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Optogenetics-based Neuromodulation for the Alleviation of hemi-Parkinsonian Motor Asymmetry in Rat Optogenetics-based Neuromodulation for the Alleviation of hemi-Parkinsonian Motor Asymmetry in Rat $15.00

Zeyang Yu, Wael F. Asaad, Arto V. Nurmikko and Ilker Ozden

University of Missouri, Columbia, USA

Vol.3, Issue 3

134-141

29-12-2017

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Abstract: The development of more effective deep brain stimulation (DBS) paradigms for Parkinson’s disease is limited by the non-specific nature of electrical stimulation. Optogenetics, with its spatial and cell-type specificity, is a potential alternative therapeutic approach. In 6-hydroxydopamine-induced hemi-Parkinsonian rats, we investigated the therapeutic values of optogenetic modulation of the subthalamic nucleus (STN) and the motor cortex. Here we report optogenetic inhibition of principal neurons in the STN significantly improved hemi-Parkinsonian motor asymmetry, measured by amphetamine-induced rotations. We also show preliminary results that revealed therapeutic improvement in motor asymmetry by single-site optogenetic excitation of the motor cortex. Although improvement from optogenetic modulations did not exceed the effects of DBS in the STN, our findings suggest that spatially patterned optogenetic stimulation of the cortex, i.e., more precise manipulation of cortical activity over larger area, should be investigated as a therapeutic approach for Parkinson's disease.
The Role of Neuroimaging in Diagnosis of Neurodegenerative Disease The Role of Neuroimaging in Diagnosis of Neurodegenerative Disease $15.00

Elham Pahlevani

Shahrekord University of Medical Sciences, Iran

Vol.3 , Issue 3

125-133

29-12-2017

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Abstract: Imaging the human body is one of the most important aspects of medical science in clinics and research. Due to the increasing spread of diseases related to the nervous system, neuroimaging has grown substantially in the last two decades. In this study, neuroimaging techniques that are used to diagnose neurodegenerative diseases have been expressed. Clinical applications of each neuroimaging method have also been reviewed. Some imaging techniques create structural and anatomical images, and some provide physiological and functional images. Recent advances in neuroimaging have led to the creation of hybrid techniques. In these multimodality methods, structural and functional images are combined. This feature leads to increased accuracy in the diagnosis of neurodegenerative diseases.
Controlling 3D Object made of CT data in Medical Training System Using Leap Motion Controlling 3D Object made of CT data in Medical Training System Using Leap Motion $15.00

Mohamed Atef Seif, Ryosuke Umeda and Hiroki Higa

Ryukyu University, Japan

Vol.3, Issue 3

120-124

29-12-2017

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Abstract: This paper describes a user interface of 3D (three-dimensional) object converted from 2D (two-dimensional) CT data in DICOM format using Leap Motion device that can be used as a medical training system for medical students and interns. The resultant data can be controlled in a 3D development environment of Unity software. The system consists of desktop computer, the displaying software environment and Leap Motion device. The experimental results show that we can have a desirable control over the rendered object in 360 degrees, and that we can check the details of the object using zooming feature in the system.
Multi-label classification of brain tumor mass spectrometry data. In pursuit of tumor boundary detection method. Multi-label classification of brain tumor mass spectrometry data. In pursuit of tumor boundary detection method. $15.00

Anatoly Sorokin, Vsevolod Shurkhay, Evgeny Zhvansky, Konstantin Bocharov, Igor Popov, Dmitry Zubtsov, Alexander Vorobiev, Alexander Potapov and Eugene Nikolaev

Vol. 3, Issue 3

117-119

29-12-2017

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Abstract: The mass-spectrometry is the promising tool for the fast characterization of brain biopsy samples as a part of the intraoperative identification of tumor boundary. The spray-from-tissue ambient ionization method is a new instrument for mass-spectrometry analysis of soft tissues without sample preparation. In this contribution, we analyze the performance of multi-label classification techniques in detection of the tumor and necrosis fragments within the sample.
Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients $15.00

Voula C. Georgopoulos and Chrysostomos D. Stylios

School of Health and Welfare Professions, Greece

Vol. 3, Issue 3

111-116

29-12-2017

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Abstract: For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.
Supervised Learning-based Nuclei Segmentation on Cytology Pleural Effusion Images with Artificial Neural Network Supervised Learning-based Nuclei Segmentation on Cytology Pleural Effusion Images with Artificial Neural Network

Khin Yadanar Win, Somsak Choomchuay, Kazuhiko HAMAMOTO, Manasanan Raveesunthornkiat

King Mongkut’s Institute of Technology Ladkrabang, Thailand

Vol.3, Issue 3

104-110

29-12-2017

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Abstract: Automated segmentation of cell nuclei is the crucial step towards computer-aided diagnosis system because the morphological features of the cell nuclei are highly associated with the cell abnormality and disease. This paper contributes four main stages required for automatic segmentation of the cell nuclei on cytology pleural effusion images. Initially, the image is preprocessed to enhance the image quality by applying contrast limited adaptive histogram equalization (CLAHE). The segmentation process is relied on a supervised Artificial Neural network (ANN) based pixel classification. Then, the boundaries of the extracted cell nuclei regions are refined by utilizing the morphological operation. Finally, the overlapped or touched nuclei are identified and split by using the marker-controlled watershed method. The proposed method is evaluated with the local dataset containing 35 cytology pleural effusion images. It achieves the performance of 0.95%, 0.86 %, 0.90% and 92% in precision, recall, F-measure and Dice Similarity Coefficient respectively. The average computational time for the entire algorithm took 15 mins per image. To our knowledge, this is the first attempt that utilizes ANN as the segmentation on cytology pleural effusion images.
A Medical Training System Using Augmented Reality: Trial Development of Environment Platform A Medical Training System Using Augmented Reality: Trial Development of Environment Platform $15.00

Ryosuke Umeda, Mohamed Atef Seif, Hiroki Higa, and Yukio Kuniyoshi

Ryukyu University, Japan

Vol.3, Issue 3

99-103

29-12-2017

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Abstract: A medical training system using augmented reality, AR is presented in this paper. Recognizing an AR marker through a Web camera, computer generated images appear on a real place. We prepared 3D (three-dimensional) anatomical objects to show, and evaluated our system using AR platform. It was found from the experimental result that our system overlaid the digital information of the 3D anatomical objects on the operator’s surrounding real world appropriately, and that feature of visible/invisible objects was verified in AR environment platform.
Making protein-protein interaction prediction more reliable with a large-scale dataset at the proteome level Making protein-protein interaction prediction more reliable with a large-scale dataset at the proteome level $15.00

Yoichi Murakami and Kenji Mizuguchi

Tokyo University of Information Sciences

Vol.3 , Issue 3

91-98

29-12-2017

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Abstract: Reliable information about protein-protein interactions (PPIs) enables us to better understand biological processes, pathways and functions. However, there are many experimental problems in identifying complete PPI-networks in a cell or organism. To supplement the limitations of current experimental techniques, we have previously proposed PSOPIA, a computational method to predict whether two proteins interact or not (http://mizuguchilab.org/PSOPIA/) [1]. In the PPI prediction, the selection of datasets is a big issue for accurately evaluating the performance of different algorithms [2, 3]. It is generally believed that increasing the size and diversity of examples makes the dataset more representative and reduces the noise effects; however, for many algorithms, it is impractical to use a large-scale dataset because of the memory and CPU time requirements. In this study, PSOPIA was retrained on a highly imbalanced large-scale dataset having a diverse set of examples at the proteome level. The dataset consisted of 43,060 high-confidence direct physical PPIs obtained from TargetMine [4] (as PPIs being only 0.13% of the total) and 33,098,951 non-PPIs. As a result, the new prediction model achieved a higher AUC of 0.89 (pAUCFPR≤0.5% = 0.24) than the previous model of PSOPIA. Furthermore, it was applied to the problem of filtering out protein pairs incorrectly labeled as interacting from a low-confidence human PPI dataset. Here, we suggest that a diverse …
Automatic Diabetic Retinopathy Screening Based on Morphological Operation and Support Vector Machine Integration Automatic Diabetic Retinopathy Screening Based on Morphological Operation and Support Vector Machine Integration $15.00

Syna Sreng, Noppadol Maneerat, Kazuhiko Hamamoto

King Mongkut’s Institute of Technology Ladkrabang, Thailand

Vol. 3, Issue 3

85-90

29-12-2017

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Abstract: Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.
Accuracy verification of knife tip positioning with position and orientation estimation of the actual liver for liver surgery support system Accuracy verification of knife tip positioning with position and orientation estimation of the actual liver for liver surgery support system $15.00

Daiki Yano, Masanao Koeda, Mayuko Doi, Kodai Okumoto, Shogo Yoshida, Katsuhiko Onishi, Hiroshi Noborio and Kaoru Watanabe

Osaka Electro-Communication University

Vol.3, Issue 3

79-84

29-12-2017

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Abstract: We are developing a surgical support system for liver abdominal surgery to prevent surgical accidents. Our support system can detect proximity of certain body parts and can make a warning when the knife approaches the critical part to be excised, such as large blood vessels. The system has two distance cameras with different features, and these cameras are located above the operating table. One camera measures the liver shape and the other tracks the position of a surgical knife during surgery. In this report, to evaluate the accuracy of the distance between the position of the knife tip and the position of the blood vessels, the position and orientation of the liver were estimated using depth images of mock liver and virtual liver by simulated annealing algorithm, and the distance between the knife tip and the blood vessel in the mock liver was measured. The experimental results showed the maximum average error of the measured distance was 5.76 mm.
Neuronal Activity Spike Sorting System Hardware-Software Co-design Neuronal Activity Spike Sorting System Hardware-Software Co-design $15.00

Wei Cai and Jian Xu

University of California

Vol.3, Issue 3

71-78

29-12-2017

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Abstract: Brain Machine Interface (BMI) is a spike sorting provide a connection between the external behavior and neural behavior of animals. Moreover, the spike sorting is significant for stability of the advanced application. To detect neuronal activity, multichannel recording is one of major methods. This paper proposed a software-hardware co-design framework with a 16-channel neural recording. Two-stage spike detection usually included a threshold method and a nonlinear energy operator (NEO). The spike clustering used the feature extraction. This multichannel spike sorting system algorithm were verified by simulations data and experiments results. The results presented a significant improvement on feature space during spike separation, due to the discrete derivative method.
Processing of retinal signals in the Limulus brain Processing of retinal signals in the Limulus brain $15.00

Kazuo Mori, Takehiko Saito, Frederick A. Dodge and Robert B. Barlow

Optical Neuroimaging Unit, Okinawa Institute of Science and Technology, Okinawa, Japan & Center for Vision Research, SUNY Upstate Medical University, NY, US

Volume 2 Issue 2

62 - 70

26-04-2016

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Abstract: Limulus retina sends signals to the brain through optic nerve fibers with processes terminating in both the first and second optic ganglia, called lamina and medulla. At the lamina, OFF signals are generated and sent to the medulla. Medullar cells extend fibers to the other parts of the brain. To understand the neural mechanisms of their visually guided mating behavior, it is important to investigate responses in the medulla. However, there are only very few studies on medulla due to experimental difficulties. In this paper, we developed two experimental setups to study the medulla ex vivo and succeeded in recording responses and in mapping receptive fields. Responses of medullar cells share many features with responses of ganglion cells in vertebrate retina. The recorded responses can be classified as non-spiking, sustained and transient ON, sustained and transient OFF, ON-OFF, inhibition, and bilateral and contralateral types. Their receptive fields vary from 6 degrees to more than 180 degrees. Some cells have receptive fields classified as ON-center and OFF-center. In addition, one medullar cell had a separated receptive field extending over 90 degrees in the horizontal direction. Keywords: visual processing, intracellular recording, medulla, brain, invertebrate, Limulus
Multi-Color Recognition based on Mini-Max Color Threshold for Medical Purpose Multi-Color Recognition based on Mini-Max Color Threshold for Medical Purpose $15.00

Zacharie Mbaitiga

Volume 1 Issue 1

55 - 61

25-12-2015

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Abstract: This paper discusses the multi-color recognition using the min-max color threshold for outdoor robot navigation. All colors used in this project are RGB orthogonal color space in order to see how much of each primary color between min and max that can be observed in the color to be recognized. The white color value in the color space is set as the object for which the target color to be recognized belongs, while the black color value is set as the object background. The recognition process is done by summing up first the values of the red, green and blue in each color to obtain the rgb sum value, which is then divided by the individual color element to obtain the color threshold. This threshold is compared to the originally color threshold for the recognition, where a satisfactory result is expected as the project is not yet finished. Keywords: Color recognition, threshold, mini-max, multicolor
Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination $15.00

Nermin Shaltout, Ahmed Rafea, Mohamed Moustafa, Ahmed Moustafa, Mahmoud ElHefnawi and Mohamed ElHefnawi

Volume 1 Issue 1

48 - 54

25-12-2015

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Abstract: The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88% increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone. Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.
A Study on Automatic Evaluation Method of Pointing and Calling for Nurse Education A Study on Automatic Evaluation Method of Pointing and Calling for Nurse Education $15.00

Tomoki Maekawa, Akira Urashima, Masaki Nakamura, Tomoji Toriyama, Junichi Oshima, Mitsuko Nakagawa and Tadao Nomura

Volume 1 Issue 1

45 - 47

25-12-2015

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Abstract: Pointing and calling is a method of preventing human error, which is widely used in Japanese industry. Pointing and calling also has the potential to reduce human error in hospital nursing duties. The development and evaluation of novel technologies are necessary to distinguish if pointing and calling is to be appropriately applied in this context. In this paper, we report on an automatic evaluation method for pointing and calling and present. the evaluation results from an experimental study of the simulated duties of 40 active nurses. Keywords: Pointing and calling; human error; wearable sensor; evaluation of motion
 

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