Search results for: mental images

Authors: Saman Ghaffarian, Ilgin Gökaşar

Abstract:

This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

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Authors: R. R. Hordijk, O. J. G. Somsen

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Traditionally, radar systems are the eyes and ears of a ship. However, these systems have their drawbacks and nowadays they are extended with systems that work with video and photos. Processing of data from these videos and photos is however very labour-intensive and efforts are being made to automate this process. A major problem when trying to recognize objects in water is that the 'background' is not homogeneous so that traditional image recognition technics do not work well. Main question is, can a method be developed which automate this recognition process. There are a large number of parameters involved to facilitate the identification of objects on such images. One is varying the resolution. In this research, the resolution of some images has been reduced to the extreme value of 1% of the original to reduce clutter before the polynomial fit (pre-processing). It turned out that the searched object was clearly recognizable as its grey value was well above the average. Another approach is to take two images of the same scene shortly after each other and compare the result. Because the water (waves) fluctuates much faster than an object floating in the water one can expect that the object is the only stable item in the two images. Both these methods (pre-processing and comparing two images of the same scene) delivered useful results. Though it is too early to conclude that with these methods all image problems can be solved they are certainly worthwhile for further research.

Keywords: image processing, image recognition, polynomial fit, water

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Authors: Zolani Metu

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The death of more than 140 psychiatric patients who were unethically deinstitutionalized from the Life Esidimeni hospital Johannesburg, in 2016, shined a light on South Africa’s failing public mental healthcare system. Compounded by insufficient research evidence on African deinstitutionalization, this necessitates inquiries into deinstitutionalized mental healthcare, reintegration and community-based mental healthcare within the South African context. This study employed a quantitative research approach which utilized a cross-sectional research design, to investigate experiences with the reintegration of institutionalized forensic mental health service users into communities in the Western Cape, South Africa. A convenience sample of 100 mental health care workers from different occupational and organizational backgrounds in the Western Cape was purposively selected using the Western Cape Health Directorate as a sampling frame. A self-administered questionnaire (SAQ) was used as the data collection instrument. The results of the study indicate that criminogenic factors such as substance use, history of violent behaviour, criminal history and disruptive social behaviour complicate the reintegration of forensic mental health service users into communities. The current extent of reintegration of forensic mental health service users was found to be 'poor' (46%; n= 46); and financial difficulties, criminogenic factors and limited Community-Based Care (CBC) facilities were identified as key barriers to the reintegration process. 56% of all job applications for forensic mental health service users were unsuccessful, and 53% of all applications for their admission into CBC facilities were declined. Although social support (informal) was found to be essential for successful reintegration, institutional support (formal) through assertive community treatment (35%; n= 35) and CBC facilities (21%) and the disability grant (DG=50%) was found to be more important for family coping and reintegration. Moreover, 72% of respondents had positive perceptions about the process of reintegration; no statistically significant relationship was found between years of experience and perceptions about reintegration (P-value = 0.062); and perceptions were not found to be a barrier to reintegration. No statistically significant relationship was found between years of working experience and understanding the legislative framework of deinstitutionalization (P-Value =.0.061). However, using a Chi-square test, a significant relationship (P-value = 0.021) was found between sex and understanding the legal framework involved in the process of reintegration. The study recommends a post-2020 deinstitutionalization agenda that factors-in criminogenic realities associated with forensic mental health service users, and affirms the strengthening of PHC and community based care systems as precedents of successful deinstitutionalization and reintegration of mental health service users.

Keywords: forensic mental health, deinstitutionalization, reintegration, mental health service users

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Authors: Reetinder Kaur, R. K. Pathak

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Mental health is recognised as an important global health concern. World Health Organisation in 2004 estimated that neuropsychiatric illnesses in India account for 10.8 percent of the global burden. The prevalence of serious mental illnesses is estimated as 6.5 percent by National Commission of Macroeconomics and Health in 2005. India spends only 0.06 percent of its health budget on mental health. One of the major problems that exist in Indian mental health care is the treatment gap due to scarcity of manpower, inadequate infrastructure and deficiencies in policy initiatives. As a result, traditional healing is a popular resource for mentally ill individuals and their families. The various traditional healing resources include faith healers, healers at temples and Dargahs. Chandigarh is a Union Territory located in North India. It has surplus manpower and infrastructure available for mental health care. Inspite of availability of mental health care services, mentally ill individuals and their families seek help from traditional healers at various Dargahs within or outside Chandigarh. For the present study, the data was collected from four dargahs. A total of thirty patients medically diagnosed with various mental illnesses, their family members who accompanied them and healers were part of this study. The aim of the study was to: Understand the interactions between healer, patient and family members during the course of treatment, understand explanations of mental illnesses and analyse the healing practices in context of culture. The interviews were conducted using an interview guide for the three sets of informants: Healers, patients and family members. The interview guide for healer focussed on the healing process, healer’s understanding of patient’s explanatory models, healer’s knowledge about mental illnesses and types of these illnesses cured by the healer. The interview guide for patients and family members focussed on their understanding of the symptoms, explanations for illness and help-seeking behaviour. The patients were observed over the weeks (every Thursday, the day of pir and healing) during their visits to the healer. Detailed discussions were made with the healer regarding the healing process and benefits of healing. The data was analysed thematically and the themes: The role of sacred, holistic healing, healer’s understanding of patient’s explanatory models of mental illness, the patient’s, and family’s understanding of mental illnesses, healer’s knowledge about mental illnesses, types of mental illnesses cured by the healer, bad dreams and their interpretation emerged. From the analysis of data, it was found that the healers concentrate their interventions in the social arena, ‘curing’ distressed patients by bringing significant changes in their social environment. It is suggested that in order to make the mental health care services effective in India, the collaboration between healers and psychiatrist is essential. However, certain specifications need to be made to make this kind of collaboration successful and beneficial for the stakeholders.

Keywords: Dargah, mental illness, traditional healing, policy

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Authors: Tara Hunter, Kristine Concepcion, Wendy Cheng, Brianna Pike, Jane Estoesta, Anne Stuart

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In 2015, Family Planning NSW was contracted by the NSW Ministry of Health to design and deliver Sexual Safety Policy training (SSPT) to mental health professionals across NSW. The training was based on their current guidelines and developed in consultation with an expert reference group. From October 2015 to April 2017 it was delivered to over 2,400 mental health professionals with a view to supporting implementation of consistent prevention and intervention related to sexual safety in the mental health setting. An evaluation was undertaken to determine the knowledge and confidence of participants related to sexual safety before and after the training, and whether any improvements were translated into changes in practice. Participants were invited to complete a survey prior to the training, upon completion and three to six months thereafter. Telephone interviews were conducted among service managers and mental health champions six months post-training. Prior to training, the majority of mental health professionals reported being slightly to moderately confident in identifying a sexual safety incident. When asked on their understanding of sexual safety, gender sensitive practice and trauma informed care, they reported no confidence, slight confidence and moderate confidence. Immediately after the training, 54.5% reported being very confident and 10.9% extremely confident in identifying a sexual safety incident. More than half felt very confident or extremely confident in their understanding of sexual safety principles. The impact survey (six months later) found that the majority of participants (91%) were highly confident in identifying a sexual safety incident. Telephone interviewees reported a change in workplace culture and increased awareness after the training. Mental health professionals experienced increased knowledge and confidence about sexual safety principles following the training and were able to implement positive changes and concrete actions to better address sexual safety issues in their workplace.

Keywords: sexual safety, mental health professionals, trauma informed care, policy training

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Authors: Zihan Yu, Kohei Inoue, Kiichi Urahama

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We present a method for de-hazing images. A dark envelope image is derived with the bilateral minimum filter and a bright envelope is derived with the bilateral maximum filter. The ambient light and transmission of the scene are estimated from these two envelope images. An image without haze is reconstructed from the estimated ambient light and transmission.

Keywords: image dehazing, bilateral minimum filter, bilateral maximum filter, local contrast

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Authors: Abdulkader Helwan

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Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

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Authors: Seyed Reza Mirmahdi, Seyedeh Maryam Hashemi Jabali

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This research was conducted to evaluate the effects of group spiritual therapy on increasing the life expectancy and mental health among the elderlies. This was a quasi-experimental research using a pretest-posttest design with a control group conducted over a population including all the elderly people of Tehran in 2012-13. A randomized sampling method was used to select 30 elderly people living in Parham nursing home that were then randomly assigned into two control and experimental groups of 15 people each. The instruments used were Miller’s life expectancy and mental health test (SCL.90.R) standard questionnaires. Individuals in experimental group received 12 sessions of group spiritual therapy while those in control group did not receive any kind of therapy. The tests were performed again for all the subjects (30 individuals) at the end of the experiment. To test the hypotheses, the data collected by questionnaires were analyzed using descriptive methods through relevant tables and charts and also inferential methods through the analysis of covariance using the SPSS software. Results showed that group spiritual therapy leads to a significant increase in both mental health and life expectancy in the experimental group of elderlies living in Parham nursing home compared to those in the control group.

Keywords: spiritual therapy, life expectancy, mental health, elderlies

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Authors: Singara Singh Kasana, Pankaj Garg

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In this paper, a blind data hiding technique based on interpolation of sub sampled versions of a cover image is proposed. Sub sampled image is taken as a reference image and an interpolated image is generated from this reference image. Then difference between original cover image and interpolated image is used to embed secret data. Comparisons with the existing interpolation based techniques show that proposed technique provides higher embedding capacity and better visual quality marked images. Moreover, the performance of the proposed technique is more stable for different images.

Keywords: interpolation, image subsampling, PSNR, SIM

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Authors: M. V. Chidananda Murthy, M. Z. Kurian, H. S. Guruprasad

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Super-resolution is the technique of intelligently upscaling images, avoiding artifacts or blurring, and deals with the recovery of a high-resolution image from one or more low-resolution images. Single-image super-resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super-resolution has been demonstrated to improve image quality in scaled down images in the image domain, its effects on the Fourier-based technique remains unknown. Super-resolution substantially improved the spatial resolution of the patient LGE images by sharpening the edges of the heart and the scar. This paper aims at investigating the effects of single image super-resolution on Fourier-based and image based methods of scale-up. In this paper, first, generate a training phase of the low-resolution image and high-resolution image to obtain dictionary. In the test phase, first, generate a patch and then difference of high-resolution image and interpolation image from the low-resolution image. Next simulation of the image is obtained by applying convolution method to the dictionary creation image and patch extracted the image. Finally, super-resolution image is obtained by combining the fused image and difference of high-resolution and interpolated image. Super-resolution reduces image errors and improves the image quality.

Keywords: image dictionary creation, image super-resolution, LGE images, patch extraction

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Authors: Zachary Pan

Abstract:

Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: features extraction, image segmentation, medical images, tumor detection

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Authors: Helene Martin, Solmaz Boroomandi Barati, Jean-Charles Pinoli, Stephane Valette, Yann Gavet

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In the present work we developed an image processing algorithm to measure water droplets characteristics during dropwise condensation on pillared surfaces. The main problem in this process is the similarity between shape and size of water droplets and the pillars. The developed method divides droplets into four main groups based on their size and applies the corresponding algorithm to segment each group. These algorithms generate binary images of droplets based on both their geometrical and intensity properties. The information related to droplets evolution during time including mean radius and drops number per unit area are then extracted from the binary images. The developed image processing algorithm is verified using manual detection and applied to two different sets of images corresponding to two kinds of pillared surfaces.

Keywords: dropwise condensation, textured surface, image processing, watershed

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

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Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: color overlapping windows, image stitching, leukocyte detection, white blood cell detection

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Authors: Onyebueke Godwin, Okwarafor Friday

Abstract:

Introduction: Persons with HIV/AIDS disease are predisposed to mental health disorders such as depression and suicide. HIV/AIDS, being a chronic medical illness with antecedent stigmatization ostracization, leads to low mood, low self-esteem, and a tendency to kill oneself due to the burden of the disease in terms of cost and disability. The aim of one study was to examine the prevalence of depression and risk of suicide among HIV/AIDS patients compared to negative persons. Instruments: The Major Depressive Episode and Suicidality modules of the MINI-Neuropsychiatric inventory were used to screen the attendees. Report: The prevalence of depression and risk of suicide were 27.8% and 7.8%, respectively, for the HIV positive subjects, but 1208% and 2.2%, respectively, for negative subjects. Conclusion and Significance: Persons with HIV/AIDS usually present with mental health symptoms, but the attending physicians usually pay attention to physical symptoms. The symptoms of the disease or the side effects of the medication may mask the mental health disease. Recommendation: There is need to screen HIV/AIDS patents for mental health disorders during clinic visits.

Keywords: depression, HIV/AIDS, suicidality

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Authors: Qiang Wang, Hongyang Yu

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Multi-human 3D pose estimation is a challenging task in computer vision, which aims to recover the 3D joint locations of multiple people from multi-view images. In contrast to traditional methods, which typically only use color (RGB) images as input, our approach utilizes both color and depth (D) information contained in RGB-D images. We also employ a transformer-based model as the backbone of our approach, which is able to capture long-range dependencies and has been shown to perform well on various sequence modeling tasks. Our method is trained and tested on the Carnegie Mellon University (CMU) Panoptic dataset, which contains a diverse set of indoor and outdoor scenes with multiple people in varying poses and clothing. We evaluate the performance of our model on the standard 3D pose estimation metrics of mean per-joint position error (MPJPE). Our results show that the transformer-based approach outperforms traditional methods and achieves competitive results on the CMU Panoptic dataset. We also perform an ablation study to understand the impact of different design choices on the overall performance of the model. In summary, our work demonstrates the effectiveness of using a transformer-based approach with RGB-D images for multi-human 3D pose estimation and has potential applications in real-world scenarios such as human-computer interaction, robotics, and augmented reality.

Keywords: multi-human 3D pose estimation, RGB-D images, transformer, 3D joint locations

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Soowon Park, Min-Ji Kim, Jun-Young Lee

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Motivation: The stigma of mental illness has been studied in many disciplines, including social psychology, counseling psychology, sociology, psychiatry, public health care, and related areas, because individuals labeled as ‘mentally ill’ are often deprived of their rights and their life opportunities. To understand the factors that deepen the stigma of mental illness, it is important to understand the influencing factors of the stigma. Problem statement: Depression is a common disorder in adults, but the incidence of help-seeking is low. Researchers have believed that this poor help-seeking behavior is related to the stigma of mental illness, which results from low mental health literacy. However, it is uncertain that increasing mental health literacy decreases mental health stigmatization. Furthermore, even though decreasing stigmatization is important, the stigma of mental illness is still a stable and long-lasting phenomenon. Thus, factors other than knowledge about mental disorders have the power to maintain the stigma. Investigating the influencing factors that facilitate the stigma of psychiatric disease could help lower the social stigmatization. Approach: Face-to-face interviews were conducted with a multi-clustering sample. A total of 700 Korean participants (38% male), ranging in age from 18 to 78 (M(SD)age= 48.5(15.7)) answered demographical questions, Korean version of Link’s Perceived Devaluation and Discrimination (PDD) scale for the assessment of social stigmatization against depression, and the Korean version of the WHO-Composite International Diagnostic Interview for the assessment of mental disorders. Multiple-regression was conducted to find the predicting factors of social stigmatization against depression. Ages, sex, years of education, income, living location, and experience of mental illness were used as the predictors. Results: Predictors accounted for 14% of the variance in the stigma of depressive disorders (F(6, 693) = 20.27, p < .001). Among those, only age, years of education, and experience of mental illness significantly predicted social stigmatization against depression. The standardized regression coefficient of age had a negative association with stigmatization (β = -.20, p < .001), but years of education (β = .20, p < .001) and experience of mental illness (β = .08, p < .05) positively predicted depression stigmatization. Conclusions: The present study clearly demonstrates the association between personal factors and depressive disorder stigmatization. Younger age, more education, and self-stigma appeared to increase the stigmatization. Young, highly educated, and mentally ill people tend to reject patients with depressive disorder as friends, teachers, or babysitters; they also tend to think that those patients have lower intelligence and abilities. These results suggest the possibility that people from a high social class, or highly educated people, who have the power to make decisions, help maintain the social stigma against mental illness patients. To increase the awareness that people from high social classes have more stigmatization against depressive disorders will help decrease the biased attitudes against mentally ill patients.

Keywords: depressive disorder stigmatization, age, education, self-stigma

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Authors: Denise Ben-Porath, Tracy Masterson

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In the past, individuals who struggled with and sought treatment for mental health difficulties were stigmatized. However, the current generation holds more open attitudes around mental health issues. Indeed, public figures such as Demi Lovato, Naomi Osaka, and Simone Biles have taken to social media to break the silence around mental health, discussing their own struggles and the benefits of treatment. Thus, there is considerable reason to believe that this generation would hold fewer stigmatizing attitudes toward mental health difficulties and treatment compared to previous ones. In this study, we explored possible changes in stigma on mental health diagnosis and treatment seeking behavior between two generations: Gen Z, the current generation, and Gen X, those born between 1965-1980. It was hypothesized that Gen Z would hold less stigmatizing views on mental illness than Gen X. To examine possible changes in stigma attitudes between these two generations, we conducted a cross-generational cohort design by using the same methodology employed 20 years ago from the Ben-Porath (2002) study. Thus, participants were randomly assigned to read one of the following four case vignettes employed in the Ben-Porath (2002) study: (a) “Tom” who has received psychotherapy due to depression (b) “Tom” who has been depressed but received no psychological help, (c) “Tom” who has received medical treatment due to a back pain, or (d) “Tom” who had a back pain but did not receive medical attention. After reading the vignette, participants rated “Tom” on various personality dimensions using the IFQ Questionnaire and answered questions about their frequency of social media use and willingness to seek mental health treatment on a scale from 1-10. Identical to the results 20 years prior, a significant main effect was found for diagnosis with “Tom” being viewed in more negative terms when he was described as having depression vs. a medical condition (back pain) [F (1, 376) = 126.53, p < .001]. However, in the study conducted 20 years earlier, a significant interaction was found between diagnosis and help-seeking behavior [F (1, 376) = 8.28, p < .005]. Specifically, “Tom” was viewed in the most negative terms when described as depressed and seeking treatment. Alternatively, the current study failed to find a significant interaction between depression and help seeking behavior. These findings suggest that while individuals who hold a mental health diagnosis may still be stigmatized as they were 20 years prior, seeking treatment for mental health issues may be less so. Findings are discussed in the context of social media use and its impact on destigmatization.

Keywords: stigma, mental illness, help-seeking, social media

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Authors: Wen Zhang

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The smartphone plays a significant role in media convergence, and smartphone photography is reconstructing the way we communicate and think. This article aims to explore the smartphone photography practices of urban Chinese smartphone users and images produced by smartphones from a techno-cultural perspective. The analysis consists of two types of data: One is a semi-structured interview of 21 participants, and the other consists of the images created by the participants. The findings are organised in two parts. The first part summarises the current tendencies of capturing, editing, sharing and archiving digital images via smartphones. The second part shows that food and selfie/anti-selfie are the preferred subjects of smartphone photographic images from a technical and multi-purpose perspective and demonstrates that screenshots and image texts are new genres of non-photographic images that are frequently made by smartphones, which contributes to improving operational efficiency, disseminating information and sharing knowledge. The analyses illustrate the positive impacts between smartphones and photography enthusiasm and practices based on the diffusion of innovation theory, which also makes us rethink the value of photographs and the practice of ‘photographic seeing’ from the screen itself.

Keywords: digital photography, image-text, media convergence, photographic- seeing, selfie/anti-selfie, smartphone, technological innovation

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Authors: Mohammed H. Rasheed, Hussein Nadhem Fadhel, Mohammed M. Siddeq

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In this paper we suggested image quality measuring metrics tools that can provide an accurate and close to the perceived quality sense of the tested images. Such tools give metrics that can be used to compare the performance of image compression algorithms. In this paper, two new metrics to measure the quality of decompressed images are proposed. The metric measurement based on combined data (CD) between an originals and decompressed images. Compared with other e.g., PSNR and RMSE, the proposed metrics gives values with the closest reflection of image quality perception by the human eye.

Keywords: RMSE, PSNR, image quality metrics, image compression

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Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

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Authors: Szu-Yi Chang, Jiin-Ru Rong

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This paper discussed a case involving a young female patient with schizophrenia. The patient's condition was deteriorating, and she was becoming increasingly reliant on her family to take care of her, and as her father did not understand the illness well and was afraid that others will learn about the presence of a mentally ill individual in their family, he and the patient's mother were thus unable to cope with the patient's deteriorating condition, which in turn caused her to suffer from a lack of self-confidence and low self-esteem. The patient received nursing care from July 26th to October 25th, 2017, during which counseling, family visits, and phone interviews were carried out, and her condition was monitored. By referring to the practical ability indicators for community psychiatric mental health nursing that were developed by the psychiatric mental health nurses' association of the Republic of China, defining categories such as 'self-construction,' 'self-management,' 'disease management,' and 'family nursing,' and incorporating indicators for empowerment and various skills into the steps and strategies used for nursing care, we will able to help the patient to construct her own identity, raise her self-esteem, improve her ability to independently perform activities of daily living, strengthen her disease management ability, and gradually build up her life management skills. The patient's family was also encouraged to communicate more among themselves, so as to align them with the nursing care objectives of improving the patient's ability to adapt to community life and her disease. The results indicated that the patient was able to maintain her mental stability within her community. By implementing effective self-management and maintaining a routine life, the patient was able to continue her active participation in community work and rehabilitation activities. Improvements were also achieved with respect to family role issues by establishing mutual understanding among the patient's family members and gaining their support. It is recommended that mental health nurses can leverage their community mental health nursing skills and the related strategies to promote adaptation to community life among mental life patients.

Keywords: community psychiatric mental health nursing, family nursing, schizophrenia, self-management

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Authors: Nai-Jiin Yang, Tyler Renshaw

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Prior research has suggested that youth internalizing and externalizing problems significantly correlate with student subjective wellbeing (SSW) and achievement problems (SAP). Yet, only a few studies have used data from mental health screener based on the dual-factor model to explore the empirical relationships among internalizing problems, externalizing problems, academic problems, and student wellbeing. This study was conducted through a secondary analysis of previously collected data in school-wide mental health screening activities across secondary schools within a suburban school district in the western United States. The data set included 1880 student responses from a total of two schools. Findings suggest that both internalizing and externalizing problems are substantial predictors of both student wellbeing and academic problems. However, compared to internalizing problems, externalizing problems were a much stronger predictor of academic problems. Moreover, this study did not support academic problems that moderate the relationship between SSW and youth internalizing problems (YIP) and between youth externalizing problems (YEP) and SSW. Lastly, SAP is the strongest predictor of SSW than YIP and YEP.

Keywords: academic problems, externalizing problems, internalizing problems, school mental health, student wellbeing, universal mental health screening

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Authors: Jianzhou Ni, Hua Fan

Abstract:

It has been determined that supervisor support has a major influence on postgraduate students' academic emotions and is considered a method of successfully anticipating postgraduates' good psychological well-being levels. As a result, by assessing the mediating influence upon academic emotions for contemporary postgraduates in China, this study investigated the tight reciprocal relationship between psychological empowerment and positive mental well-being among postgraduates. To that end, a help enables a theoretical analysis of role clarity, academic emotion, and positive psychological health was developed, and its validity and reliability were demonstrated for the first time using the normalized postgrad relationship with supervisor scale, academic emotion scale, and positive mental scale, as well as questionnaire data from Chinese postgraduate students. This study used the cross-lagged (ARCL) panel model data to longitudinally measure 798 valid data from two survey questions polls taken in 2019 (T1) and 2021 (T2) to investigate the link between supervisor support and positive graduate student mental well-being in a bidirectional relationship of influence. The study discovered that mentor assistance could have a considerable beneficial impact on graduate students' academic emotions and, as a result, indirectly help learners attain positive mental health development. This verifies the theoretical premise that academic emotions partially mediate the effect of mentor support on positive mental health development and argues for the coexistence of the two. The outcomes of this study can help researchers gain a better knowledge of the dynamic interplay among three different research variables: supervisor support, academic emotions, and positive mental health, as well as fill gaps in previous research. In this regard, the study indicated that mentor assistance directly stimulates students' academic drive and assists graduate students in developing good academic emotions, which contributes to the development of positive mental health. However, given the restricted measurement time in this study's cross-lagged panel data and the potential effect of moderating effects other than academic mood on graduate students' good mental health, the results of this study need to be more fully understood and validated.

Keywords: supervisor support, academic emotions, positive mental health, interaction effects, longitudinal cross-lagged measurements

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Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

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Authors: Chorom Baek, Sungwon Choi

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The purpose of this study is to analyze the supervision system, and the training and continuing education of mental health professionals in USA, UK, Australia (New Zealand), Japan, and so on, and to deduce the implications of Korean mental health service system. In order to accomplish the purpose of this study, following methodologies were adopted: review on the related literatures, statistical data, the related manuals, online materials, and previous studies concerning issues in those countries for the past five years. The training program in Korea was compared with the others’ through this literature analysis. The induced matters were divided with some parts such as training program, continuing education, educational procedure, and curriculum. Based on the analysis, discussion and implications, the conclusion and further suggestion of this study are as follows: First, Korean Clinical Psychology of Association (KCPA) should become more powerful health main training agency for quality control. Second, actual authority of health main training agency should be a grant to training centers. Third, quality control of mental health professionals should be through standardization and systemization of promotion and qualification management. Fourth, education and training about work of supervisors and unification of criteria for supervision should be held. Fifth, the training program for mental health license should be offered by graduate schools. Sixth, legitimated system to protect the right of mental health trainees is needed. Seventh, regularly continuing education after licensed should be compulsory to keep the certification. Eighth, the training program in training centers should meet KCPA requirement. If not, KCPA can cancel the certification of the centers.

Keywords: clinical psychology, Korea, mental health system, training program

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Authors: Toufic Abd El-Latif Sadek

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The asphalt object represents the asphalted areas like roads, and the concrete object represents the concrete areas like concrete buildings. The efficient extraction of asphalt and concrete objects from one satellite thermal image occurred at a specific time, by preventing the gaps in times which give the close and same brightness values between asphalt and concrete, and among other objects. So that to achieve efficient extraction and then better analysis. Seven sample objects were used un this study, asphalt, concrete, metal, rock, dry soil, vegetation, and water. It has been found that, the best timing for capturing satellite thermal images to extract the two objects asphalt and concrete from one satellite thermal image, saving time and money, occurred at a specific time in different months. A table is deduced shows the optimal timing for capturing satellite thermal images to extract effectively these two objects.

Keywords: asphalt, concrete, satellite thermal images, timing

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Authors: Rhea Kapoor

Abstract:

Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Cindy Davis, Samantha Rayner

Abstract:

Secondary traumatic stress (STS) is an indirect form of trauma affecting the psychological well-being of mental health workers; STS is found to be a prevalent risk in mental health occupations. Various factors impact the development of STS within the literature; including the level of trauma individuals are exposed to and their level of empathy. Research is limited on STS in mental health workers in Australia; therefore, this study examined STS and related factors of empathetic behavior and trauma caseload among mental health workers. The research utilized an online survey quantitative research design with a purposive sample of 190 mental health workers (176 females) recruited via professional websites and unofficial social media groups. Participants completed an online questionnaire comprising of demographics, the secondary traumatic stress scale and the empathy scale for social workers. A standard hierarchical regression analysis was conducted to examine the significance of covariates, traumatized clients, traumatic stress within workload and empathy in predicting STS. The current research found 29.5% of participants to meet the criteria for a diagnosis of STS. Age and past trauma within the covariates were significantly associated with STS. Amount of traumatized clients significantly predicted 4.7% of the variance in STS, traumatic stress within workload significantly predicted 4.8% of the variance in STS and empathy significantly predicted 4.9% of the variance in STS. These three independent variables and the covariates accounted for 18.5% of the variance in STS. Practical implications include a focus on developing risk strategies and treatment methods that can diminish the impact of STS.

Keywords: mental health, PTSD, social work, trauma

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