scholarly journals Skull stripping using traditional and soft-computing approaches for Magnetic Resonance images: A semi-systematic meta-analysis

2020 ◽  
Vol 29 (1) ◽  
pp. 33-53
Author(s):  
Humera Azam ◽  
Humera Tariq
Keyword(s):  
Soft Computing ◽  
Meta Analysis ◽  
Research Work ◽  
Reliability And Validity ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Mr Images ◽  
Good Efficiency ◽  
Skull Stripping ◽  
Brain Mr Images

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

The Student Risk Screening Scale: A Reliability and Validity Generalization Meta-Analysis

2017 ◽  
Vol 26 (3) ◽  
pp. 143-155 ◽  
Author(s):  
Stephen P. Kilgus ◽  
Katie Eklund ◽  
Daniel M. Maggin ◽  
Crystal N. Taylor ◽  
Amanda N. Allen
Keyword(s):  
Internal Consistency ◽  
Externalizing Behavior ◽  
Meta Analysis ◽  
Reliability And Validity ◽  
Future Research ◽  
Risk Screening ◽  
Validity Generalization ◽  
Meta Analyses ◽  
Student Risk ◽  
Screening Scale

The purpose of this study was to conduct reliability and validity generalization meta-analyses of evidence regarding the Student Risk Screening Scale (SRSS), a universal screener for externalizing behavior problems. A systematic review of the literature resulted in the identification of 17 studies inclusive of evidence regarding SRSS score (a) internal consistency reliability (i.e., alpha coefficients), and/or (b) criterion-related validity (e.g., correlations between the SRSS and various outcomes). Multilevel meta-analyses indicated that across studies, SRSS scores were associated with adequate internal consistency (α = .83). Analyses further suggested the SRSS was a valid indicator of both social and behavioral outcomes ( r = .52) and academic outcomes ( r = .42). Follow-up analyses suggested that in accordance with theory-driven expectations, the SRSS was a stronger indicator of externalizing problems and broad behavior outcomes relative to alternative outcomes (e.g., internalizing problems). Limitations and directions for future research are discussed, including recommendations for the collection of additional SRSS diagnostic accuracy evidence.

Cerebral Contour Extraction with Particle Method in Neonatal MR Images

2011 ◽  
Vol 15 (3) ◽  
pp. 362-369
Author(s):  
Syoji Kobashi ◽  
◽  
Daisuke Yokomichi ◽  
Yuki Wakata ◽  
Kumiko Ando ◽  
...  
Keyword(s):  
Ground Truth ◽  
Particle Method ◽  
Magnetic Resonance Images ◽  
Mean Square ◽  
Contour Extraction ◽  
Mr Images ◽  
Basic Work ◽  
Brain Mr Images ◽  
Method Of Extraction ◽  
The Brain

Cerebral surface extraction from neonatal MR images is the basic work of quantifying the deformation of the cerebrum. Although there are many conventional methods of segmenting the cerebral region, only the rough area is given by counting the number of surface voxels in the segmented region. This article proposes a new method of extraction that is based on the particle method. The method introduces three kinds of particles that correspond to cerebrospinal fluid, gray matter, and white matter; it converts the brain MR images into the set of particles. The proposed method was applied to neonatal magnetic resonance images, and the experimental results showed that the cerebral contour was extracted with a root-mean-square-error of 0.51 mm compared with the ground truth contour given by a physician.

scholarly journals Reviews – a journey on innovation from the past into the future

2019 ◽  
Vol 6 (4) ◽  
pp. 1-14
Author(s):  
João José Pinto Ferreira ◽  
Anne-Laure Mention ◽  
Marko Torkkeli
Keyword(s):  
Selection Criteria ◽  
Meta Analysis ◽  
Reliability And Validity ◽  
Future Research ◽  
Global Studies ◽  
Causal Inferences ◽  
Validity Of Results ◽  
The Past ◽  
Treatment Conditions ◽  
Blind Spots

Reviews of literature including systematic meta-analysis are invaluable to advance science and guide directions for future research. The premise for conducting reviews is well established (Dickersin & Berlin, 1992; Glass, 1976). Systematic reviews in a field gather scholarly efforts on a topic, theme, population, setting and treatment conditions to identify peculiarities and generalizations across subsets. Reviews thus increase power and precision of causal inferences and estimates of relationships between constructs and help manage literature “blind spots” by increasing reliability and validity of results from widely dispersed regional and global studies. The advantage of reviews is thus especially noticeable in cases where occurrence rates of conditions or events are particularly low or where small effect sizes equally matter (e.g. in medical research) (Lau et al., 1992). The cumulation of diverse perspectives in a review offers nuances that cannot be found from a single study. This is mostly because each study is shaped by researcher’s cognitive capabilities and is influenced by the characteristics of research design including selection criteria for participants, research context including treatment conditions and sophistication of methods employed (Light & Pillemer, 1984). A formal meta-analysis of reviews in this view is more likely to detect small but significant effects than a single review performed by a researcher using traditional methods (Rosenthal, Cooper & Hedges, 1994). (...)

Quality 4.0 – a measurement model using the confirmatory factor analysis (CFA) approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
G.S. Sureshchandar
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Industry 4.0 ◽  
Research Work ◽  
Reliability And Validity ◽  
Measurement Model ◽  
Future Research ◽  
Content Type ◽  
Formidable Challenge ◽  
Confirmatory Factor

PurposeThe disruptions caused by new-age technologies of Industry 4.0 are posing a formidable challenge to researchers, academicians and practitioners alike. Quality 4.0 that depicts the role of the quality function in the Industry 4.0 scenario must be comprehended so that the rudiments of Quality 4.0 are understood properly, and interventions can be made to embrace the new normal. As the literature on Quality 4.0 is extremely scarce, empirical studies are mandatory to augment the process of theory building.Design/methodology/approachThe research work identifies 12 axes of the Quality 4.0 revolution based on literature review and insights from experts. Subsequently, a measurement model is formulated and an instrument to measure the level of Quality 4.0 implementation is developed. The measurement model has been checked for model fit, reliability and validity using the confirmatory factor analysis approach.FindingsThe proposed model was found to be adequate, reliable and valid and concludes that though technology plays a significant role in the development of the Quality 4.0 system, aspects of traditional quality are very much apropos to transform to the next frontier of quality.Research limitations/implicationsImplications for future research are provided which would help to further explore the nascent field of Quality 4.0.Practical implicationsThis research would help the practitioners better understand the various requirements and measure the degree of implementation of a Quality 4.0 system.Originality/valueThe present research is perhaps the first of its kind in propounding a measurement model, through empirical analysis, for the betterment of the understanding of Quality 4.0 and its associated constituents.

scholarly journals Stable-2007 and indeterminate detention

2021 ◽  
Vol 16 ◽  
Author(s):  
Andrew J. R. Harris
Keyword(s):  
Sexual Offending ◽  
Meta Analysis ◽  
Dynamic Assessment ◽  
Reliability And Validity ◽  
Future Research ◽  
Dynamic Risk Factors ◽  
Dynamic Risk ◽  
Dynamic Risk Assessment ◽  
History Of ◽  
Implicit Assessment

Public safety is the primary reason to assess future risk in men with a history of sexual offending. Over the last twenty-five years our knowledge of, and ability to assess, dynamic risk factors in men with a history of sexual offending has meaningfully improved, but understanding, adoption, utilization, and reasonable implementation of the fruits of this new knowledge is not universal. This article presents a brief overview of the development of dynamic risk assessment for men with a history of sexual offending, primarily following the work of R. Karl Hanson and his associates. This is followed by a review of a meta-analysis on the reliability and validity of STABLE-2007 and two other independent studies that provide useful ancillary information. Utilizing STABLE-2007 with men faced with, or under sanction of indeterminate detention is the focus of this paper and we will review how mental health diagnoses affect recidivism assessment, some concerns about implicit assessment biases, how to employ stable dynamic assessment in secure facilities, address treatment implications resulting from dynamic assessment, and present ideas for future research. I will close by presenting nine (9) arguments why using STABLE-2007 is recommended practice with indeterminate detention populations.

Making the Invisible Visible: Guidelines for the Coding Process in Meta-Analyses

2021 ◽  
pp. 109442812110463
Author(s):  
Jessica Villiger ◽  
Simone A. Schweiger ◽  
Artur Baldauf
Keyword(s):  
Decision Making ◽  
Meta Analysis ◽  
Reliability And Validity ◽  
Quality Standards ◽  
Research Community ◽  
Future Research ◽  
Research Communities ◽  
Meta Analyses ◽  
The Invisible

This article contributes to the practice of coding in meta-analyses by offering direction and advice for experienced and novice meta-analysts on the “how” of coding. The coding process, the invisible architecture of any meta-analysis, has received comparably little attention in methodological resources, leaving the research community with insufficient guidance on “how” it should be rigorously planned (i.e., cohere with the research objective), conducted (i.e., make reliable and valid coding decisions), and reported (i.e., in a sufficiently transparent manner for readers to comprehend the authors’ decision-making). A lack of rigor in these areas can lead to erroneous results, which is problematic for entire research communities who build their future knowledge upon meta-analyses. Along four steps, the guidelines presented here elucidate “how” the coding process can be performed in a coherent, efficient, and credible manner that enables connectivity with future research, thereby enhancing the reliability and validity of meta-analytic findings. Our recommendations also support editors and reviewers in advising authors on how to improve the rigor of their coding and ultimately establish higher quality standards in meta-analytic research.

Prevalence of musculoskeletal chest pain in the emergency department: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shivani Mandrekar ◽  
Prem Venkatesan ◽  
Ravishankar Nagaraja
Keyword(s):  
Emergency Department ◽  
Chest Pain ◽  
Urban Areas ◽  
Meta Analysis ◽  
Research Work ◽  
Cochrane Library ◽  
Future Research ◽  
Reliable Estimate ◽  
European Continent ◽  
Pooled Prevalence

Abstract Objectives Our objective was to systematically review and meta-analyse relevant studies to determine the prevalence of musculoskeletal chest pain in the emergency department. Methods This review was constructed while confirming to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. PubMed, Cochrane Library, SCOPUS, Science Direct, and OVID were systematically searched from their inception to January 19, 2020, to identify observational studies, where the prevalence of musculoskeletal causes of chest pain was reported in isolation or in combination with other causes or could be calculated from the available data. Results A meta-analysis of the nine included studies, having a total of 14,743 participants, showed the global pooled prevalence of musculoskeletal chest pain in the emergency department to be 16% (10–22%) [I 2=99.24%]. The pooled prevalence for the European continent was 17% (9–25%) [I 2=99.51%] and that for the urban areas was 13% (7–19%) [I 2=99.00%]. Conclusions This review provides a reliable estimate of the prevalence of musculoskeletal chest pain in the emergency department. More studies providing age and gender-specific data for the prevalence of musculoskeletal chest pain in the emergency department should be carried out. A paucity of such data from rural areas also needs to be addressed in future research work. The prevalence values from this study will be useful in the application of Bayesian reasoning utilised in diagnosing patients, where the process of Bayesian arguing begins by knowing pre-test probabilities of different differential diagnosis, in this case that of musculoskeletal chest pain in the emergency department.

scholarly journals A Professional Estimate on the Segmentation of Brain Cancer in MR Images using M-FCM

2020 ◽  
Vol 9 (1) ◽  
pp. 2425-2430
Keyword(s):  
Brain Cancer ◽  
Computer Software ◽  
Ground Truth ◽  
Magnetic Resonance Images ◽  
Morphological Operations ◽  
Mr Images ◽  
Segmentation Accuracy ◽  
Software Code ◽  
Brain Mr Images ◽  
The Brain

Brain imaging innovations have been forever made for a significant part in analyzing and focusing the unused sees of the brain life systems and functions. A computer software code is designed for the detection of cancer in brain magnetic resonance images. Image segmentation, morphological operations and feature extraction are some of the image processing methods developed for the brain cancer detection in MR images concerning the cancer influenced sufferers. In the proposed research, a Modified morphological-based Fuzzy-C-Means (MFCM) algorithm is proposed to segment the cancer region in the brain MR images. M-FCM algorithm is used to perform the segmentation process significantly through the idealize choice of a cluster, based on the updated membership function. Quantitative analysis between ground truth and segmented cancer is presented in terms of segmentation accuracy and segmentation sensitivity

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