Image-related Problems Research Articles

Digital holography without a dark room environment: extraction of interference fringes byusing deep learning.

When obtaining digital holograms, dark rooms are used to prevent the influence of natural light on the formation of holograms. Further, in recent years, researchers have actively studied machine learning techniques such as deep learning to resolve image-related problems. In this study, we obtained a pair of holograms influenced by natural light and holograms unaffected by natural light, and trained U-Net to perform image transformation to remove the effects of natural light from holograms. Thus, this study aimed to propose a method for eliminating the effects of natural light from holograms by using the U-Net we trained. To verify the effectiveness of the proposed method, we evaluated the image quality of the reconstructed image of holograms before and after image processing by U-Net. The results showed that the peak signal-to-noise ratio (PSNR) increased by 7.38[dB] after processing by U-Net. Additionally, the structural similarity index (SSIM) increased by 0.0453 after processing by U-Net. This study confirmed that in digital holography, holograms can be acquired without the use of a dark room and that the method proposed in this study can eliminate the effects of natural light and produce high-quality reconstructed images.

Does the postpartum body image of primiparous women giving vaginal delivery affect their sexual functions and depression and fatigue levels?

The body image-related problems can negatively affect women’s lives in many ways. The study was aimed at investigating the effects of postpartum body image on sexual functions, and fatigue and depression levels of primiparous women who gave birth vaginally. This cross-sectional study was conducted with 133 women who presented to the obstetrics and gynecology clinics of a state hospital in Turkey between June 2021 and December 2021. At the end of the study, a moderate relationship was determined between the women’s body image and their depression (r = 0.500; p < 0.001) and fatigue (r = 0.666; p < 0.001) levels, and a weak relationship between their body image and sexual functions (r = −0.372; p < 0.001). According to the backward regression model used in the study, the women’s fatigue and education levels accounted for their body image by 45.7%.

Phase retrieval using hologram transformation with U-Net in digital holography

Digital holography is a method of recording light waves emitted from an object as holograms and then reconstructing the holograms using light wave propagation calculations to observe the object in three dimensions. However, a problem with digital holography is that unwanted images, such as conjugate images, are superimposed as the hologram is reconstructed to create an observed image. In particular, the superimposition of conjugate light on the observed image is caused by the imaging device’s ability to record just the intensity distribution of light rather than the phase distribution of light. In digital holography, it has been shown that unwanted light can be eliminated by the phase-shift method. However, it is difficult to apply the phase-shift method to digital holographic microscopy (DHM), which takes only one shot of light intensity. Alternatively, machine learning methods called deep learning have been actively studied in recent years for image-related problems, with image transformation as an example. Furthermore, a method that combines digital holography and deep learning has been proposed to perform image transformation to remove conjugate images using deep learning on the reconstructed image of a hologram. In this study, we generated a pair of holograms with only light intensity distribution and holograms with complex amplitude by simulating light wave propagation, trained U-Net to perform image transformation that adds phase information to the hologram with only light intensity distribution, and proposed a method for phase retrieval and conjugate image removal for holograms using the learned U-Net. To verify the effectiveness of the proposed method, we evaluated the image quality of the reconstructed image of holograms before and after processing by U-Net. Results showed that the peak signal-to-noise ratio (PSNR) increased by 8.37 [dB] in amplitude and 9.06 [dB] in phase. The amplitude and phase of the structural similarity index (SSIM) increased by 0.0566 and 0.0143, respectively. Furthermore, the results of applying the proposed method to holograms captured by actual digital holography optics showed the effectiveness of the proposed method in eliminating conjugate images in the reconstructed images. These results show that the proposed method is capable of phase retrieval of holograms in a single shot without the need for a complex optical system. This is expected to contribute to the field of portable DHMs and other applications that require compact and simple optical systems.

A CNN-Based Method for Counting Grains within a Panicle

The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.

Deep learning model for glioma, meningioma and pituitary classification

&lt;p&gt;One of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%.&lt;/p&gt;

Az Örnekle Öğrenme Problemleri için MAML ve ProtoNet Algoritmalarının İncelenmesi

Deep neural networks have proven to be very effective for image-related problems. However, their success is mainly attributed to the large-scale annotated datasets that have been used to train them. Convolutional neural networks which are special type of neural networks have achieved very good results for visual recognition problems and therefore have become the standard tool for these tasks. The use of large-scale annotated datasets like the ImageNet have even improved the results obtained by these networks. However, creating an annotated dataset of that scale is very difficult due to its cost. In some cases, even if there are enough resources, it is sometimes impossible to obtain such large datasets. It is shown that the neural networks cannot be trained well if there is not enough training data. As these networks require large amounts of annotated data to be able to generalize well, it is very important to develop new models that can be trained well even if the training data is not abundant. Meta-learning paradigm addresses this problem of few-shot learning by proposing models that can utilize the experience from the previous tasks to learn new tasks. Meta-learning algorithms gain the fast adaptation ability by using the meta-data obtained from the previous tasks. The meta-learning concept has regained its popularity after the success of some deep neural networks-based meta-learning algorithms for the few-shot image classification problems. In this study, two meta-learning algorithms, namely, Model-Agnostic Meta-Learning (MAML) and Prototypical Networks (ProtoNet) are applied to few-shot learning problems and their performance is evaluated. MiniImageNet and CIFAR100 few-shot learning image classification datasets have been used as the test bed, and the two algorithms have been evaluated under different meta-learning and algorithm hyper-parameter settings. The results suggest that MAML results in better classification accuracy than ProtoNet when the number of shot is taken as 1, and ProtoNet results in better accuracy when the number of shot is increased. The main reason for this is that while MAML tries to find common initial weights that can easily distinguish all classes, ProtoNet tries to find a different prototype for each class, and the number of shots definitely increases the representation power of that prototype.

Imageem: Pre-Trained Encoded Vector Embeddings for Image Modelling

Today some of the most intriguing challenges posed in the field of AI, is the lack of computational resources required to train the deep learning models, especially the image related problems. These Image modelling problems are solved by training a Convolutional Neural Network (CNN) which is computationally expensive process. Our aim is to extract the features of the images by training on these vector embeddings locally and then deploying it in the server for easy access to the researchers. The proposed system provides high dimensional vectors that capture dense features of objects. Pre-trained word embedding is a common way of representing words in a vocabulary present in a document. These embeddings have the capability to capture the context of a particular word in a sentence or an entire document, in relation with words other than the one under review. A popular algorithm for training these word embeddings is skip-gram model proposed in Word2Vec architecture.By using these trained vector embeddings of popular image models, researchers can download them and apply it to their use case which will reduce their training time by a thousand fold. Our aim is to make it so simple for the user that he can train it on edge devices like raspberry pi, jetson nano, these pre-trained vectors image models of high accuracy can be trained on even mobile phone processor.

The Importance of eSlide Macro Images for Primary Diagnosis with Whole Slide Imaging

Introduction: A whole slide image (WSI) is typically comprised of a macro image (low-power snapshot of the entire glass slide) and stacked tiles in a pyramid structure (with the lowest resolution thumbnail at the top). The macro image shows the label and all pieces of tissue on the slide. Many whole slide scanner vendors do not readily show the macro overview to pathologists. We demonstrate that failure to do so may result in a serious misdiagnosis. Materials and Methods: Various examples of errors were accumulated that occurred during the digitization of glass slides where the virtual slide differed from the macro image of the original glass slide. Such examples were retrieved from pathology laboratories using different types of scanners in the USA, Canada, Europe, and Asia. Results: The reasons for image errors were categorized into technical problems (e.g., automatic tissue finder failure, image mismatches, and poor scan coverage) and human operator mistakes (e.g., improper manual region of interest selection). These errors were all detected because they were highlighted in the macro image. Conclusion: Our experience indicates that WSI can be subject to inadvertent errors related to glitches in scanning slides, corrupt images, or mistakes made by humans when scanning slides. Displaying the macro image that accompanies WSIs is critical from a quality control perspective in digital pathology practice as this can help detect these serious image-related problems and avoid compromised diagnoses.

Robust kernel-based learning for image-related problems

Robustness is one of the most critical issues in the appearance-based learning techniques. This study develops a novel robust kernel for kernel machines, and consequently improves their robustness in resisting noise for solving the image-related learning problems. By incorporating a robust ρ-function to reduce the influence of outlier components, this kernel gives more reasonable kernel values when images are seriously corrupted. The authors incorporate the proposed kernel into different kernel-based approaches, such as support vector machine (SVM) and kernel Fisher discriminant (KFD) analysis, to validate its performance on various visual learning problems of face recognition and data visualisation. Experimental results indicate that the proposed kernel can provide the superior robustness to the classical approaches.

Feature space locality constraint for kernel based nonlinear discriminant analysis

Subspace learning is an important approach in pattern recognition. Nonlinear discriminant analysis (NDA), due to its capability of describing nonlinear manifold structure of samples, is considered to be more powerful to undertake classification tasks in image related problems. In kernel based NDA representation, there are three spaces involved, i.e., original data space, implicitly mapped high dimension feature space and the target low dimension subspace. Existing methods mainly focus on the information in original data space to find the most discriminant low dimension subspace. The implicit high dimension feature space plays a role that connects the original space and the target subspace to realize the nonlinear dimension reduction, but the sample geometric structure information in feature space is not involved. In this work, we try to utilize and explore this information. Specifically, the locality information of samples in feature space is modeled and integrated into the traditional kernel based NDA methods. In this way, both the sample distributions in original data space and the mapped high dimension feature space are modeled and more information is expected to be explored to improve the discriminative ability of the subspace. Two algorithms, named FSLC-KDA and FSLC-KSR, are presented. Extensive experiments on ORL, Extended-YaleB, PIE, Multi-PIE and FRGC databases validate the efficacy of the proposed method.

School Based Adolescent Care Services: A District Model

To study the perceived problems of higher secondary school students in a district and to document the effect of a family life and life skill education package. A survey was conducted among 11501 adolescents belonging to 103 higher secondary schools in Thiruvananthapuram district, using Teenage Screening Questionnaire-Trivandrum (TSQ-T). Family life and life skill education package was given to class XI students and post intervention evaluation of improvement in knowledge level was assessed after 6 months by a structured pre-tested self-administered questionnaire. 61.2% adolescents reported scholastic problem, 22.1% family related problems, 31.9% personal problems and 15.2% adjustment problems, with boys reporting higher percentage. 65.8% reported body image related problems, 26.4% had dental, 21.3% ENT and 16.2% had dermatological complaints. Among girls 50.2% reported menstrual problems. 506 adolescents volunteered for medical check-up and 1247 for detailed psychological assessment. Family life and life skill education package showed consistent improvement in knowledge even after a gap of 6 months. The study results showed that a school based adolescent care service programme is effective and feasible.

Body image and quality of life in a Spanish population

PurposeThe aim of the current study was to analyze the psychometric properties, factor structure, and internal consistency of the Spanish version of the Body Image Quality of Life Inventory (BIQLI-SP) as well as its test–retest reliability. Further objectives were to analyze different relationships with key dimensions of psychosocial functioning (ie, self-esteem, presence of psychopathological symptoms, eating and body image-related problems, and perceived stress) and to evaluate differences in body image quality of life due to gender.Patients and methodsThe sample comprised 417 students without any psychiatric history, recruited from the Pablo de Olavide University and the University of Seville. There were 140 men (33.57%) and 277 women (66.43%), and the mean age was 21.62 years (standard deviation = 5.12). After obtaining informed consent from all participants, the following questionnaires were administered: BIQLI, Eating Disorder Inventory-2 (EDI-2), Perceived Stress Questionnaire (PSQ), Self-Esteem Scale (SES), and Symptom Checklist-90-Revised (SCL-90-R).ResultsThe BIQLI-SP shows adequate psychometric properties, and it may be useful to determine the body image quality of life in different physical conditions. A more positive body image quality of life is associated with better self-esteem, better psychological wellbeing, and fewer eating-related dysfunctional attitudes, this being more evident among women.ConclusionThe BIQLI-SP may be useful to determine the body image quality of life in different contexts with regard to dermatology, cosmetic and reconstructive surgery, and endocrinology, among others. In these fields of study, a new trend has emerged to assess body image-related quality of life.

“Who Thinks I Need a Perfect Body?” Perceptions and Internal Dialogue among Adolescents about Their Bodies

The main aim of this study was to provide a detailed examination of the nature of the messages that adolescent boys and girls receive about their bodies. Forty adolescent boys and 40 adolescent girls participated in an in-depth interview to gain an understanding of the range of potential ‘sources’ of body-related messages. Messages were organized around the source of these messages (self, mother, father, brother, sister, female friends, male friends, media). There were consistent gender differences in the way that adolescents received and interpreted messages about their bodies. Overall girls received more positive and more negative messages than boys did. Boys reported having received virtually no negative messages from most people. The content of internal dialogue among adolescents revealed that messages about the body could be interpreted, distorted, and deflected. The implications of these findings for preventing body image-related problems and disordered eating among adolescents are discussed.

Influential sources affecting Bangkok adolescent body image perceptions

The study of body image-related problems in non-Western countries is still very limited. Thus, this study aims to identify the main influential sources and show how they affect the body image perceptions of Bangkok adolescents. The researcher recruited 400 Thai male and female adolescents in Bangkok, attending high school to freshmen level, ranging from 16-19 years, to participate in this study. Survey questionnaires were distributed to every student and follow-up interviews conducted with 40 students. The findings showed that there are eight main influential sources respectively ranked from the most influential to the least influential: magazines, television, peer group, familial, fashion trend, the opposite gender, self-realization and health knowledge. Similar to those studies conducted in Western countries, more than half of the total percentage was the influence of mass media and peer groups. Bangkok adolescents also internalized Western ideal beauty through these mass media channels. Alike studies conducted in the West, there was similarities in the process of how these influential sources affect Bangkok adolescent body image perception, with the exception of familial source. In conclusion, taking the approach of identifying the main influential sources and understanding how they affect adolescent body image perceptions can help prevent adolescents from having unhealthy views and taking risky measures toward their bodies. More studies conducted in non-Western countries are needed in order to build a cultural sensitive program, catered to the body image problems occurring in adolescents within that particular society.

Quadtree-based genetic algorithm and its applications to computer vision

Many computer vision problems can be formulated as optimization problems. Presented in this paper is a new framework based on the quadtree-based genetic algorithm that can be applied to solve many of these problems. The proposed algorithm incorporates the quadtree structure into the conventional genetic algorithm. The solutions of image-related problems are encoded through encoding the corresponding quadtrees, and therefore, the 2D locality within a solution can be preserved. Examples addressed using the proposed framework include image segmentation, stereo vision, and motion estimation. In all cases, encouraging results are obtained.

Two-Dimensional Discrete Gaussian Markov Random Field Models for Image Processing

This paper is concerned with a systematic exposition of the usefulness of two-dimensional (2-D) discrete Gaussian Markov random field (GMRF) models for image processing applications. Specifically, we discuss the following topics; notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration.

Body-image conffict following trauma and surgery

Problems of emotional conflict related to body image have become more common for two reasons: more patients are surviving mutilating injuries and diseases that would formerly have been fatal; and modern technologies make possible more drastic surgical procedures, with resulting body modifications that require personal adjustment. The author reviews his experience with hospital patients who required psychiatric help for body image-related problems and provides guidelines for therapy with such patients.