oral Health, Periodontitis, Plaque.

Aim:Patients suffering from Diabetes Mellitus havea largerpossibilityofdeveloping oral diseases furthermore; those who have uncontrolled and long-duration of Diabetes Mellitusare also at a greater risk for acquiringperiodontaldisorders. The present study intended to investigate the association of oral fitness knowledge and attitude with different demographic factorsin patients with Type II Diabetes Mellitus visiting a secondary care provincial hospitalin Sargodha, Pakistan. Methods: The present study was a cross-sectional study piloted at a secondary care provincial hospital in District Sargodha which comprised 180 patients suffering from Type II Diabetes Mellitus.Information was collected using a systematized questionnaire which was handed out to study participants visiting the outpatient department of the hospital. Results:Oral health knowledge of the study participants were cross-tabulated with socio-economic and demographic factors and a significant association was seen with gender(P≤0.001), educational status(P≤0.0001), monthly income(P≤0.002), and marital status(P≤0.001). In the present study, 26% of males and 18% of females had adequate knowledge regarding oral health. Advanced levels of learningand income also transpiredto have a strong association with oral health awarenessand attitude(P≤0.01). Conclusions: Results of the present study suggest that oral well-being knowledge and attitude among patients with Type II Diabetes Mellitus were strongly associated in conjunction withgender, higher levels of education, and income. Based on these outcomes, communal-oriented oral well-being campaigns are indispensable to navigating the standard of living of patients with Type II Diabetes Mellitus. Keywords: Dental, Diabetes Mellitus, Knowledge, Oral Health, Oral complications, Type II Diabetes Mellitus.

Introduction: Convolutional neural networks (CNNs) have maintained their dominance in deep learning methods for human action recognition (HAR) and other computer vision tasks. However, the need for a large amount of training data always restricts the performance of CNNs. Method: This paper is inspired by the two-stream network, where a CNN is deployed to train the network by using the spatial and temporal aspects of an activity, thus exploiting the strengths of both networks to achieve better accuracy. Contributions: Our contribution is twofold: first, we deploy an enhanced spatial stream, and it is demonstrated that models pre-trained on a larger dataset, when used in the spatial stream, yield good performance instead of training the entire model from scratch. Second, a dataset augmentation technique is presented to minimize overfitting of CNNs, where we increase the dataset size by performing various transformations on the images such as rotation and flipping, etc. Results: UCF101 is a standard benchmark dataset for action videos, and our architecture has been trained and validated on it. Compared with the other two-stream networks, our results outperformed them in terms of accuracy.

The current literature shows the ambiguous relationship between corruption and shadow economy, specifically in the case of developing countries, because most of the earlier research system on judicial corruption was not incorporated while measuring different corruption indices. This study has used dynamic judicial indicators to observe corruption control as a latent variable to contribute to an advanced analysis of the nexus between shadow economy and corruption. It has employed an unbalanced panel data of 65 developing countries from 2000-2019 to investigate this relationship empirically. Confirmatory Factor Analysis and Multiple Indicator and Multiple Causes Models are used to estimate the magnitudes and signs of various dimensions and causes of corruption control, shadow economy, and their nexus. The results of this study suggest that robust judicial indicators and a more independent judicial system significantly reduce corruption and consequently shrink the shadow economy. Thus, it supports the maxim that the shadow economy and corruption, particularly those in developing countries, are complementary while empirically contradicting the notion that the shadow economy and corruption are substitutes. This study finds a substantial and positive connection between cash holdings and labor force participation rate with shadow economic activities and also predicted an inverse relationship between GDP per capita with the shadow economy.

Smartphone applications have gained popularity in recent years due to the large footprint of mobile phone usage and availability of a large number of value-added applications. The official app stores (google, IOS, Microsoft, Amazon) provide a platform for hosting, publishing, distributing, and managing the mobile applications developed by companies and individuals. This mobile application ecosystem could be used to distribute the malicious apps which are specifically designed to track behaviour of users, spy on the activities of users, and could be a threat to the privacy, confidentiality, and integrity of the users. In this paper, we present a novel approach called DEEPSEL (Deep Feature Selection), a deep learning-based method for the identification of malware and malicious codes within android applications. DEEPSEL uses a set of features to characterize the behaviour of android applications and classify them as legitimate and malicious. The main contribution is characterized by the usage of particle swarm optimization for performing feature selection. We evaluated our approach on a public malware data-set which is composed of samples collected from 39 unique malware families. Our results show that the proposed method can achieve very good results with an accuracy of around 83.6% and an F-measure of around 82.5%.

Aim: To investigate the comparison between teacher centered and Student centered medical education approach among multiple medical and dental institutes of Lahore. Methods: A thorough investigation was done by collecting data on results of students given Teacher centered medical education and Student centered medical education. Students of each class were divided into 5 groups and given tasks of presenting on given topics and individual students’ participation was encouraged in each group. Results: The results of teacher centered teaching showed that 20% of class scored above 80% marks. 78% of class scored between 60% and 80% marks and 2% scored less than 60% marks. On the other hand the student centered presentation had an excellent outcome where 100% of students scored more than 80% marks, out of which 60% scored more than 90%. Conclusion: It is concluded that Students’ performance in student centered medical education was higher than that in teacher centered education. This teaching technique was seen to be better retentive because of active participation of students. MeSH Words: Medical Education, Self-centered learning, Student centered learning, teacher centered learning.

Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning models – MobileNet-V2 and Darknet53 – the original video frames are normalized. For feature extraction, pre-trained deep models are used, which are fused using the canonical correlation approach. Following that, an improved particle swarm optimization (IPSO)-based algorithm is used to select the best features. Following that, the selected features were used to classify actions using various classifiers. The experimental process was performed on six publicly available datasets such as KTH, UT-Interaction, UCF Sports, Hollywood, IXMAS, and UCF YouTube, which attained an accuracy of 98.3%, 98.9%, 99.8%, 99.6%, 98.6%, and 100%, respectively. In comparison with existing techniques, it is observed that the proposed framework achieved improved accuracy.

BACKGROUND & OBJECTIVE: Technological advancements have transformed society into a global entity; medical education is no exception. Innumerable social media platforms such as Facebook, WhatsApp, Instagram, and Edmodo encourage collaborative learning, improved communication, and knowledge-sharing. The current study aims to assess students' perception of WhatsApp as an additional educational and learning aid for communication purposes, sharing of learning materials, and as a discussion forum. METHODOLOGY: This cross-sectional study was carried out from July 2021 to December 2021 at Faryal Dental College, Sheikhupura, Pakistan. A standardized questionnaire was prepared and circulated amongst students of 1st Year BDS to Final Year BDS. Responses were analyzed using SPSS Version 20. RESULTS: The study comprised 150 participants; the majority, i.e., 62% (n=93), were females, and 38% (n=57) were males. Learning through WhatsApp was found to be useful by 90% of students. An association (p≤0.02) was found among both genders regarding the usefulness of WhatsApp as an additional learning aid. CONCLUSION: These days, social media is a dominant mechanism used for communal exchanges and is also utilized as an additional aid for teaching and learning purposes. Incorporating social media with conventional class teaching in medical education has evident advantages.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.

Aim: Oral complications of diabetes mellitus have an influence on the general health and well-being of the people and demonstrate to be a burden for the dental health sector, predominantly in developing countries. The current study aimed to check the awareness and knowledge of oral health status concerning Type II diabetes mellitus patients at a THQ hospital in Sargodha, Pakistan, and perform an oral examination to evaluate oral health status. Methods: This study was conducted at a THQ hospital in District Sargodha in which 180 Type 2 diabetes mellitus patients were included. Data was documented employing a structured questionnaire which was administered to participants visiting the outpatient department. Results: The average age was documented to be 41.58 ± 8.35 years. It was discerned that 44% of the study participants had adequate oral health awareness; however, 56% had insufficient oral health understanding. The outcomes indicated that 62% had adverse oral health as revealed by their scores on the oral health attitude questionnaire while 38% were noticed to have good oral hygiene. It was also seen that the oral health attitude was strongly correlated with oral health knowledge (P 0.01). Conclusions: The findings of this study signify that Type 2 diabetes mellitus patients visiting district Sargodha have insufficient oral health awareness and reveal undesirable oral health attitudes. Based upon these results, community-oriented oral health promotion programs are essential to steer the lifestyle of diabetic patients. Keywords: Knowledge, dental, Diabetes mellitus, Type II diabetes, Oral health, Oral complications.

Objective: To assess the knowledge about different mandibular nerve block techniques of fresh dental graduates, dental surgeons and specialists to attain mandibular nerve block for dental treatment in Multan. Design of the Study: It was a cross-sectional survey. Study Settings: This study was carried out at Department of Dentistry at Multan Medical & Dental College, Nishtar Institute of dentistry (Nishtar Medical University) and Bakhtawer Amin Dental College from August 2019 to August 2020. Material and Methods: The study involved 220 respondents. The questionnaire was administered to fresh dental graduates (house officers) and dental surgeons in 3 dental colleges of Multan, and was collected on the same day. House officers who did not return the forms were reminded personally and contacted on the phone thrice over the following week. Results of the Study: The frequency of respondants who could administer Gow-Gates without supervison was 20.9% (n=46). Participants from NID, Multan had the highest frequency (35.3%) with the lowest in BIMDC (16.6%)and MMDC(20.6%). The differences were non-significant (p=0.19). Significantly higher frequency of male respondants (10%, n=22) claimed they could administer Gow-Gates without supervison compared to female respondants (5.4%, n=12, p=0.012, Chi-Square). Training for Gow-Gates had been received by 12.7% (n=28) of the House Officers and dental surgeons. The Highest frequency of training was in NID, Multan (5.4%, n= 12, p= 0.016, chi-square). Conclusion: The majority of the dental practitioners of Multan used IANB as their primary LA technique and intraligamental injections as a supplemental LA technique in their clinics to attain mandibular molars anesthesia for general dental procedures like fillings, root canals and extractions. Keywords: Mandibular local anesthesia, Inferior alveolar nerve block, Gow-Gates technique

The automatic classification of bone texture into healthy or osteoporotic cases presents a major challenge since there is no visual difference between the two cases. This classification requires an inspection of the fine granularity in the bone radiographs which is usually difficult with a naked eye. We have proposed a novel method in this paper, that can be used for the classification of bone radiographs into healthy or osteoporotic cases. We mimic the observations of the physicians by preprocessing the bone radiographs with Gabor filters bearing a high frequency. Later, we design and utilize a convolutional neural network wherein filtered images are fed as input to the system which classifies the images into their respective classes. The proposed algorithm has been validated on a bone radiograph challenge dataset. Our results depict that the method proposed in this research exhibits very good results in terms of classification. A comparison of the proposed and the contemporary research methods has also been shown in this paper. The experimental results show that by exploiting high frequency Gabor filters and employing the convolutional neural network architecture, good results in performing the classification of bone radiographs are achieved.

Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate in our daily life and have been widely adopted for business and personal communications. At the same time, scammers, criminals, and fraudsters have also find the telephony network an attractive and affordable medium to target end-users with the advertisement, marketing of legal and illegal products, and bombard them with the huge volume of unwanted calls. These calls would not only trick call recipients into disclosing their private information such as credit card numbers, PIN code which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing. The fraudsters, political campaigners can also use telephony systems to spread malicious information (hate political or religious messages) in real-time through audio or text messages, which have serious political and social consequences if malicious callers are not mitigated in a quick time. In this context, the identification of malicious callers would not only minimize telephony fraud but would also bring peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get feedback from the call recipients about their recent interactions with the caller, but these systems not only bring inconvenience to callees but also require changes in the system design. The call detail records extensively log the activities of users and can be used to categorize them as the spammer and non-spammer. In this paper, we utilize the information from the call detailed records and proposed a spam detection framework for the telephone network that identifies malicious callers by utilizing the social behavioral features of users within the network. To this extent, we first model the behavior of the users as the directed social graph and then analyze different features of the social graph i.e. the Relationship Network and Call patterns of users towards their peers. We then used these features along with the decision tree to classify callers into three classes i.e. human, spammer and call center. We analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection accuracy and extremely low false-positive rate. The performance results show that the spammers and call center numbers not only have a large number of non-repetitive calls but also have a large number of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of repetitive calls and most of them interacted for a relatively long duration.

Objective: of this study is to analyze the behavior/attitude of general dental practitioners towards record keeping and quality assessment of patient records found in different dental practices of Lahore. Study design: Cross sectional, Descriptive, Questionnaire based study (Copy of questionnaire attached). Place and Duration of Study: Data collection for this study was conducted in different private dental practices of Lahore from Oct-2017 to Dec-2017. Methods; A random sample of 60 dental practices were selected by means of stratified sampling from different towns of Lahore. Dentists were interviewed and patient records were checked for data collection which is analyzed using SPSS version 23. Results: Interview of 43 dentists and analysis of patient records from their practices revealed that 16 (37.2%) practices have no record at all and even none of the remaining 27 (62.8%) practices. Who claim to have patient records, has any properly completed record. Shows that dentists have got very casual behavior towards record keeping as most of them were not having any records and the remaining ones who claimed to have patient records, were maintaining them in a very poor form. Conclusion: Female dentists, postgraduates and dentists working in group practices and affluent areas were found to have relatively more tendency towards record keeping. Recommendation; Dentist training institutes and health implementing authorities are the main areas which need to be stressed upon for improvement of record keeping. Keywords: (MESH) Record keeping, Dental photography, Dentist, Post-graduate, Health authorities, Affluent areas.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

Objective: To determine the incidence of congenital talipes equinovarus among children reporting at tertiary care hospital. Study setting & Duration: The study was conducted at Watim Medical & Dental College Rawat, Rawalpindi in the departments of Anatomy, Medical education & Dentistry from January 2021 to December 2021. Materials and Methods: There were 988 participants in this study. An easy questionnaire was used to gather information on children with CTEV who presented themselves to the outdoor department. Both consanguineous and non-consanguineous relationships were considered. Results: A total of 96 (9.7%) children presented with congenital talipes equinovarus or club foot and out of these children 56 (5.7%) patients were males and 40 (4.04%) were female. Conclusion: Clubfoot or congenital talipes equinovarus is one of the most common abnormalities of the foot and idiopathic talipes equinovarus is most common in the studied population. It is more common in males and is mostly bilateral. It should be detected at birth and treated early to prevent permanent disability. Keywords: Children, CTEV, idiopathic, consanguinity

Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

A standard screening procedure involves video endoscopy of the Gastrointestinal tract. It is a less invasive method which is practiced for early diagnosis of gastric diseases. Manual inspection of a large number of gastric frames is an exhaustive, time-consuming task, and requires expertise. Conversely, several computer-aided diagnosis systems have been proposed by researchers to cope with the dilemma of manual inspection of the massive volume of frames. This article gives an overview of different available alternatives for automated inspection, detection, and classification of various GI abnormalities. Also, this work elaborates techniques associated with content-based image retrieval and automated systems for summarizing endoscopic procedures. In this survey, we perform a comprehensive review of feature extraction techniques and deep learning methods which were specifically developed for automatic analysis of endoscopic videos. In addition, we categorize features extraction techniques according to image processing domains and further we classify them based on their visual descriptions. We also review hybrid feature extraction techniques which are developed by the fusion of different kind of basic descriptors. Moreover, this survey covers various endoscopy data-sets available for the bench-marking of vision based algorithms. On the basis of literature, we explain emerging trends in computerized analysis of endoscopy. We also survey important issues, challenges, and future research directions to the development of computer-assisted systems for detection of maladies and interactive surgery in the GI tract.

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.

This paper proposes a computer assisted diagnostic (CAD) system for the detection of melanoma in dermoscopy images. Clinical findings have concluded that in case of melanoma, the lesion borders exhibit differential structures such as pigment networks and streaks as opposed to normal skin spots, which have smoother borders. We aim to validate these findings by performing segmentation of the skin lesions followed by an extraction of the peripheral region of the lesion that is subjected to feature extraction and classification for detecting melanoma. For segmentation, we propose a novel active contours based method that takes an initial lesion contour followed by the usage of Kullback-Leibler divergence between the lesion and skin to fit a curve precisely to the lesion boundaries. After segmentation of the lesion, its periphery is extracted to detect melanoma using image features that are based on local binary patterns. For validation of our algorithms, we have used the publicly available PH < formula > < tex > $^{2}$ < /tex > < /formula > dermoscopy dataset. An extensive experimental analysis reveals two important findings: 1). The proposed segmentation method mimics the ground truth data accurately, outperforming the other methods that have been used for comparison purposes, and 2). The most significant melanoma characteristics in the lesion actually lie on the lesion periphery.

Brain tumor is a serious life-threatening disease. The early diagnosis of brain tumor is very important for the proper treatment. The removal of brain tumor requires implementation of an invasive procedure relying on biopsy and spinal tap method. There are more than 120 types of brain tumor. Therefore, the identification of a particular brain tumor is a very difficult task because of the small size and complex structure of the brain. A lot of research has been conducted to classify brain tumors into benign and malignant tumors but there is very limited research work available to classify brain tumor by its types. Among the various types of brain tumors is Glioblastoma which is very aggressive and life threatening. The main purpose of the proposed approach is accurate classification of brain tumor contained images followed by localization on those images. The proposed approach uses Convolutional Neural Networks (CNNs) for classification and Region-based Convolutional Neural Networks (R-CNN) and Faster Region-based Convolutional Neural Networks (Faster R-CNN) to localize tumor regions in MRI images after classification into three types, i.e. Glioblastoma, Dendroglioma and Astrocytoma. Moreover, the proposed approach involves the use of transfer learning techniques, namely feature extraction and fine tuning and achieves classification rate of 95.40% and detection rates of 89.10%.

In Alzheimer’s disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer’s disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed. While depicting a real-world setting, longitudinal data of three years are employed for training, whereas only the baseline visit's data is used for validation. The train set is preprocessed for extraction of two dense clusters representing the subjects who remain stable at MCI or progress to AD after three years of the baseline visit. Similarity between these clusters and the test point is calculated in Euclidean space. Multiple features from two modalities of biomarkers, i.e., neuropsychological measures (NM) and structural magnetic resonance imaging (MRI) morphometry are also analyzed. Due to the limited MCI dataset size (NM: 145, MRI: 52, NM+MRI: 29), leave-one-out cross validation setup is employed for performance evaluation. The algorithm performance is noted for both unimodal case and bimodal cases. Superior performance (accuracy: 89.66%, sensitivity: 87.50%, specificity: 92.31%, precision: 93.33%) is delivered by multivariate predictors. Three notable conclusions of this study are: 1) Longitudinal data are more powerful than the temporal data, 2) MRI is a better predictor of MCI-to-AD conversion than NM, and 3) multivariate predictors outperform single predictor models.

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineering

Assessment of osteoporotic disease from the radiograph image is a significant challenge. Texture characteristics when observed from the naked eye for the bone microarchitecture of the osteoporotic and healthy cases are visually very similar making it a challenging classification problem. To extract the discriminative patterns in all the orientations and scales simultaneously in this study we have proposed an approach that is based on a combination of multi resolution Gabor filters and 1D local binary pattern (1DLBP) features. Gabor filter are used due to their advantages in yielding a scale and orientation sensitive analysis whereas LBPs are useful for quantifying microstructural changes in the images. Our experiment show that the proposed method shows good classification results with an overall accuracy of about 72.71% and outperforms the other methods that have been considered in this paper.

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization (HPSO) algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

Cardiac auscultation is a method used to listen heart sound. Condition of the heart can be predicted with cardiac auscultation because heart generates a specific rhythm of sound and any changes in the rhythm of the heart sound may be due to abnormalities of heart. Auscultation is an easy way to diagnose heart abnormalities; however, it needs training and years of physician’s experience to diagnose heart and identify any heart abnormalities. With years of experience, it is still difficult to analyze heart sound. The ability to automatically identify abnormalities or at least support physician decision is relevant to ease the reach of medical diagnosis using mobile or Digi-scope. This paper presents a novel approach for segmentation of S1 and S2 heart sounds by using some of the heart sounds temporal and spectral features. Our method differentiates between S1 and S2 heart sounds and also improves the results as compared to the three finalists.

In this paper, a novel method towards rotation, scale and illumination invariant texture image classification is introduced. We exploit the useful rotation and scale characteristics of Gabor filters and illumination invariance characteristics of LBP, proposing image features which are invariant to the above mentioned imaging dynamics. The images are first filtered using Gabor filters followed by a summation of filter responses across scales. An LBP of the resulting features is calculated followed by an integral histogram of LBPs across various orientations of the Gabor filters. An experimental validation of the invariance of the descriptor is shown on a texture classification problem using two publicly available datasets: the USC-SIPI, and CUReT texture datasets. Our experiments show that the proposed descriptor outperforms the other methods that have been considered in this paper.

In this paper, we propose a novel segmentation method that has been used for segmenting lesions in dermoscopy images. This method uses the variational level sets formulation with a novel area term based on supervised learning that results in the global optimization of a cost function, that can potentially result in a robust segmentation of the images. This term uses a mixture of Gaussians that are trained from a set of training images, and evolves an active contour such that the difference between the learned models and the empirical distributions obtained from the evolving curve for both the lesion and the skin are minimized. In the end, our approach is validated on the publicly available PH2 dermoscopy imaging dataset and the results show that the proposed method outperforms the other state-of-the-art methods that have been considered in this paper.

Osteoporosis is an age-based disease causing skeletal disorder. It is described by the decreased bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy patients and osteoporotic cases show a great resemblance. The texture representation is done using two type of methods: appearance based methods and feature based methods. This study explains two systems, one based on PCA and one based on LDA. The system contains two stages, first one is PCA or LDA based feature computation and the second is the classification stage. The classification has been done using classifiers i.e. kNN, NB and SVM. The discriminating power of the texture descriptors is assessed using ten-folded cross-validation scheme using different machine learning techniques. The scope of the study is to support therapists in osteoporosis prediction, avoiding unnecessary further testing with bone.

In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a troublesome task especially while tracking the object over successive frames. Numerous methods have been proposed over the years for efficient detection of object of interest in a remote scene environment while in he meanwhile discarding all those which aren’t of interest and thus considered as noise. It is still one of the most actively researched areas in the field of image processing and computer vision. In this paper, a method is proposed which will not only detect a fixed shape object in a remote scene environment but it will also track it over successive frames. However, an additional methodology is also proposed which will detect the object in case of change of viewing angles e.g. scenario’s like rotation of object, zooming etc. First, Scale Invariant Feature Transform (SIFT) will be presented which will provide invariance up to four different parameters i.e. rotation, translation and zoom. In the second phase, ASIFT will be used which will provide invariance up to six different parameters i.e. translation, rotation, zoom and camera axis orientations. After both algorithms are presented, a detailed comparison between both is presented. Detection of object is performed with the help of both SIFT and ASIFT and then comparison is made based on feature points. Finally, Tracking is performed based on Proximal Gradient Particle filters which will further strengthen the comparison between SIFT and ASIFT once the object that needs to be tracked changes its course of motion or zoom. Experimental results will show which one of the two filters is more efficient.

Object recognition and semantic segmentation have been the two most common problems of traditional scene understanding in the computer vision domain. Major breakthroughs were reported in the last few years because of the increased utilization of deep learning, which offer a convincing alternative by learning the problem specific features on their own. In this paper, a summary of the frequently used framework – convolutional neural networks (CNN) is discussed. Accordingly a categorization scheme has been proposed to analyze the deep networks developed for image segmentation. Under this scheme, thirteen methods from the literature have been reviewed which are classified on the basis on how they perform segmentation operation i.e. semantic segmentation, instance segmentation and hybrid approaches. These method were reviewed from different aspects like their category, the novelty in the architecture of the method, and their special features in contrast with the traditional approaches. Latest review and analysis of these segmentation approaches, which provided outstanding results for image segmentation compared to the ordinary system, reveals that deep learning is increasingly becoming an important part of image segmentation and improvement in deep learning algorithms, which could resolve computer vision problems.

The assessment of osteoporotic subjects from X-ray images poses a significant challenge for pattern recognition and medical diagnostic applications. Textured images of bone micro-architecture of osteoporotic and healthy subjects exhibit high degree of similarity hence amplifying difficulty of classifying such textures. This research is focused on exploring different texture based methods to segregate osteoporotic from healthy controls. We enacted set of well evaluated preprocessing model to enhance the prospects of drawing a distinct line between two classes while exercising diverse texture analysis approaches including Grey Level Co-occurrence Matrix (GLCM), two-dimensional and one-dimensional Local Binary Patterns. Finally we propose a hybrid technique to attain an enhanced class distinction. Consequently experiments were conducted on two populations of osteoporotic patients and controls, with comparative analysis of the results.

In this paper an automatic tracking approach is proposed for the measurement of the cervical spine using Kanade-Lucas-Tomasi (KLT) feature tracking algorithm through fluoroscopic sequences. Previous research related to cervical vertebrae shows that abnormalities in the cervical vertebrae structures may affect the movement of the cervical spine. The aim of this paper is to automate the detection of range of movement in a lateral view of the spine during a flexion-extension cycle. The parameters analyzed were translation and in-plane rotation of the individual vertebrae. For analysis of these parameters, fluoroscopic recordings of three individuals were used. The algorithm marked landmarks on first frame, considered them as reference position and extracted translation and rotation of these vertebrae landmarks in the successive frames using Harris corner detector and use link motion vector for trajectory. Manual selection of vertebrae C3 to C6 (annotated data) were used for the validation of the proposed algorithm. The automated results are very close and uniform to the manual selection.

Multi-projector displays allow the realization of large and immersive projection environments by allowing the tiling of projections from multiple projectors. Such tiled displays require real time geometrical warping of the content that is being projected from each projector. This geometrical warping is a computationally intensive operation and is typically applied using high-end graphics processing units (GPUs) that are able to process a defined number of projector channels. Furthermore, this limits the applicability of such multi-projector display systems only to the content that is being generated using desktop based systems. In this paper we propose a platform independent FPGA based scalable hardware architecture for geometric correction of projected content that allows addition of each projector channel at a fractional increase in logic area. The proposed scheme provides real time correction of HD quality video streams and thus enables the use of this technology for embedded and standalone devices.

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC)...

Spam and unwanted content has been a significant challenge for the Internet technologies (email, social networks, search engines, etc.) for decades. However, in recent years, the advent of modern and cheap telephony technologies and larger user base (more than six billion users) has attracted scammers to use telephony for distributing unwanted content via instant messaging and calls. Detection of unwanted caller in the telephony has become challenging because the content is available only after the call has already been answered by the recipients and thus is too late to block the unwanted caller after the call has already been established. One of the interesting possibilities is to develop a telephony blacklist database using social behaviour of users towards their friends and family circle by modelling call meta-data as a weighted network graph. In this chapter, we model user’s behaviour as a weighted call graph network and identify malicious users by analysing different network features of users. To this extent, we have identified a set of features that help represent malicious and non-malicious behaviour of users in a network. We have conducted rigorous experimentation of the proposed system via its implementation with data set collected by small-scale telecommunication operator. We present the outcomes of our evaluation highlighting the efficacy of the system’s performance and identifying possible directions for future work.

Melanoma is the most deadly form of skin cancer and its incidence rate is significantly increasing. The design of an assisted diagnosis system for the detection of melanoma is a challenging task involving various steps related to computer vision. Researchers have concluded that the accurate identification of melanoma requires robust preprocessing steps on dermoscopy images including hair removal, illumination correction etc., that can help in a better detection of melanoma. In this paper, we propose a novel illumination correction algorithm followed by robust feature extraction from dermoscopy images, leading to a better identification of cancer. Illumination correction is based on statistical estimation of illumination content in the images, followed by the extraction of differential structures using a combination of Gabor filtering followed by extracting local mesh patterns, which exhibit physiological significance based on various clinical rules for detecting melanoma. Our experiments show that the proposed technique outperforms all the other methods that have been considered in this paper.

Electroencephalogram (EEG) data is a set of brain signals recorded by special EEG headsets. These signals reflect the cortical electrical activity. The technique for utilization of EEG data has emerged to be a safe and portable non-invasive Brain Computer Interface (BCI) that can easily be used for studying the human cognitive states. In this paper we have focused on studying the pilot's cortical potentials in simulated flight environment in order to classify his mental state into three categories i.e. rest mode, navigation flying mode, and dogfight mode. 14 channel Emotiv EEG headset was used by the subjects while playing a fighter aircraft game which could simulate all the required scenarios. The subject was screened in a dark room with huge projector screen along with audio stimuli. Several sessions of EEG data were recorded and feature extraction was carried out. Random Forest Tree classification algorithm proved to produce the best results. The pilot's cognitive state was classified according to the labeled recordings by taking one second of data each time and classifying it. As a result 81.7 % accuracy was achieved. The decent accuracy of results prove that real time pilot cognitive state can be decoded effectively, and if transmitted live onto the ground command and control room, it can be utilized for ensuring pilots safety as well as for training and monitoring of pilots on-board activities.

Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background. ROI of image is extracted out by using one of the segmentation techniques named as adaptive thresholding with morphological operators. After extracting ROI, we analyzed the classification results by using texture and color features on the extracted region and compared the results. The images were obtained from one of the hospitals of Portugal utilizing dermoscopy as an imaging methodology. Our extracted algorithm shows better performance than conventional thresholding and based on experimental results it can be concluded that the color features outperform the classification results but it gives more accurate results by combining color and texture features extracted from ROI.

Mild cognitive impairment is a preclinical stage of Alzheimer's disease (AD). For effective treatment of AD, it is important to identify mild cognitive impairment (MCI) patients who are at a high risk of developing AD over the course of time. In this study, autoregressive modelling of multiple heterogeneous predictors of Alzheimer's disease is performed to capture their evolution over time. The models are trained using three different arrangements of longitudinal data. These models are then used to estimate future biomarker readings of individual test subjects. Finally, standard support vector machine classifier is employed for detecting MCI patients at risk of developing AD over the coming years. The proposed models are thoroughly evaluated for their predictive capability using both cognitive scores and MRI-derived measures. In a stratified five-fold cross validation setup, our proposed methodology delivered highest AUC of 88.93% (Accuracy = 84.29%) and 88.13% (Accuracy = 83.26%) for 1 year and 2 year ahead AD conversion prediction, respectively, on the most widely used Alzheimer's disease neuroimaging initiative data. The notable conclusions of this study are: 1) Clinical changes in MRI-derived measures can be better forecasted than cognitive scores, 2) Multiple predictor models deliver better conversion prediction than single biomarker models, 3) Cognitive score boosted by MRI-derived measures delivers better short-term ahead conversion prediction, and 4) Neuropsychological scores alone can deliver good accuracy for long-term conversion prediction.

A fully invariant system helps in resolving difficulties in object detection when camera or object orientation and position are unknown. In this paper, the proposed correlation filter based mechanism provides the capability to suppress noise, clutter and occlusion. Minimum Average Correlation Energy (MACE) filter yields sharp correlation peaks while considering the controlled correlation peak value. Difference of Gaussian (DOG) Wavelet has been added at the preprocessing stage in proposed filter design that facilitates target detection in orientation variant cluttered environment. Logarithmic transformation is combined with a DOG composite minimum average correlation energy filter (WMACE), capable of producing sharp correlation peaks despite any kind of geometric distortion of target object. The proposed filter has shown improved performance over some of the other variant correlation filters which are discussed in the result section.

Gastroenterology imaging is a diagnostic procedure that incorporates various computer vision challenges for the design of assisted diagnostic systems. The most typical challenge is the design of more adequate visual descriptors that can assist the classification algorithms in getting good diagnostic results. Literature shows that most of the texture descriptors for feature extraction from gastric lesions are based on Gabor filters or local binary patterns (LBP). Although good results are obtained, these techniques have their shortcomings. In this paper, we aim to explore the use of fusion of Gabor filters and LBPs for characterizing gastric lesions. The images are first subjected to Gabor filtering using isotropic Gabor filters, followed by extracting LBPs from the filtered images. We validate the performance of the descriptor on a novel gastroenterology dataset: the Post-MAPS dataset. Our results show that the proposed feature set outperforms the other methods that have been considered in this paper.

Diagnosis of dermoscopic skin lesions due to skin cancer is the most challenging task for the experienced dermatologists. In this context, dermoscopy is the non-invasive useful method for the detection of skin lesions which are not visible to naked human eye. Among different types of skin cancers, malignant melanoma is the most aggressive and deadliest form of skin cancer. Its diagnosis is crucial if not detected in early stage. This paper mainly aims to present an efficient machine learning approach for the detection of melanoma from dermoscopic images. It detects melanomic skin lesions based upon their discriminating properties. In first step of proposed method, different types of color and texture features are extracted from dermoscopic images based on distinguished structures and varying intensities of melanomic lesions. In second step, extracted features are fed to the classifier to classify melanoma out of dermoscopic images. Paper also focuses on the role of color and texture features in the context of detection of melanomas. Proposed method is tested on publicly available PH2 dataset in terms of accuracy, sensitivity, specificity and Area under ROC curve (AUC). It is observed that good results are achieved using extracted features, hence proving the validity of the proposed system.

In this letter, we propose to tackle rotation and scale variance in texture classification at the machine learning level. This is achieved by using image descriptors that interpret these variations as shifts in the feature vector. We model these variations as a covariate shift in the data. This shift is then reduced by minimising the Kullback-Leibler divergence between the true and estimated distributions using importance weights (IW). These IWs are used in support vector machines (SVMs) to formulate the IW-SVMs. The experimental results show that IW-SVMs exhibit good invariance characteristics and outperform other state-of-the-art classification methods. The proposed methodology gives a generic solution that can be applied to any texture descriptor that models the transformations as a shift in the feature vector.

This letter introduces a novel approach to rotation and scale invariant texture classification. The proposed approach is based on Gabor filters that have the capability to collapse the filter responses according to the scale and orientation of the textures. These characteristics are exploited to first calculate the homogeneous texture of images followed by the rearrangement of features as a two-dimensional matrix (scale and orientation), where scaling and rotation of images correspond to shifting in this matrix. The shift invariance property of discrete fourier transform is used to propose rotation and scale invariant image features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this letter.

Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability of this detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. In this paper, we propose a novel method for the segmentation of gastroenterology images from two distinct imaging modalities and organs: chromoendoscopy (CH) and narrow-band imaging (NBI) from stomach and esophagus, respectively. We have used various visual features individually and their combinations (edgemaps, creaseness, and color) in normalized cuts image segmentation framework to segment ground truth datasets of 142 CH and 224 NBI images. Experiments show that an integration of edgemaps and creaseness in normalized cuts gives the best segmentation performance resulting in high-quality segmentations of the gastroenterology images.