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  1. Zheng P, Belaton B, Liao IY, Rajion ZA
    PLoS One, 2017;12(11):e0187558.
    PMID: 29121077 DOI: 10.1371/journal.pone.0187558
    Landmarks, also known as feature points, are one of the important geometry primitives that describe the predominant characteristics of a surface. In this study we proposed a self-contained framework to generate landmarks on surfaces extracted from volumetric data. The framework is designed to be a three-fold pipeline structure. The pipeline comprises three phases which are surface construction, crest line extraction and landmark identification. With input as a volumetric data and output as landmarks, the pipeline takes in 3D raw data and produces a 0D geometry feature. In each phase we investigate existing methods, extend and tailor the methods to fit the pipeline design. The pipeline is designed to be functional as it is modularised to have a dedicated function in each phase. We extended the implicit surface polygonizer for surface construction in first phase, developed an alternative way to compute the gradient of maximal curvature for crest line extraction in second phase and finally we combine curvature information and K-means clustering method to identify the landmarks in the third phase. The implementations are firstly carried on a controlled environment, i.e. synthetic data, for proof of concept. Then the method is tested on a small scale data set and subsequently on huge data set. Issues and justifications are addressed accordingly for each phase.
  2. Jing C, Kuai H, Matsumoto H, Yamaguchi T, Liao IY, Wang S
    Brain Inform, 2024 Jan 08;11(1):1.
    PMID: 38190053 DOI: 10.1186/s40708-023-00216-5
    Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph Diffusion Reconstruction Network (GDRN), a novel framework designed to capture addiction-related brain connectivity from fMRI data acquired from addicted rats. The proposed GDRN incorporates a diffusion reconstruction module that effectively maintains the unity of data distribution by reconstructing the training samples, thereby enhancing the model's ability to reconstruct nicotine addiction-related brain networks. Experimental evaluations conducted on a nicotine addiction rat dataset demonstrate that the proposed GDRN effectively explores nicotine addiction-related brain connectivity. The findings suggest that the GDRN holds promise for uncovering and understanding the complex neural mechanisms underlying addiction using fMRI data.
  3. Ooi EH, Lee KW, Yap S, Khattab MA, Liao IY, Ooi ET, et al.
    Comput Biol Med, 2019 03;106:12-23.
    PMID: 30665137 DOI: 10.1016/j.compbiomed.2019.01.003
    Effects of different boundary conditions prescribed across the boundaries of radiofrequency ablation (RFA) models of liver cancer are investigated for the case where the tumour is at the liver boundary. Ground and Robin-type conditions (electrical field) and body temperature and thermal insulation (thermal field) conditions are examined. 3D models of the human liver based on publicly-available CT images of the liver are developed. An artificial tumour is placed inside the liver at the boundary. Simulations are carried out using the finite element method. The numerical results indicated that different electrical and thermal boundary conditions led to different predictions of the electrical potential, temperature and thermal coagulation distributions. Ground and body temperature conditions presented an unnatural physical conditions around the ablation site, which results in more intense Joule heating and excessive heat loss from the tissue. This led to thermal damage volumes that are smaller than the cases when the Robin type or the thermal insulation conditions are prescribed. The present study suggests that RFA simulations in the future must take into consideration the choice of the type of electrical and thermal boundary conditions to be prescribed in the case where the tumour is located near to the liver boundary.
  4. Alias A, Ibrahim A, Abu Bakar SN, Swarhib Shafie M, Das S, Abdullah N, et al.
    Clin Ter, 2018 11 6;169(5):e217-e223.
    PMID: 30393808 DOI: 10.7417/CT.2018.2082
    INTRODUCTION: The first step in the forensic identification is sex determination followed by age and stature estimation, as both are sex-dependent. The mandible is the largest, strongest and most durable bone in the face. Mandible is important for sex confirmation in absence of a complete pelvis and skull.

    AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.

    MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.

    RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.

    CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.

  5. Ooi EH, J Y Chia N, Ooi ET, Foo JJ, Liao IY, R Nair S, et al.
    Int J Hyperthermia, 2018 12;34(8):1142-1156.
    PMID: 29490513 DOI: 10.1080/02656736.2018.1437282
    A recent study by Ooi and Ooi (EH Ooi, ET Ooi, Mass transport in biological tissues: Comparisons between single- and dual-porosity models in the context of saline-infused radiofrequency ablation, Applied Mathematical Modelling, 2017, 41, 271-284) has shown that single-porosity (SP) models for describing fluid transport in biological tissues significantly underestimate the fluid penetration depth when compared to dual-porosity (DP) models. This has raised some concerns on whether the SP model, when coupled with models of radiofrequency ablation (RFA) to simulate saline-infused RFA, could lead to an underestimation of the coagulation size. This paper compares the coagulation volumes obtained following saline-infused RFA predicted based on the SP and DP models for fluid transport. Results showed that the SP model predicted coagulation zones that are consistently 0.5 to 0.9 times smaller than that of DP model. This may be explained by the low permeability value of the tissue interstitial space, which causes the majority of the saline to flow through the vasculature. The absence of fluid flow tracking in the vasculature in the SP model meant that any flow of saline into the vasculature is treated as losses and do not contribute to the saline penetration depth of the tissue. Comparisons with experimental results from the literature revealed that the DP models predicted coagulation zone sizes that are closer to the experimental values than the SP models. This supports the hypothesis that the SP model is a poor choice for simulating the outcome of saline-infused RFA.
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