Automated Feature Description of Renal Size Using Image Processing

Ultrasonography (US) is one of the procedures to monitor the growth of renal size in diagnose kidney disease. However considering the complexity of renal size, this procedure leads to inter-observer variability and poor repeatability. Given images from Abdominal CT scan, a level set thresholding and combination of logical and arithmetic operation based method was developed to calculate the automated feature description of renal size. This is achieved by applying 2D CT scan image into image segmentation and feature extraction where thresholding and morphological segmentation method are conducted. Then, parameters of the kidney such as perimeter, area, major axis and minor axis were measured and analyzed in classification step. As a result, analysis on the kidney size between subjects who are normal and the results from the studies has shown capability to classify correctly the size of kidneys about accuracy of 80% to 81% in terms of the kidney’s relative axis which is the ratio of right kidney and left kidneys. In addition, the method in measurement kidney size is compared between manual method and automated method and results shows that the accuracy of the automated method in terms of compactness is about 91% to 95

On bloodvessel branching analysis for the detection of Alzheimer's disease

Alzheimer’s Disease (AD) is increasingly prevalent in modern society and methods for its diagnosis are only just starting to emerge. Given images of brain tissue, we show how Alzheimer’s disease can be detected from the branching structures of blood vessels. This is achieved by a new approach which counts the branching points and derives measures which are suited to the analysis of small branching structures. The measures are formulated to be rotation, scale and position invariant and are deployed in tandem with more standard measures. Analysis on a database comprised of brain tissue samples from subjects who are normal, with Alzheimer’s and age matched normal has shown capability to classify correctly images of brain tissue from subjects afflicted with Alzheimer’s disease.<br/

THE EFFECT OF MOTIVATION ON ARABIC COLLOCATION KNOWLEDGE: THE MEDIATING ROLE OF COLLOCATION LEARNING STRATEGIES

Background and Purpose: The role of collocation learning strategies is less of a concern as mediator. Although several correlation studies of bivariate factors have provided the relationship between the variables, many cannot answer the question of how the relationship exists. Also, a lot of studies have taken into account the variables of collocation learning strategies as mediator factors and have not illustrated clearly the relationship between independent variables (motivation) and dependent variables (collocation knowledge). As such, the aim of the present study is to identify the knowledge of Arabic collocation by taking several factors, namely; motivation and collocation learning strategies that have the potential to increase the knowledge of Arabic collocation in Malaysia.&nbsp; &nbsp; Methodology: In this study, a cross-sectional design was applied. Simple random sampling was used, where a total of 344 final year Arabic language students from eight public universities in Malaysia took part in the study by completing a set of tests and questionnaires. The data were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. &nbsp; Findings: The findings show there is a direct and indirect relationship between motivation variable, collocation learning strategies and collocation knowledge. In addition, the learning strategies factor also serves as a partial mediator. &nbsp; Contributions: This study suggests that collocation learning strategies play a role in the relationship between motivation and collocation knowledge in the context of Arabic language at higher education level in Malaysia. &nbsp; Keywords: Collocation learning strategies, motivation, collocation knowledge, Arabic language, public universities. &nbsp; Cite as: Asbulah, L. H., Aladdin, A., &amp; Sahrim, M. (2020).&nbsp;The effect of motivation on Arabic collocation knowledge: The mediating role of collocation learning strategies.&nbsp;&nbsp;Journal of Nusantara Studies, 5(2), 1-18. http://dx.doi.org/10.24200/jonus.vol5iss2pp1-1

Blood vessel feature description for detection of Alzheimers disease

We describe how image analysis can be used to detect the presence of Alzheimer’s disease. The data are images of brain tissue collected from subjects with and without Alzheimer’s disease. The analysis concentrates on the shape and structure of the blood vessels which are known to be affected by amyloid beta, whose drainage is affected by Alzheimer’s disease. The structure is analysed by a new approach which measures the influence of the blood vessels’ branching structures. Their density and tortuosity are analysed in conjunction with a boundary description derived using Fourier descriptors. These measures form a feature vector which is derived from the images of brain tissue, and the discrimination capability shows that it is possible to detect the presence of Alzheimer’s disease using these measures and in an automated way. These measures also show that shape information is influenced by the vessels’ branchingstructure, as known to be consistent with Alzheimer’s disease evolution

Blood vessel shape description for detection of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common form of dementia and is characterised by the deposition of aggregated proteins in neurofibrillary tangles or amyloid plaques within the vascular structure of the brain. Amyloid plaques consist of amyloid-beta (A?) in the extracellular spaces of the brain or in the walls of blood vessels, reflecting a failure to eliminate A? from the ageing brain. The failure to remove A? is potentially reflected in the vessels’ shape: vessel shape can improve or reduce fluid flow and thus drainage, according to tortuosity and other shape factors.Neuropathological studies on post-mortem human tissue have described that the small vessels of aged brains are more tortuous compared to Young brains and tortuosity increases with the presence of Alzheimer pathology[1-4]. There is currently much interest in the diagnosis of AD, especially at the early stages where therapy could be better directed (or even deployed). The central aim of this thesis is to determine whether diagnosis is possible from image data, of brain tissue and MRI scans of the brain. We propose that the capillaries can be analysed as a branching structure, which appears to be a new analysis for medical images. The approach includes new measurements of the branching structure which are enriched by analysis of the vessels’ tortuosity and density. The introduction of measures of shape by compactness and Fourier descriptors further enriches this study.The branching structures are detected by evidence gathering approaches and described by their structure. This allows recognition to be achieved; the structure of those samples derived from patients with AD differs from that for normal subjects. The descriptions can be classified using machine learning techniques, as such, achieving an automated process from image to recognition. We analysed the structure of the blood vessels in a database of brain tissue images collected from control, age-matched and patients with severe AD. The database comprises five subjects of each of the three types imaged in controlled conditions, and five MRI images of a normal brain from the Brain Tissue Resource of Newcastle UK. We show that by automated image analysis we are able to discriminate between brain tissue samples from patients presenting AD and from the normal samples.We also show discriminative capability between posterior and anterior regions of the brain imaged in 3D by MRI. The branching structure is the description that is most suited for classification purposes. On this initial dataset, statistically significant differences (p=0.04) were seen between anterior and posterior and we can achieve 90% correct classification from a combination of these descriptions.We are thus confident that these approaches are well suited to further investigation aiming for a diagnostic tool for clinical use in the assessment of possibility of Alzheimer’s disease

Analysing morphological patterns of blood vessels for detection of Alzheimer's disease

The physiological consequences of Alzheimer's disease (AD) concern the development of amyloid plaques and neurofibrillary tangles. Development of amyloid plaques in the brain is caused by Amyloid Beta that forms part of an amyloid precursor protein. In a normal brain, these protein fragments are broken down and eliminated but with AD, these fragments accumulate to form hard insoluble plaques. Our techniques are based on the image analysis of brain tissue and study the branching structures of the blood vessels (which is novel itself), on the analysis of tortuosity and density. These are known to have links with the onset of AD. The branching structures are detected by evidence gathering approaches and described by their structure. This allows recognition to be achieved: the structure of those samples derived from patients with AD differs from that for normal subjects. This also occurs for the tortuosity and to a lesser extent the density. The descriptions can be classified using machine learning techniques, as such achieving an automated process from image to recognition. We analyse the structure of the blood vessels in a database of images collected from control, age-matched and patients with severe AD. The database comprises five subjects of each of the three types, imaged in controlled conditions and from the Brain Tissue Resource of Newcastle UK. We show that by automated image analysis we now appear able to discriminate between brain tissue samples from patients presenting AD and from the normal samples. The branching structure is the description that is most suited to classification purposes. On this initial dataset we can achieve 100% correct classification from a combination of these descriptions and around 90% correct classification from the branches and their paths. We are thus confident in the correct referral of patients for further investigation when this new technique is translated for clinical us

Mechanical reinforcement with enhanced electrical and heat conduction of epoxy resin by polyaniline and graphene nanoplatelets

In this study, the effects of polyaniline (PANI) incorporation (3 wt% of PANI) and graphene nanoplatelets (GNPs) loading (0.1–0.7 wt%) on the mechanical, thermal, and electrical performance of epoxy matrix were investigated. The incorporation of 0.3 wt% GNPs optimally enhanced the bending strength, bending modulus, tensile strength, tensile modulus, and impact strength (90 MPa, 1422 MPa, 63 MPa, 602 MPa, and 8.29 kJm−2, respectively). At 0.3 wt% GNPs, the hybridization effect optimally enhanced the glass transition behaviour of the epoxy nanocomposites. The electrical and thermal conductivities of epoxy were improved upon the inclusion of PANI, and this increase was further augmented when the GNPs content increased to 0.3 wt%. However, higher GNPs contents deteriorated the mechanical performance and electrical and heat conduction. Field emission scanning electron microscopy showed good filler distribution and effective interactions among the GNPs, PANI, and epoxy components with appropriate compositions

Optical tomography system using charge-coupled device for transparent object detection

This research presents an application of Charge-Coupled Device (CCD) linear sensor and laser diode in an optical tomography system. Optical tomography is a non-invasive and non-intrusive method of capturing a cross-sectional image of multiphase flow. The measurements are based on the final light intensity received by the sensor and this approach is limited to detect solid objects only. The aim of this research is to analyse and demonstrate the capability of laser with a CCD in an optical tomography system for detecting objects with different clarity in crystal clear water. Experiments for detecting transparent objects were conducted. The object’s diameter and image reconstruction can also be observed. As a conclusion, this research has successfully developed a non-intrusive and non-invasive optical tomography system that can detect objects in crystal clear water