This study aimed to optimize electrode positions for measuring facial electromyography (fEMG) signals using a 3D head model. A highly detailed 3D head model was developed from medical images and used to simulate fEMG signals from the frontalis and corrugator muscles. Various electrode positions on the forehead were tested to determine which positions provided the strongest signals and best separation of the two muscle activations. The results showed that electrode pairs measuring each muscle should be oriented orthogonally to best distinguish the muscle signals. The optimal positions found can help improve fEMG-based human-computer interfaces by providing more accurate muscle activation measurements from fewer electrodes.
Expert system of single magnetic lens using JESS in Focused Ion Beamijcsa
Â
This work shows expert system of symmetrical single magnetic lens used in focused ion beam optical system. Java expert system shell(JESS) programming is proposed to build the intelligent agent "MOPTION"for getting an optimum magnetic flux density , and calculate the ion optical trajectory. The combination of such rule based engine and SIMION 8.1 has configured the reconstruction process and compiled the data retrieved by the proposed expert system agent to implement the pole-pieces reconstruction for lens design. The pole pieces reconstruction has been resulted in 3D graph , and under the infinite magnification conditions of the optical path, aberration (spherical / chromatic and total) disks diameters have been obtained and got the values (0.03,0.13 and 0.133) micron (ÎĽm) respectively.
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Â
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Â
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering csandit
Â
Computerized medical image segmentation is a challenging area because of poor resolution
and weak contrast. The predominantly used conventional clustering techniques and the
thresholding methods suffer from limitations owing to their heavy dependence on user
interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The
performance further deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering
for segmenting vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for abnormality
detection and thus proper localisation and labelling will enhance the diagnostic output of a
physician. The method is compared with Otsu thresholding and K-means clustering to illustrate
the robustness. The reference standard for validation was the annotated images from the
radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Microscopy images segmentation algorithm based on shearlet neural networkjournalBEEI
Â
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
Expert system of single magnetic lens using JESS in Focused Ion Beamijcsa
Â
This work shows expert system of symmetrical single magnetic lens used in focused ion beam optical system. Java expert system shell(JESS) programming is proposed to build the intelligent agent "MOPTION"for getting an optimum magnetic flux density , and calculate the ion optical trajectory. The combination of such rule based engine and SIMION 8.1 has configured the reconstruction process and compiled the data retrieved by the proposed expert system agent to implement the pole-pieces reconstruction for lens design. The pole pieces reconstruction has been resulted in 3D graph , and under the infinite magnification conditions of the optical path, aberration (spherical / chromatic and total) disks diameters have been obtained and got the values (0.03,0.13 and 0.133) micron (ÎĽm) respectively.
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Â
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Â
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
Segmentation and Labelling of Human Spine MR Images Using Fuzzy Clustering csandit
Â
Computerized medical image segmentation is a challenging area because of poor resolution
and weak contrast. The predominantly used conventional clustering techniques and the
thresholding methods suffer from limitations owing to their heavy dependence on user
interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The
performance further deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering
for segmenting vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for abnormality
detection and thus proper localisation and labelling will enhance the diagnostic output of a
physician. The method is compared with Otsu thresholding and K-means clustering to illustrate
the robustness. The reference standard for validation was the annotated images from the
radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Microscopy images segmentation algorithm based on shearlet neural networkjournalBEEI
Â
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcsandit
Â
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
BRAIN PORTION EXTRACTION USING HYBRID CONTOUR TECHNIQUE USING SETS FOR T1 WEI...sipij
Â
Brain portion extraction from magnetic resonance image (MRI) of human head scan is an important
process in medical image analysis. In this paper, we propose a computationally simple and a robust brain
segmentation method. This method is based on forming a contour using the intensity values that satisfy a
set property and detect the boundary of the brain. After detecting the brain boundary the brain portion is
segmented. Experiments were conducted by applying the method on 3 volumes of T1 MRI data set collected
from Internet Brain Segmentation Repository(IBSR) and compared the results with that of the popular
skull stripping method Brain Extraction Tool (BET). The experimental results show that the proposed
algorithm gives better results than that of BET.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...rahulmonikasharma
Â
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPROVED PARALLEL THINNING ALGORITHM TO OBTAIN UNIT-WIDTH SKELETONijma
Â
To extract the creditable features in a fingerprint image, many people use a thinning algorithm that plays a
very important role in preprocessing. In this paper, we propose a robust parallel thinning algorithm that
can preserve the connectivity of the binarized fingerprint image, while making the thinnest skeleton of only
1-pixel wide, which gets extremely close to the medial axis. The proposed thinning method repeats three
sub-iterations. The first sub-iteration takes off only the outermost boundary pixel using the inner points. To
extract the one-sided skeletons, the second sub-iteration seeks the skeletons with a 2-pixel width. The third
sub-iteration prunes the needless pixels with a 2-pixel width existing in the obtained skeletons. The
proposed thinning algorithm shows robustness against rotation and noise and makes the balanced medial
axis. To evaluate the performance of the proposed thinning algorithm, we compare it with and analyze
previous algorithms.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Â
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Â
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
Â
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Â
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcsandit
Â
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
BRAIN PORTION EXTRACTION USING HYBRID CONTOUR TECHNIQUE USING SETS FOR T1 WEI...sipij
Â
Brain portion extraction from magnetic resonance image (MRI) of human head scan is an important
process in medical image analysis. In this paper, we propose a computationally simple and a robust brain
segmentation method. This method is based on forming a contour using the intensity values that satisfy a
set property and detect the boundary of the brain. After detecting the brain boundary the brain portion is
segmented. Experiments were conducted by applying the method on 3 volumes of T1 MRI data set collected
from Internet Brain Segmentation Repository(IBSR) and compared the results with that of the popular
skull stripping method Brain Extraction Tool (BET). The experimental results show that the proposed
algorithm gives better results than that of BET.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...rahulmonikasharma
Â
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPROVED PARALLEL THINNING ALGORITHM TO OBTAIN UNIT-WIDTH SKELETONijma
Â
To extract the creditable features in a fingerprint image, many people use a thinning algorithm that plays a
very important role in preprocessing. In this paper, we propose a robust parallel thinning algorithm that
can preserve the connectivity of the binarized fingerprint image, while making the thinnest skeleton of only
1-pixel wide, which gets extremely close to the medial axis. The proposed thinning method repeats three
sub-iterations. The first sub-iteration takes off only the outermost boundary pixel using the inner points. To
extract the one-sided skeletons, the second sub-iteration seeks the skeletons with a 2-pixel width. The third
sub-iteration prunes the needless pixels with a 2-pixel width existing in the obtained skeletons. The
proposed thinning algorithm shows robustness against rotation and noise and makes the balanced medial
axis. To evaluate the performance of the proposed thinning algorithm, we compare it with and analyze
previous algorithms.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Â
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
A novel approach for efficient skull stripping using morphological reconstruc...eSAT Journals
Â
Abstract Brain is the part of the central nervous system located in skull. For the diagnosis of human brain bearing tumour, skull stripping plays an important pre-processing role. Skull stripping is the process separating brain and non-brain tissues of the head which is the critical processing step in the analysis of neuroimaging data. Though various algorithms have been proposed to address this problem, challenges remain. In this paper a new efficient skull stripping method for magnetic resonance images (MRI) is proposed. This method adopts a two-step approach; in the first step an improved systematic application of morphological reconstructions operations is done for the brain image and in the second step, a thresholding based technique is used to extract the brain inside the skull. This paper experimented on Axial PD and FLAIR MRI brain images. Index Terms: Skull stripping, thresholding, morphological reconstruction, Axial PD and FLAIR MRI images of brain.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
Â
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Â
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Abstract
Human–computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use. The field formally emerged out of computer science, cognitive psychology and industrial design through the 1960s, formulating guidelines for the development of interactive computer systems highlighting usability concerns for improved interfaces. Computing devices are becoming more prevalent and integrated into both our social and work spaces.HCI therefore plays an important role in ensuring that computer systems are not only functional but also respect the needs and capabilities of the humans that use them.
HCI encompasses not only ease of use but also new interaction techniques. It involves input and output devices and the interaction techniques that use them; presentation of information, control and monitoring of computer’s actions and the processes that developers follow when creating interfaces. In this seminar, emphasis is laid on the movement of a user’s eyes which can provide a convenient, natural, and high-bandwidth source of additional user input. Some of the human factors and technical considerations that arise in trying to use eye movements as an input medium and the first eye movement-based interaction techniques are discussed in this section.
AYUSHA PATNAIK,
SEM - 6th
TRIDENT ACADEMY OF TECHNOLOGY,
BBSR
A new hybrid method for the segmentation of the brain mrissipij
Â
The magnetic resonance imaging is a method which has undeniable qualities of contrast and tissue
characterization, presenting an interest in the follow-up of various pathologies such as the multiple
sclerosis. In this work, a new method of hybrid segmentation is presented and applied to Brain MRIs. The
extraction of the image of the brain is pretreated with the Non Local Means filter. A theoretical approach is
proposed; finally the last section is organized around an experimental part allowing the study of the
behavior of our model on textured images. In the aim to validate our model, different segmentations were
down on pathological Brain MRI, the obtained results have been compared to the results obtained by
another models. This results show the effectiveness and the robustness of the suggested approach.
Modelling and Control of a Robotic Arm Using Artificial Neural NetworkIOSR Journals
Â
Abstract: Often it can be seen that men with a lost arm face severe difficulties doing daily chores. Artificial
Intelligence could be effectively used to provide some respite to those people. Neural networks and their
applications have been an active research topic since recent past in the rehabilitation robotics/machine
learning community, as it can be used to predict posture/gesture which is guided by signals from the human
brain. In this paper, a method is proposed to estimate force from Surface Electromyography (s-EMG) signals
generated by specific hand movements and then design and control a Robotic arm using Artificial Neural
Network (ANN) to replicate human arm. Here the force prediction is a Regression process. A hand model has
been successfully moved using servo motor that has been programmed based on the results obtained from
sample data. The results shown in this paper illustrate how the Robotic arm performs.
Index Terms: Surface EMG, Artificial Neural Network, Robotic arm, Regression.
This is slide set of my Octopus-ReEL (Realtime Encephalography Lab) presentation in GDG-Izmir event held on Nov 3rd 2018 at Ege University Computer Engineering Dept.
OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE FOR BIOMECHANIC CLASSIFICATION TA...ijaia
Â
Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are
complicated and prove difficult to analyze due to physiological noise and other issues. Computational
intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful
tools for analyzing EMG signals and creating optimal myoelectric control schemes for prostheses. This
research examines the performance of four different neural network architectures (feedforward, recurrent,
counter propagation, and self organizing map) that were tasked with classifying walking speed when given
EMG inputs from 14 different leg muscles. Experiments conducted on the data set suggest that self
organizing map neural networks are capable of classifying walking speed with greater than 99% accuracy.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
Â
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
Â
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
Short-term hand gestures recognition based on electromyography signalsIAESIJAI
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Electromyography pattern recognition to predict limb movements can significantly enhance the control of the prosthesis. However, this technique has not yet been widely used in clinical practice. Improvements in the myoelectric pattern recognition (MPR) system can improve the functionality of the prosthesis. This study proposes new sets of time domain features to enhance the MPR control system. Three groups of features are evaluated, time domain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.
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Electrode Position Optimization For Facial E M G Measurements For Human Computer Interface
1. Electrode position optimization for facial EMG
measurements for human-computer interface
N. Nöjd, M. Hannula and J. Hyttinen
Department of Biomedical Engineering
Tampere University of Technology
Tampere, Finland
Abstract— The aim of this work was to model facial EMG produced from a few motor units. Our anatomic
electromyography (fEMG) to find optimal electrode positions accurate 3D model allows us to obtain precise results for the
for wearable human-computer interface system. The system is purposes of the muscle activation modeling and we use a
a head cap developed in our institute and with it we can source that models the activation of a whole muscle.
measure fEMG and electro-oculogram (EOG). The signals can
be used to control the computer interface: gaze directions move II. MATERIALS AND METHODS
the cursor and muscle activations correspond to clicking. In
this work a very accurate 3D model of the human head was A. Head model
developed and it was used in the modeling of fEMG. The The volume data of the head used was of the Visible
optimal positions of four electrodes on the forehead measuring Human Female project consisting of anatomical cryosection
the activations of frontalis and corrugator muscles were images and CT slices. The initial segmentation of major
defined. It resulted that the electrode pairs used in frontalis tissues was performed employing 3D methods with flexible
and corrugator measurements should be placed orthogonally propagation restraints e.g, 3D active contour and level set
by comparison with each other to get a signal that enables the methods. The resulting raw segmentations were further fine
separation of those two different activations.
tuned where necessary using 3D morphological and 2D
Keywords - fEMG; volume conductor model; FDM; modeling
methods. In some cases when there were very complex
structures or almost invisible tissue borders, manual
I. INTRODUCTION segmentation was the only option.
We have constructed a wireless head cap that enables the For tissues within the skin and the eyes, synthetic tissue
measurements of facial muscle activations and the layers were produced using morphological methods due to
movements of the eyes. Our ultimate goal is to develop a their low visibility resulting from poor image contrast. For
measurement system that would suit in the control of a example the forehead muscles like corrugator and frontalis
computer interface: the gaze direction could move the cursor muscles could not be seen in every CT and cryosection
with some facial expressions to correspond clicking. The image, but in the segmentation phase those two muscle types
radio circuit used in the wireless data transmission enables were approximated to exist at the positions where they
the use of six measurement channels. To be able to get a should be according to the correlated CT and MR images in
good signal quality with only a few measurement electrodes the book named Basic Atlas of Sectional Anatomy [4].
the electrode positions on the forehead should be planned
The volume conductor model included seven different
carefully.
tissue types: scalp, muscle, eye, skull, cerebrospinal fluid
In this work a very accurate 3D head model is developed (CSF), grey matter, and white matter. The tissue resistivity
and used to model the fEMG. Modeled signal is used to values that were applied in the volume conductor model are
optimize the electrode positions for fEMG measurements. shown in Table I.
Modeling of EMG is important also in other research areas:
fEMG could be used in human-computer interfaces to TABLE I. TISSUE RESISTIVITY VALUES
control the cursor [3], but the use of the facial muscle Tissue Resistivity / Ωcm Reference
activation measurements will probably increase also in Latikka, Kuurne, Eskola [5]
medicine as the level of sedation or anesthesia is controlled Scalp 351 (the same resistivity than the
with those measurements. grey matter has)
Muscle 250 Duck [6]
Modeling of EMG parameters has been in focus also Eye (vitreous
67 Gabriel & Gabriel [7]
earlier, and there exist several studies that model for example humour)
the motor unit potentials and the effects of changing different Oostendorp et al. [8] (value of
Skull 5265
white matter multiplied by 15)
motor unit parameters on the surface EMG [1,2]. Most of the CSF 55 Duck [6]
existing studies use mathematical models instead of Grey matter 351 Latikka, Kuurne, Eskola [5]
anatomical. Also there is a lack of models that cover the White matter 391 [5]
whole activation feature of a muscle, not only the surface
2. Originally the segmented realistically shaped volume
conductor model had a resolution of 0.33 mm x 0.33 mm x
0.33 mm. Anyhow the resolution had to be decreased in
some parts of the model so that the calculation capacity of
the computer and the calculation program were able to solve
the forward and inverse problems. The best resolution was
kept unchanged at the forehead parts, because the muscles
whose activity the modeling concerned located there. A
sagittal view of the original model and the model with the
decreased resolution can be seen in Fig 1.
B. Simulated fEMG
a) b)
Reciprocity theorem and lead field concept were used to
calculate the surface potentials [9]. An iterative FDM solver Figure 2. a) Source dipoles in frontalis muscles were aligned 27 degrees
developed in our institute was used to construct a finite backward. That was an approximation for the direction of the forehead and
difference method -model (FDM) of the segmented thus for the direction of the muscle cells in frontalis muscles. In the
anatomical model. Lead vectors were calculated for all the corrugator muscles dipoles were aligned on a (z,y)-plane the x component
16 804030 nodes in the volume conductor model, and those being zero. b) The direction of source dipoles in frontalis and corrugator
muscles in (z,y)-direction. In frontalis muscles dipoles were aligned 27
lead fields were calculated with 33 different surface degrees left and right, and in corrugator muscles 63 degrees left and right.
electrodes, one being all the time a reference. The lead Fig. b) modified from [10].
vectors and source vectors in the muscles were used in
calculations of the surface potentials. C. Optimal electrode positions
The source configuration used to simulate the surface The positions of four electrodes measuring the activations
potentials consisted of dipoles in every node point of an of frontalis and corrugator muscles were defined in this
activated muscle: In corrugator muscles (left and right) there work. Radio transmitter system has a limited data rate and
were 17773 and 13123 source dipoles and in frontalis the wireless data transmission in the current head cap enables
muscles 102470 and 91641. The whole muscle can be treated the data transmission of six measurement channels with the
as a source, because muscles in a face always contract so, sampling frequency of 1 kHz. Channels are reserved for
that every muscle cell in an activated muscle contracts bipolar measurements of fEMG (2), EOG (2), and the last
despite the force needed. In facial muscles the gradation of two channels for later use e.g., for the measurement of brain
tension force is carried out via variation in the firing rates. activity. More detailed information of the radio transmitter in
Source dipoles were unit vectors that were aligned the head cap can be found in [11].
approximately parallel to the realistic muscle cells. In the Optimal positions for muscle activation measurements
frontalis muscles dipoles were unit vectors directing 27° back were defined so that one of the source muscles at a time was
and left or right depending on the side of the muscle. In the chosen to be active and the surface potentials produced by
corrugator muscles dipoles were unit vectors directing that muscle were calculated. The surface potentials at the
upwards and 63° left or right. Fig 2. clarifies the directions of forehead were considered on 33 different electrodes. The
the source dipoles. electrode pair which produced the biggest potential
difference was the most sensitive for the measurement and
was selected to be the optimal electrode pair for measuring
the activity of that specific muscle.
a) b) c)
Figure 1. The resolution of the original head model was decreased in posterior and lowest part of the head. a) Sagittal view of the model with the
decreased resolution. b) Sagittal view of the original model. c) 3D view of the model with the decreased resolution.
3. III. RESULTS
We segmented Visible Human Female head from 855
cryosection images along with data from the CT images. The
model comprised of seven different tissue types. The original
model consisted of altogether over 160 million voxels and
the model with decreased resolution of 16 million voxels.
Voxel resolution was 0.33 mm in all dimensions in the
region of interest. Within the eyes and skin we created more
detailed synthetic segmentations based on anatomical data
from literature. The lead field calculations with the model for
33 electrodes took almost five days, but those had to be
calculated only once. Figure 3. The most sensitive electrode pairs for right and left corrugator
Optimal electrode positions obtained by comparing the measurements are (32, 23) and (32, 29), and for right and left frontalis (32,
6) and (27, 5). The numbering of single electrodes goes from left and up to
simulated surface potential values on the forehead are shown right and down.
in Fig 3. It can be seen, that the positions of the measurement
electrodes for corrugator and frontalis are partly overlapping.
That causes the signal of corrugator or frontalis muscle
right corrugator
activation to be measured with both measurement electrode right frontalis
pairs, what means that we do not know whether the
corrugator of frontalis is activated. It can also be seen, that
for the frontalis muscles the electrode positions are not
symmetrically placed on the forehead. This might result from
the asymmetry of the muscles – those are by hand separated
from the homogenous muscle tissue – and muscles can also
be naturally asymmetric since the facial expressions are
unique.
Obtained electrode positions were improved so that they
have better separating capability for different sources. The 20
electrode pairs, which gave the biggest potential values for
both right corrugator and right frontalis muscle, were
selected for a test, in which the separating capability of the 23 23 23 23 23 18 17 19 23 20 6 6 6 6 1 1 14 14 6 1
32 26 25 27 28 23 23 23 29 23 32 26 24 25 32 26 32 26 23 24
measurement electrode was studied - the electrode positions
were tried to tune so that the electrode pair sensitive to the
Figure 4. Black bars show the potential value difference due to right
corrugator would not be sensitive to the frontalis. This way frontalis muscle activation and gray bars due to right corrugator muscle
we attempted to avoid the overlapping problem. In Fig 4. activation on each electrode pair. Electrode pairs are named underneath the
there are potential values of the surface electrode pairs due to bar pairs. The ten electrode pairs from left are selected, because, they
active corrugator (gray bars) and active frontalis (black bars). showed the biggest sensitivity for corrugator muscle activation, and the ten
The electrode pairs which were the most sensitive for the electrode pairs on the right, because they were the most sensitive for the
frontalis activation.
corrugator are the ten first from the left and the ten last are
the electrode pairs which were the most sensitive for the
When the best measurement electrode pair for the
frontalis. In every electrode pair the ratio of the potentials
corrugator activation was searched, the best potential ratio
resulting from the wanted source (corrugator or frontalis) and
(32.6) was given by the electrode pair (23, 27). The ratios of
from the other (frontalis or corrugator) was calculated. The
other pairs were below 2. For the frontalis activation the
electrode pair which gave the biggest ratio was selected to be
electrode pair (6, 24) gave the best potential ratio (14.6).
the electrode pair which measures the activation of the
Also the electrode pairs (6, 26), (6, 25), (1, 26) and (1, 24)
wanted source best without measuring the activation of the
gave good ratios (about 10). The final optimal electrode
other source.
positions for frontalis and corrugator measurements are
shown in Fig 5. Because the left and right corrugator and
frontalis muscles activate normally in tandem it is enough to
measure only the other side.
4. [2] R. Merletti, L. Lo Conte, E. Avignone, P. Cuglielminotti, “Modeling
of surface myoelectric signals--Part l: Model implementation,” IEEE
Transactions on Biomedical Engineering, vol. 46, pp. 810-820, 1999.
[3] A. Barretto, S. Scargle, M. Adjouadi, “A practical EMG-based
human-computer interface for users with motor disabilities,” Journal
of Rehabilitation Research and Development, vol. 37, pp. 53-64,
2000.
[4] W. Bo, W.N., W. Krueger, J. Carr, R. Bowden, I. Meschan, Basic
Atlas of Sectional Anatomy With Correlated Imaging, 3rd ed.,
Philadelphia, 1998, W.B. Saunders Company.
[5] J. Latikka, T. Kuurne, H. Eskola, “Conductivity of living intracranial
tissues,” Physics in Medicine and Biology, vol. 46, pp. 1611-1666,
Figure 5. The optimal electrode positions for the measurements of 2001.
frontalis and corrugator muscle activations. [6] F. Duck, Physical Properties of Tissue: A Comprehensive Reference
Book, San Diego, 1990, Academic Press.
Comparing the electrode positions in Fig 3. and Fig 5. [7] C. Gabriel, S. Gabriel, “Compilation of the dielectric properties of
shows that better separating capability for frontalis and body tissues at RF and microwave frequencies”, 1996.
corrugator muscle activation measurements is achieved by [8] T. Oostendorp, J. Delbeke, D. Stegeman, The Conductivity of the
placing the electrode pairs more orthogonally. Our result of Human Skull: Results of In Vivo and In Vitro Measurements, IEEE
the optimal electrode positions is particularly applicable to Transactions on Biomedical Engineering, vol. 47, pp. 1487-1492,
2000.
the person used as a model. The anatomy of the head, facial
[9] J. Malmivuo, R. Plonsey, Bioelectromagnetism: Principles and
muscles and eyes is highly individual and thus, the strict Applications of Bioelectric and Biomagnetic Fields, New York, 1995,
optimal electrode positions can vary from person to another. Oxford University Press. 480.
Still the main result holds: the positions of the electrode pairs [10] A. V. Boxtel, “Optimal signal bandwidth for the recording of surface
should lie orthogonally on the forehead to be able to separate EMG activity on facial, jaw, oral and neck muscles”,
the signals from corrugator and frontalis sources. That is Psychophysiology, vol. 38, pp. 22-34, 2001.
based on the directions of the muscle fibers: those are quite [11] A. Vehkaoja, J. Verho, M. Puurtinen, N. Nöjd, J. Lekkala, J. Hyttinen,
alike from person to person despite the fact that the thickness quot;Wireless head cap for EOG and facial EMG measurementsquot;, IEEE-
EMBS 2005: 27th Annual International Conference,
or the places of the muscles can vary. issue 6, pp. 5865-8 , 17-18 Jan. 2006 Page(s):5865 - 5868
IV. CONCLUSIONS
A new accurate model is now available for modelling
purposes such as bioelectric field problems. It has high
spatial accuracy and number of inhomogeneities providing
good platform for various simulations. For bioelectric
simulation with FEM or FDM methods the number of
elements in the resulting model exceeds the standard
computer resources. The model used in this study had seven
but the latest version has already 23 different tissue types.
In this work the used source model for facial muscle
activation was the whole muscle full of unit vectors all
directed to same direction parallel to real muscle cells. In the
future the activation of the frontalis and corrugator muscles
could be measured and different source configurations
inserted into the model to see what kind of source
configuration produces the most realistic surface potential
distribution. Real measurements should also be done to make
certain that the obtained electrode positions provide better
signal quality than those used in the current head cap.
With the current measurement system the movements of
eyes and the activations of two different facial muscles can
be measured, and measured information can be used to
control the computer. We will develop our system further,
and modeling of both fEMG and EOG will be used in the
development.
REFERENCES
[1] E. Stalberg, L. Karlsson, “Simulation of the normal concentric needle
electromyogram by using a muscle model,” Clinical
Neurophysiology, vol. 112, pp. 464-471, 2001.