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3D Rendering of Magnetic Resonance Images using Visualization Toolkit and Microsoft.NET Framework
Madusanka, Nuwan,Zaben, Naim Al,Shidaifat, Alaaddin Al,Choi, Heung-Kook Korea Multimedia Society 2015 The journal of multimedia information system Vol.2 No.2
In this paper, we proposed new software for 3D rendering of MR images in the medical domain using C# wrapper of Visualization Toolkit (VTK) and Microsoft .NET framework. Our objective in developing this software was to provide medical image segmentation, 3D rendering and visualization of hippocampus for diagnosis of Alzheimer disease patients using DICOM Images. Such three dimensional visualization can play an important role in the diagnosis of Alzheimer disease. Segmented images can be used to reconstruct the 3D volume of the hippocampus, and it can be used for the feature extraction, measure the surface area and volume of hippocampus to assist the diagnosis process. This software has been designed with interactive user interfaces and graphic kernels based on Microsoft.NET framework to get benefited from C# programming techniques, in particular to design pattern and rapid application development nature, a preliminary interactive window is functioning by invoking C#, and the kernel of VTK is simultaneously embedded in to the window, where the graphics resources are then allocated. Representation of visualization is through an interactive window so that the data could be rendered according to user's preference.
Madusanka, Nuwan,Choi, Yu Yong,Choi, Kyu Yeong,Lee, Kun Ho,Choi, Heung-Kook Korea Multimedia Society 2017 멀티미디어학회논문지 Vol.20 No.2
The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.
Tisa Selma,Nuwan Madusanka,Tae-Hyung Kim,Young-Hoon Kim,Chi-Woong Mun,Heung-Kook Choi 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.8
Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.
A Comparative Study of Alzheimer’s Disease Classification using Multiple Transfer Learning Models
Deekshitha Prakash,Nuwan Madusanka,Subrata Bhattacharjee,Hyeon-Gyun Park,Cho-Hee Kim,최흥국 한국멀티미디어학회 2019 The journal of multimedia information system Vol.6 No.4
Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer’s Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.
Tisa Selma,Nuwan Madusanka,김태형,김영훈,문치웅,최흥국 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.8
Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.
Selma, Tisa,Madusanka, Nuwan,Kim, Tae-Hyung,Kim, Young-Hoon,Mun, Chi-Woong,Choi, Heung-Kook Korea Multimedia Society 2016 멀티미디어학회논문지 Vol.19 No.8
Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.
뇌 해마의 관상면 중심점으로부터거리분석에 따른 치매분류
최부경,소재홍,손영주,Nuwan Madusanka,최흥국 한국멀티미디어학회 2018 멀티미디어학회논문지 Vol.21 No.2
Alzheimer's disease has the significant factors for the both specific and characteristic features according to the disease progressing that are the volumetry and surface area by the brain hippocampus shrinking and thinning. However, we have suggested a shape analysis to calculate the variance by the roughness, coarseness or uneven surface on 3D MR images. For the reasons we have presented two methods: the first method is the distance calculation from major axis to edge points and the second method is the distance calculation from centroidal point to edge points on a coronal plane. Then we selected the shortest distance and the longest distance in each slice and analyzed the ANOVA and average distances. Consequently we obtained the available and great results by the longest distance of the axial and centroidal point. The results of average distances were 44.85(AD), 45.04(MCI) and 49.31(NC) from the axial points and 39.30(AD), 39.58(MCI) and 44.78(NC) from centroidal points respectively. Finally the distance variations for the easily recognized visualization were shown by the color mapping. This research could be provided an indicator of biomarkers that make diagnosis and prognosis the Alzheimer's diseases in the future.