Browsing by Author "Tsang, Siny"

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A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
(2012-01-01)
Chen, You-Yin
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Cho, Chien-Wen
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Lin, Sheng-Huang
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Lai, Hsin-Yi
;
Lo, Yu-Chun
Parkinson's Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality. (C) 2011 Elsevier Ltd. All rights reserved.
Automatic segmentation of magnetic resonance images using a decision tree with spatial information
(2009-03-01)
Chao, Wen-Hung
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Chen, You-Yin
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Lin, Sheng-Huang
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Shih, Yen-Yu I.
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Tsang, Siny
Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data - phantom MR images obtained from IBSR (http://www.cma.mgh.harvard.edu/ibsr) and simulated brain MR images obtained from BrainWeb (http://www.bic.mni.mcgill.ca/brainweb) - were segmented using an automatic decision tree algorithm to obtain images with improved visual rendition. Spatial information on the general gray level (G), spatial gray level (S), and two-dimensional wavelet transform (M was combined in-plane in two coordinate systems (Euclidean coordinates (x,y) or polar coordinates (r,theta)). The decision tree was constructed based on a binary tree with nodes created by splitting the distribution of input features of the tree. The spatial information obtained from MR images with different noise levels and inhomogeneities were segmented to compare whether the use of a decision tree improved the identification of human anatomical structures in a neuroimage. The average accuracy rates of segmentation for phantom images with a noise variation of 15 gray levels were 0.9999 and 0.9973 with spatial information (G, x,y, r, theta) and (S, x, y, r, theta), respectively, and 0.9999 and 0.9819 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The average accuracy rates of segmentation for simulated MR images with a noise level of 5% were 0.9532 and 0.9439 with spatial information (G, x, y, r, theta) and (S, x, y, r, theta), respectively, and 0.9446 and 0.9287 with spatial information (G, x, y, S, r, theta) and (W, x, y, G, r, theta). The accuracy rates of segmentation were highest for both simulated phantom and brain MR images, having the lowest noise levels, from a reduction of overlapping gray levels in the images. The accuracies of segmentation were higher when the spatial information included the general gray level than when it included the spatial gray level, which in turn were higher than when it included the wavelet transform. Furthermore, the performance of segmentation was also evaluated with a boundary detection methodology that is based on the Hausdorff distance to compare with the mean computer to observer difference (COD) and mean interobserver difference (IOD) for gray matter (GM), white matter (WM), and all areas (ALL) from images segmented using the decision tree. The values of mean COD are similar and around 12 mm for GM segmented using the decision tree. Our segmentation method based on a decision tree algorithm presented an easy way to perform automatic segmentation for both phantom and tissue regions in brain MR images. (C) 2008 Published by Elsevier Ltd.
Automatic spike sorting for extracellular electrophysiological recording using unsupervised single linkage clustering based on grey relational analysis
(2011-06-01)
Lai, Hsin-Yi
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Chen, You-Yin
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Lin, Sheng-Huang
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Lo, Yu-Chun
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Tsang, Siny
Automatic spike sorting is a prerequisite for neuroscience research on multichannel extracellular recordings of neuronal activity. A novel spike sorting framework, combining efficient feature extraction and an unsupervised clustering method, is described here. Wavelet transform (WT) is adopted to extract features from each detected spike, and the Kolmogorov-Smirnov test (KS test) is utilized to select discriminative wavelet coefficients from the extracted features. Next, an unsupervised single linkage clustering method based on grey relational analysis (GSLC) is applied for spike clustering. The GSLC uses the grey relational grade as the similarity measure, instead of the Euclidean distance for distance calculation; the number of clusters is automatically determined by the elbow criterion in the threshold-cumulative distribution. Four simulated data sets with four noise levels and electrophysiological data recorded from the subthalamic nucleus of eight patients with Parkinson's disease during deep brain stimulation surgery are used to evaluate the performance of GSLC. Feature extraction results from the use of WT with the KS test indicate a reduced number of feature coefficients, as well as good noise rejection, despite similar spike waveforms. Accordingly, the use of GSLC for spike sorting achieves high classification accuracy in all simulated data sets. Moreover, J-measure results in the electrophysiological data indicating that the quality of spike sorting is adequate with the use of GSLC.
Correction of inhomogeneous magnetic resonance images using multiscale retinex for segmentation accuracy improvement
(2012-03-01)
Chao, Wen-Hung
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Lai, Hsin-Yi
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Shih, Yen-Yu I.
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Chen, You-Yin
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Lo, Yu-Chun
The purpose of this study was to improve the accuracy of tissue segmentation on brain magnetic resonance (MR) images preprocessed by multiscale retinex (MSR), segmented with a combined boosted decision tree (BDT) and MSR algorithm (hereinafter referred to as the MSRBDT algorithm). Simulated brain MR (SBMR) T1-weighted images of different noise levels and RF inhomogeneities were adopted to evaluate the outcome of the proposed method; the MSRBDT algorithm was used to identify the gray matter (GM), white matter (WM), and cerebral-spinal fluid (CSF) in the brain tissues. The accuracy rates of GM, WM, and CSF segmentation, with spatial features (G, x, y, r, theta), were respectively greater than 0.9805, 0.9817, and 0.9871. In addition, images segmented with the MSRBDT algorithm were better than those obtained with the expectation maximization (EM) algorithm; brain tissue segmentation in MR images was significantly more precise. The proposed MSRBDT algorithm could be beneficial in clinical image segmentation. (C) 2011 Elsevier Ltd. All rights reserved.
Design and fabrication of a polyimide-based microelectrode array: Application in neural recording and repeatable electrolytic lesion in rat brain
(2009-08-30)
Chen, You-Yin
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Lai, Hsin-Yi
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Lin, Sheng-Huang
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Cho, Chien-Wen
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Chao, Wen-Hung
The design and testing of a new microelectrode array, the NCTU (National Chiao Tung University) probe, was presented. Evaluation results showed it has good biocompatibility, high signal-to-noise ratio (SNR: the root mean square of background noise to the average peak-to-peak amplitude of spikes) during chronic neural recordings, and high reusability for electrolytic lesions. The probe was a flexible, polyimide-based microelectrode array with a long shaft (14.9 mm in length) and 16 electrodes (5 mu m-thick and 16 mu m in radius); its performance in chronic in vivo recordings was examined in rodents. To improve the precision of implantation, a metallic, impact-resistant layer was sandwiched between the polyimide layers to strengthen the probe. The three-dimensional (3D) structure of electrodes fabricated by electroplating produced rough textures that increased the effective surface area. The in vitro impedance of electrodes on the NCTU probe was 2.4 +/- 0.52 M Omega at 1 kHz. In addition, post-surgical neural recordings of implanted NCTU probes were conducted for up to 40 days in awake, normally behaving rats. The electrodes on the NCTU probe functioned well and had a high SNR (range: 4-5) with reliable in vivo impedance (<0.7 M Omega). The electrodes were also robust enough to functionally record events, even after the anodal current (30 mu A, 10 s) was repeatedly applied for 60 times. With good biocompatibility, high and stable SNR for chronic recording, and high tolerance for electrolytic lesion, the NCTU probe would serve as a useful device in future neuroscience research. (C) 2009 Elsevier B.V. All rights reserved.
Design, simulation and experimental validation of a novel flexible neural probe for deep brain stimulation and multichannel recording
(2012-06-01)
Lai, Hsin-Yi
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Liao, Lun-De
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Lin, Chin-Teng
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Hsu, Jui-Hsiang
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He, Xin
An implantable micromachined neural probe with multichannel electrode arrays for both neural signal recording and electrical stimulation was designed, simulated and experimentally validated for deep brain stimulation (DBS) applications. The developed probe has a rough three-dimensional microstructure on the electrode surface to maximize the electrode-tissue contact area. The flexible, polyimide-based microelectrode arrays were each composed of a long shaft (14.9 mm in length) and 16 electrodes (5 mu m thick and with a diameter of 16 mu m). The ability of these arrays to record and stimulate specific areas in a rat brain was evaluated. Moreover, we have developed a finite element model (FEM) applied to an electric field to evaluate the volume of tissue activated (VTA) by DBS as a function of the stimulation parameters. The signal-to-noise ratio ranged from 4.4 to 5 over a 50 day recording period, indicating that the laboratory-designed neural probe is reliable and may be used successfully for long-term recordings. The somatosensory evoked potential (SSEP) obtained by thalamic stimulations and in vivo electrode-electrolyte interface impedance measurements was stable for 50 days and demonstrated that the neural probe is feasible for long-term stimulation. A strongly linear (positive correlation) relationship was observed among the simulated VTA, the absolute value of the SSEP during the 200 ms post-stimulus period (Sigma SSEP) and c-Fos expression, indicating that the simulated VTA has perfect sensitivity to predict the evoked responses (c-Fos expression). This laboratory-designed neural probe and its FEM simulation represent a simple, functionally effective technique for studying DBS and neural recordings in animal models.
Imaging brain hemodynamic changes during rat forepaw electrical stimulation using functional photoacoustic microscopy
(2010-08-15)
Liao, Lun-De
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Li, Meng-Lin
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Lai, Hsin-Yi
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Shih, Yen-Yu I.
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Lo, Yu-Chun
The present study reported the development of a novel functional photoacoustic microscopy (fPAM) system for investigating hemodynamic changes in rat cortical vessels associated with electrical forepaw stimulation. Imaging of blood optical absorption by fPAM at multiple appropriately-selected and distinct wavelengths can be used to probe changes in total hemoglobin concentration (HbT, i.e., cerebral blood volume [CBV]) and hemoglobin oxygen saturation (SO(2)). Changes in CBV were measured by images acquired at a wavelength of 570 nm (lambda(570)), an isosbestic point of the molar extinction spectra of oxy- and deoxy-hemoglobin, whereas SO(2) changes were sensed by pixel-wise normalization of images acquired at lambda(560) or lambda(600) to those at lambda(570). We demonstrated the capacity of the fPAM system to image and quantify significant contralateral changes in both SO(2) and CBV driven by electrical forepaw stimulation. The fPAM system complements existing imaging techniques, with the potential to serve as a favorable tool for explicitly studying brain hemodynamics in animal models. (C) 2010 Elsevier Inc. All rights reserved.
Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree
(2008-11-15)
Chao, Wen-Hung
;
Chen, You-Yin
;
Cho, Chien-Wen
;
Lin, Sheng-Huang
;
Shih, Yen-Yu I.
The purpose of this study was to improve the accuracy rate of brain tissue classification in magnetic resonance (MR) imaging using a boosted decision tree segmentation algorithm. Herein. we examined simulated phantom MR (SPMR) images, simulated brain MR (SBMR) images, and a real data. The accuracy rate and k index when classifying brain tissues as gray matter (GM), white matter (WM), or cerebral-spinal fluid (CSF) were better when using the boosted decision tree algorithm combined with a fuzzy threshold than when using a statistical region-growing (SRG) algorithm [Wolf 1, Vetter M, Wegner 1, Bottger T, Nolden M, Schobinger M, et al. The medical imaging interaction toolkit. Med Imag Anal 2005;9:594-604] and an adaptive segmentation (AS) algorithm [Wells WM, Crimson WEL, Kikinis R, Jolesz FA. Adaptive segmentation of MRI data. IEEE Trans Med Imag 1996; 15:429-42]. The segmentation performance when using this algorithm on real data from brain MR images was also better than those of SRG and AS algorithm. Segmentation of a real data using the boosted decision tree produced particularly clear brain MR imaging and permitted more accurate brain tissue segmentation. In conclusion, a decision tree with appropriate boost trials successfully improved the accuracy rate of MR brain tissue segmentation. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
MicroPET imaging of noxious thermal stimuli in the conscious rat brain
(2010)
Chen, You-Yin
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Shih, Yen-Yu I.
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Lo, Yu-Chun
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Lu, Pen-Li
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Tsang, Siny
Small animal positron emission tomography (microPET) has been utilized in the investigation of nociception. However, a possible drawback from previous studies is the reduced activation pattern due to the application of anesthesia. The purpose of the present study was to demonstrate a potential means of avoiding anesthesia during stimulation, as well as minimizing the confounding anesthetic effect. Sodium pentobarbital and ketamine were first evaluated to determine their effect on microPET images in the current study. [(18)F]-Fluorodeoxyglucose ((18)F-FDG) was an appropriate radiotracer to reveal activated regions in rat brains. Pentobarbital anesthesia significantly reduced (18)F-FDG uptake in neural tissues, blurrier to lower contrast; therefore, ketamine was used to anesthetize animals during microPET. After the rats were anesthetized and secured in a laboratory-made stereotaxic frame, a simple, noninvasive stereotaxic technique was used to position their heads in the microPET scanner and to roughly conform the images in the stereotaxic atlas. For functional imaging, conscious rats were restrained in cages with minimal ambient noise; short repetitive thermal stimuli were applied to each rat's tail subsequently. The rats were adequately anesthetized with ketamine following 30 min of scanning without stimulation. An activation index (AI) was calculated from microPET data to quantify the local metabolic activity changes according to the normalized (18)F-FDG dosage. The average AI indicated a side-to-side difference for all innocuous stimulations in the thalamus. However, such side-to-side difference was only observed for noxious heat and cold stimulations in primary somatosensory cortex (SI), secondary somatosensory cortex (SII), and agranular insular cortex (AIC). The present study demonstrated the feasibility of the microPET technique to image metabolic functions of the conscious rat brain, offering better rationale and protocol designs for future pain studies.
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
(2011-11-10)
Chen, Shih-Wei
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Lin, Sheng-Huang
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Liao, Lun-De
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Lai, Hsin-Yi
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Pei, Yu-Cheng
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.
Transcranial imaging of functional cerebral hemodynamic changes in single blood vessels using in vivo photoacoustic microscopy
(2012-06-01)
Liao, Lun-De
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Lin, Chin-Teng
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Shih, Yen-Yu I.
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Duong, Timothy Q.
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Lai, Hsin-Yi
Optical imaging of changes in total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO2) provides a means to investigate brain hemodynamic regulation. However, high-resolution transcranial imaging remains challenging. In this study, we applied a novel functional photoacoustic microscopy technique to probe the responses of single cortical vessels to left forepaw electrical stimulation in mice with intact skulls. Functional changes in HbT, CBV, and SO2 in the superior sagittal sinus and different-sized arterioles from the anterior cerebral artery system were bilaterally imaged with unambiguous 36x65-mu m(2) spatial resolution. In addition, an early decrease of SO2 in single blood vessels during activation (i.e., 'the initial dip') was observed. Our results indicate that the initial dip occurred specifically in small arterioles of activated regions but not in large veins. This technique complements other existing imaging approaches for the investigation of the hemodynamic responses in single cerebral blood vessels. Journal of Cerebral Blood Flow & Metabolism (2012) 32, 938-951; doi:10.1038/jcbfm.2012.42; published online 4 April 2012