Heart disease is one of the most dangerous diseases that threaten human life. The doctor uses
echocardiography to analyze heart disease. The result of echocardiography test is a video that shows the
movement of the heart rate. The result of echocardiography test indicates whether the patient’s heart is
normal or not by identifying a heart cavity area. Commonly it is determined by a doctor based on his own
accuracy and experience. Therefore, many methods to do heart segmentation is appearing. But, the
methods are a bit slow and less precise. Thus, a system that can help the doctor to analyze it better is
needed. This research will develop a system that can analyze the heart rate-motion and automatically
measure heart cavity area better than the existing method. This paper proposes an improved system for
cardiac segmentation using median high boost filter to increase image quality, followed by the use of an
active shape model and optical flow. The segmentation of the heart rate-motion and auto measurement of
the heart cavity area is expected to help the doctor to analyze the condition of the patient with better
accuracy. Experimental result validated our approach.
Left Ventricle Volume Measurement on Short Axis MRI Images Using a Combined R...IDES Editor
Segmentation and volume measurement of the
cardiac ventricles is an important issue in cardiac disease
diagnosis and function assessment. Cardiac Magnetic
Resonance Imaging (CMRI) is the current reference standard
for the assessment of both left and right ventricular volumes
and mass. Several methods have been proposed for
segmentation and measurement of cardiac volumes like
deformable models, active appearance, shape models, atlas
based methods, etc. In this paper a novel method is proposed
based on a parametric superellipse model fitting for
segmentation and measurement volume of the left ventricle
on cardiac Cine MRI images. Superellipses can be used to
represent in a large variety of shapes. For fitting superellipse
on MR images, a set of data points have been needed as a
partial data. This data points resulted from a semi-automatic
region growing method that segment the homogenous region
of the left ventricle. Because of ellipsoid shape of left ventricle,
fitting superellipse on cardiac cine MRI images has excellent
accuracy. The results show better fitting and also less
computation and time consuming compared to active contour
methods, which is commonly used method for left ventricle
segmentation.
Segmentation of cysts in kidney and 3 d volume calculation from ct images ijcga
This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice
images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the
same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented
from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of
non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in
the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated
by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D
visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume
calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide practical supports to surgery options and medical
practice to medical students
Segmentation of cysts in kidney and 3 d volume calculation from ct imagesbioejjournal
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
Visible watermarking within the region of non interest of medical images base...csandit
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
Optimization Based Liver Contour Extraction of Abdominal CT Images IJECEIAES
This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region is identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.
Left Ventricle Volume Measurement on Short Axis MRI Images Using a Combined R...IDES Editor
Segmentation and volume measurement of the
cardiac ventricles is an important issue in cardiac disease
diagnosis and function assessment. Cardiac Magnetic
Resonance Imaging (CMRI) is the current reference standard
for the assessment of both left and right ventricular volumes
and mass. Several methods have been proposed for
segmentation and measurement of cardiac volumes like
deformable models, active appearance, shape models, atlas
based methods, etc. In this paper a novel method is proposed
based on a parametric superellipse model fitting for
segmentation and measurement volume of the left ventricle
on cardiac Cine MRI images. Superellipses can be used to
represent in a large variety of shapes. For fitting superellipse
on MR images, a set of data points have been needed as a
partial data. This data points resulted from a semi-automatic
region growing method that segment the homogenous region
of the left ventricle. Because of ellipsoid shape of left ventricle,
fitting superellipse on cardiac cine MRI images has excellent
accuracy. The results show better fitting and also less
computation and time consuming compared to active contour
methods, which is commonly used method for left ventricle
segmentation.
Segmentation of cysts in kidney and 3 d volume calculation from ct images ijcga
This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice
images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the
same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented
from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of
non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in
the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated
by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D
visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume
calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide practical supports to surgery options and medical
practice to medical students
Segmentation of cysts in kidney and 3 d volume calculation from ct imagesbioejjournal
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
Visible watermarking within the region of non interest of medical images base...csandit
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
Optimization Based Liver Contour Extraction of Abdominal CT Images IJECEIAES
This paper introduces computer aided analysis system for diagnosis of liver abnormality in abdominal CT images. Segmenting the liver and visualizing the region of interest is a most challenging task in the field of cancer imaging, due to small observable changes between healthy and unhealthy liver. In this paper, hybrid approach for automatic extraction of liver contour is proposed. To obtain optimal threshold, the proposed work integrates segmentation method with optimization technique in order to provide better accuracy. This method uses bilateral filter for preprocessing and Fuzzy C means clustering (FCM) for segmentation. Mean Grey Wolf Optimization technique (mGWO) has been used to get the optimal threshold. This threshold is used for segmenting the region of interest. From the segmented output, largest connected region is identified using Label Connected Component (LCC) algorithm. The effectiveness of proposed method is quantitatively evaluated by comparing with ground truth obtained from radiologists. The performance criteria like dice coefficient, true positive error and misclassification rate are taken for evaluation.
Abstract
This issue of ‘Cardiovascular Diagnosis and Therapy (CDT)’ has a special focus on application and development of magnetic resonance imaging (MRI) in cardiovascular diseases. The challenges associated with imaging of the heart and the huge disease burden associated with cardiovascular diseases has been one of the major motivations in the last few years for the development of new MRI techniques. A realm of new pulse sequences, either focusing on ‘freezing’ motion or on providing improved endogenous contrast mechanisms were developed in this context and are now being evaluated in clinical and preclinical research efforts focusing on the heart and vascular circulation.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
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
Modeling Cardiac Pacemakers With Timed Coloured Petri Nets And Related ToolsMohammed Assiri
Verification Grand Challenge is one of the Grand Challenges for Computing Research. Verification, which is the strict proof of the correctness of software according to its specifications, results in reliable software and potential cost reductions.
The cardiac pacemaker (pacemaker thereafter) is an electronic device that monitors and controls the heart rhythm via sensing and pacing operations. The pacemaker treats cardiac arrhythmia, defined as abnormal patterns of the heartbeat.
This research utilizes formal methods to model, validate and verify the interdisciplinary requirements of pacemaker systems.
Segmentation and Automatic Counting of Red Blood Cells Using Hough TransformIJARBEST JOURNAL
Authors:- V. Antony Asir Daniel1, J. Surendiran2, K. Kalaiselvi3
Abstract- Red blood cells are specialized as oxygen carrier RBC plays a crucial role in
medical diagnosis and pathological study. The blood samples are collected using the smear
glass slide. These samples are taken under the test using the image of the blood. Filtering
process are carries out to remove the noise. Morphological operation are applied on the
blood image and using Hough transform method the RBC are counted which is the
effective segmentation process.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentatio...IJECEIAES
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.
The morphologic and functional study of cardiovascular system is of vital importance because problems related to this system are one of the main causes of mortality in the world. It is important to mention that the left ventricle (VI) is the most susceptible to suffer severe damage, in diseases, such as arterial hypertension, mellitus diabetes or arteriosclerosis. This article presents a new methodology directed to segmentation of left ventricular contours, in angiographic images by using Generalized Hough Transform (TGH). It is important to obtain the ventricular edge, because analyzing processes of systole and diastole end, it is possible to calculate parameters of the cardiac functionality as the end-diastolic volume, end systolic volume, ejection fraction, cardiac output, Hyperkinéticos, Hypokinéticos segments, and normal; in this work we focus only on the removal of the same. For the system implementation, it was used some technics such binarization contrast enhancement, ordering points, filtering, mathematical morphology, b-spline and the TGH, that improved the ventricular visibility contour, highlighting data of interest, eliminating or reducing the effect of present structures such as ribs, catheter and segmented automatically the ventricular contour. The system was validated using 10 studies of angiography, obtained in the Instituto Autónomo Hospital Universitario of the Universidad de los Andes (I.A.H.U.L.A.), Merida, Venezuela. The percentage of generalized error performing the comparison of the manual segmentation and automating segmentation was 3.54%+_ 0.2 taken as a basis the Pearson correlation coefficient. Throughout the work demonstrates the functionality and applicability of the technique for the detection of ventricular edge in angiographic images. In future works is expected to supplement this analysis with the calculation of the descriptors of the cardiac functionality.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
ABSTRACT
Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a post processing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
Measurements of retinal blood vessel morphology have been shown to be related to the risk of
cardiovascular diseases. The wrong identification of vessels may result in a large variation of these
measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of
automatically identifying true vessels as a post processing step to vascular structure segmentation. We
model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying
vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to
solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images.
Experiment results are analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true
vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
Abstract
This issue of ‘Cardiovascular Diagnosis and Therapy (CDT)’ has a special focus on application and development of magnetic resonance imaging (MRI) in cardiovascular diseases. The challenges associated with imaging of the heart and the huge disease burden associated with cardiovascular diseases has been one of the major motivations in the last few years for the development of new MRI techniques. A realm of new pulse sequences, either focusing on ‘freezing’ motion or on providing improved endogenous contrast mechanisms were developed in this context and are now being evaluated in clinical and preclinical research efforts focusing on the heart and vascular circulation.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
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
Modeling Cardiac Pacemakers With Timed Coloured Petri Nets And Related ToolsMohammed Assiri
Verification Grand Challenge is one of the Grand Challenges for Computing Research. Verification, which is the strict proof of the correctness of software according to its specifications, results in reliable software and potential cost reductions.
The cardiac pacemaker (pacemaker thereafter) is an electronic device that monitors and controls the heart rhythm via sensing and pacing operations. The pacemaker treats cardiac arrhythmia, defined as abnormal patterns of the heartbeat.
This research utilizes formal methods to model, validate and verify the interdisciplinary requirements of pacemaker systems.
Segmentation and Automatic Counting of Red Blood Cells Using Hough TransformIJARBEST JOURNAL
Authors:- V. Antony Asir Daniel1, J. Surendiran2, K. Kalaiselvi3
Abstract- Red blood cells are specialized as oxygen carrier RBC plays a crucial role in
medical diagnosis and pathological study. The blood samples are collected using the smear
glass slide. These samples are taken under the test using the image of the blood. Filtering
process are carries out to remove the noise. Morphological operation are applied on the
blood image and using Hough transform method the RBC are counted which is the
effective segmentation process.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentatio...IJECEIAES
Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.
The morphologic and functional study of cardiovascular system is of vital importance because problems related to this system are one of the main causes of mortality in the world. It is important to mention that the left ventricle (VI) is the most susceptible to suffer severe damage, in diseases, such as arterial hypertension, mellitus diabetes or arteriosclerosis. This article presents a new methodology directed to segmentation of left ventricular contours, in angiographic images by using Generalized Hough Transform (TGH). It is important to obtain the ventricular edge, because analyzing processes of systole and diastole end, it is possible to calculate parameters of the cardiac functionality as the end-diastolic volume, end systolic volume, ejection fraction, cardiac output, Hyperkinéticos, Hypokinéticos segments, and normal; in this work we focus only on the removal of the same. For the system implementation, it was used some technics such binarization contrast enhancement, ordering points, filtering, mathematical morphology, b-spline and the TGH, that improved the ventricular visibility contour, highlighting data of interest, eliminating or reducing the effect of present structures such as ribs, catheter and segmented automatically the ventricular contour. The system was validated using 10 studies of angiography, obtained in the Instituto Autónomo Hospital Universitario of the Universidad de los Andes (I.A.H.U.L.A.), Merida, Venezuela. The percentage of generalized error performing the comparison of the manual segmentation and automating segmentation was 3.54%+_ 0.2 taken as a basis the Pearson correlation coefficient. Throughout the work demonstrates the functionality and applicability of the technique for the detection of ventricular edge in angiographic images. In future works is expected to supplement this analysis with the calculation of the descriptors of the cardiac functionality.
Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clust...IJECEIAES
Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
ABSTRACT
Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a post processing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
AUTOMATED SEGMENTATION OF FLUORESCENT AND FUNDS IMAGES BASED ON RETINAL BLOOD...acijjournal
Measurements of retinal blood vessel morphology have been shown to be related to the risk of
cardiovascular diseases. The wrong identification of vessels may result in a large variation of these
measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of
automatically identifying true vessels as a post processing step to vascular structure segmentation. We
model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying
vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to
solve this optimization problem and evaluate it on a large real-world dataset of 2,446 retinal images.
Experiment results are analyzed with respect to actual measurements of vessel morphology. The results
show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true
vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.
An approach for cross-modality guided quality enhancement of liver imageIJECEIAES
A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes magnetic resonance imaging (MRI) scan of liver as a guide to enhance the structures of computed tomography (CT) liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved.
A new algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the hipoechogenic characteristics of the lumen for the identification of the carotid boundaries and the echogenic characteristics for the identification of the bifurcation boundaries. The image to be segmented is processed with the application of an anisotropic diffusion filter for speckle removal and morphologic operators are employed in the detection of the artery. The obtained information is then used in the definition of two initial contours, one corresponding to the lumen and the other to the bifurcation boundaries, for the posterior application of the Chan-vese level set segmentation model. A set of longitudinal B-mode images of the common carotid artery (CCA) was acquired with a GE Healthcare Vivid-e ultrasound system (GE Healthcare, United Kingdom). All the acquired images include a part of the CCA and of the bifurcation that separates the CCA into the internal and external carotid arteries. In order to achieve the uppermost robustness in the imaging acquisition process, i.e., images with high contrast and low speckle noise, the scanner was adjusted differently for each acquisition and according to the medical exam. The obtained results prove that we were able to successfully apply a carotid segmentation technique based on cervical ultrasonography. The main advantage of the new segmentation method relies on the automatic identification of the carotid lumen, overcoming the limitations of the traditional methods.
Filter technique of medical image on multiple morphological gradient methodTELKOMNIKA JOURNAL
Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.
A hybrid approach to medical decision-making: diagnosis of heart disease wit...IJECEIAES
Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
Retinal blood vessel extraction and optical disc removaleSAT Journals
Abstract Retinal image processing is an important process by which we can detect the blood vessels and this helps us in detecting the DIABETIC RETINOPATHY at a early stage and this is very helpful because the symptoms are not known by anyone unless we have blur eye sight or we get blind. And this mainly occurs in people suffering from high diabetes. So by extracting the blood vessels using the algorithm we can see which blood vessels are actually damaged. So by using the algorithm we can continuously survey the situation and can protect our eye-sight. Keywords: field of view, retinopathy, thresholding, morphology, Otsu's algorithm, MATLAB.
Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Brea...TELKOMNIKA JOURNAL
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
Detection of uveal melanoma using fuzzy and neural networks classifiersTELKOMNIKA JOURNAL
The use of image processing is increasingly utilized for disease detection. In this article, an algorithm is proposed to detect uveal melanoma (UM) which is a type of intraocular cancer. The proposed method integrates algorithms related to iris segmentation and proposes a novel algorithm for the detection of UM from the approach of fuzzy logic and neural networks. The study case results show 76% correct classification in the fuzzy logic system and 96.04% for the artificial neural networks.
Detection of lung pathology using the fractal methodIJECEIAES
Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
Similar to Improved echocardiography segmentation using active shape model and optical flow (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
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Santiago [8]. The purpose of this paper is a better algorithm of ASM because the process of
fitting on the ASM, the results obtained are still less well, Santiago [9].
In this paper proposes a combination of ASM and PDAF to generate a stronger
segmentation of the parameter approach, El-Rewaidy [10]. In another study segmentation of the
heart cavity using another method, the watershed and showed better results than the active
contour [11]. In this research of cardiac cavity in two-dimensional short-axis, echocardiography
image with the method uses boundary and triangle equation to detect and reconstruct the
border [12]. In this paper, a new formulation of a Robust Active Shape Model (RASM) is present
to 3D segmentation of the left ventricle (LV) in cardiac MRI [13]. In this paper uses combine a
random forest classifier with an active shape model (ASM) to present a model-based learning
segmentation algorithm to detect the left ventricle (LV) boundary of the heart from ultrasound
(US) images [14]. In this paper presents a semi-automatic approach in this study that is capable
of identifying the endocardial borders robustly from cine magnetic resonance images using
deformation flow and optical flow [15]. This paper reports a quantification of cardiac motion in
Short-Axis (SAX) MRI images using the optical flow method [16]. In this paper presents
estimates the motion of cardiac ultrasound imaging and compare with several classical optical
flow estimation algorithms [17, 18]. In this paper presents a mathematical model that simulates
the real cardiac motion during myocardial tagging for evaluating Left Ventricular contractility for
Cardiac Magnetic Resonance Imaging (MRI) sequence [19].
In this paper presents a framework using the Active Shape Model (ASM) and Optical
Flow for real-time facial landmarks extraction and tracking [20]. In this paper presents a new
registration method between ultrasound (US) image and Magnetic resonance imaging (MRI),
using optical flow model for large deformation between US image (floating image) and MRI
(reference image), and presents a novel brightness-based algorithm for computation of optical
flow [21, 22]. in this paper presents Automatic segmentation of cardiac cavity images algorithm
to detect and reconstruct the boundary of the cardiac cavity using Collinear and Triangle
Equation [23]. In this paper presents a problem of accurate segmentation of the endocardial
contours of both ventricles and the epicardium of the entire heart using Optimal Features Active
Shape Model (OF-ASM) [24]. In this paper presents a segmentation the human left ventricle
(LV) in ultrasonic images, using Active Shape Models (ASM) and Active Contour Models
(ACM) [25]. Therefore, the new solutions needed to analyze the heart's performance quickly and
accurately. In this study, we will create a system to process the heart image in
echocardiography video to detect the movement of the heart cavity and the extent of the heart
cavity with active shape model and optical flow from input video echocardiography parasternal
short axis.
2. Research Method
In this part, we explain about the procedure to develop segmentation of cardiac image
for heart disease. The procedure which involves Median High Boost Filter, Active Shape Model
and Optical Flow are graphically described in a system diagram is shown Figure 1.
Figure 1. System diagram
Input Video Echocardiography
Median High Boost Filter
Active Shape Model
Optical Flow
Cardiac Cavity Area
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2.1. Input Video Echocardiography
First, the input in this research are Videos from the heart rate movement from
echocardiography test (ultrasound cardiac). In this research, we have used the short axis videos
to test out our proposed segmentation technique. The example of the image comprising in short
axis view from the left ventricular cardiac cavity from echocardiography that represented shown
in a picture Input Video Echocardiography is shown Figure 2.
Figure 2. Input video echocardiography
2.2. Median High Boost Filter
The second step is applying Median High Boost filter to eliminate noise. The new
method which is a combination between median filtering and High Boost Filter called a Median
High Boost Filter that represented shown in a Median High Boost Filter diagram is shown
Figure 3, Sigit [1].
Source Median Filter High Boost Filter Output
Figure 3. Median High Boost Filter
In the implementation of the high boost filter algorithm, Eva [2], which uses the spatial
mask that represented is shown Figure 4 Kernel used for the High Boost Filter. To improve the
high-frequency components without affecting low-frequency components using high boost filter.
Figure 4. Kernel used for the High Boost Filter
2.3. Active Shape Model
After Median High Boost Filter, the next step is doing segmentation. The segmentation
proses is used to analyzed the heart rate motion and helped the system to measure the heart
cavity area. Active Shape Model has two steps, these are training and fitting, Shaleh [4].
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2.3.1. Training
The data extraction phase will produce information from the input data provided in
accordance with the learning outcomes of training stages is the meaning of Training, Shaleh [4].
Training has some steps that represented shown in a diagram Step of Trainin Active Shape
Model is shown Figure 5.
Landmark Point
Alignment Shape
Mean
Standard
Deviation
Eigenvalue and
Eigenvector
Figure 5. Step of Training Active Shape Model
2.3.1.1. Landmark Point
This step used for representation the shape into point. That point is called a “landmark
point” and after we make a point we save the data in .txt file. The data is coordinate from the
frame videos.
𝑋̃ = (𝑥1, 𝑦1, … … , 𝑥 𝑛, 𝑦 𝑛) (1)
The result of landmark point from twenty-five point to make a shape in the next step that
represented shown in picture Landmark Point is shown Figure 6.
Figure 6. Landmark Point
2.3.1.2. Aligment Shape
Landmark point differences in positions too far so do the alignment to the first form by
doing rotating and scaling. The formula can be represented as (2):
(2)
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from the formula. Can be represented the step for the alignment shape in a diagram Alignment
Shape is shown Figure 7.
Input vector data is broken down into x & y points
Input data from a.txt file
Rotate angle at a predertemined value with the first video as a reference
Changing the result of the rotation into the return vector data
Show the shape into the polyline
Figure 7. Alignment Shape
2.3.1.3. Mean
In this step, calculate the mean shape of all the alignment form data by using the
formula below (2):
(3)
2.3.1.4. Standart Deviation
Standard Deviation is getting from the reduction of the alignment shape data with the
mean value. The result of the standard deviation is used to an input of the covariance matrix.
The formula can be represented as (4):
𝑑𝑥𝑖 = 𝑥𝑖 − 𝑥̅ (4)
After calculating Standard Deviation the next step is Covariance. A measure of how
much two different sets of data called covariance. To determine the level of related variables
varies together or how they may use the covariance. The formula can be represented as (5):
(5)
2.3.1.5. Eigenvalue and Eigenvector
To protect the data which have the largest variation corresponds to a linear
transformation of the high-dimensional space to the low dimensional space using the
eigenvector. The section representatives of eigenvectors eigenvalues are. The formula can be
represented as (6):
𝑆𝑝 𝑘 = 𝜆 𝑘 𝑝 𝑘, 𝜆 𝑘 ≥ 𝜆 𝑘+1 (6)
2.3.2. Fitting
In this part, to fit the mean shape result according to the new image using the fittings.
The first t-mode generates a form in training sets can be approximated by using a weighted
summation means and forms of irregularities, Shaleh [4]. The Fitting formula can be
represented by (7):
𝑥 = 𝑥̅ + 𝑃𝑏 (7)
With P=(p1, p2, ..., pt) as the first matrix eigenvectors of t and b=(b1, b1, ..., bt) as a weight
vector to initialize the parameter form b (eigenvector) to zero (meaning form) [4].
The result of fitting that represented shown in picture Fitting is shown Figure 8.
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Figure 8. Fitting
2.4. Optical Flow
To predict the movement of the image based on its intensity in the image sequence is
the optical flow. This will represent how far pixel motion pictures between 2 frames if applied to
a 2D image. One of the methods in computer vision is used for estimating from the optical flow
is the Lucas-Kanade. By combining information from several nearby pixels, Lucas Kanade's
method can resolve the ambiguity, Arvina [5].
The highest layer of the pyramid is the first undertaken by a Pyramidal Lucas-Kanade.
A reference starting point is the result of the Lucas-Kanade who used to work at the lower layer
and continues until the lowest level. Pyramidal Lucas-Kanade algorithm can be described as
follows in accordance with the working paper presented by the Bouquet in 2000 and this is the
steps are taken to find the position of the point on the next frame.
a. Initialization pyramid into I & J
{𝐼 𝐿} 𝐿 = 0, … … , 𝐿𝑚 𝑑𝑎𝑛 {𝐽 𝐿} 𝐿 = 0, … … . , 𝐿𝑚
b. Initialization first value of pyramid
c. Define point of u into I
L
frame
d. Define derivative I
L
into x value
e. Define derivative I
L
into y value
f. Calculate the spatial gradient matrix
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g. initialization iteration of L-K (Lucas Kanade)
𝑣
̅0̅
= [0 0]
h. Search value of the difference image
i. Search value of the image mismatch vector
j. Calculate the optical flow value
𝑛 𝑘̅̅̅ = = 𝐺−1
𝑏 𝑘
̅̅̅
k. initialization vector to the next step
𝑣 𝑘̅̅̅ = 𝑣 𝑘−1̅̅̅̅̅̅ + 𝑛 𝑘̅̅̅
l. Calculate the optical flow value to L level
𝑑𝐿 = 𝒗 𝒌̅̅̅
m. Initialization value of pyramid to next level
n. Calculate the end of optical flow value
𝑑 = 𝑔 𝑜 + 𝑑 𝑜
o. Search coordinate point to J
𝑣 = 𝑢 + 𝑑
2.5. Cardiac Cavity Area
The last step in this research is Monte Carlo to calculate the heart cavity area.
To calculate the area of the heart cavity using Monte Carlo with how to generate a random
sample and then calculate the ratio of the sample between samples in the area of segmented
(nd) with the total sample (ns). Monte Carlo formula can be represented as:
(8)
Partial Monte Carlo is part of Monte Carlo algorithm. Partial Monte Carlo algorithm aims
to improve calculations faster than the normal Monte Carlo by way of dividing borders into some
parts and the search box on the border area, Sigit [1].
3. Results and Analysis
This section describes the final result of the system. Echocardiography video in short
axis view is used as input. The table in this part representation all about the system with some
experiment. Based on Table 1, can see that landmark point result with labeling set make a point
as much 25 points to make a shape. After the training set we do the Alignment Shape to make
some data training into one reference shape.
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Table 1. Landmark Point
Frame
Input Video
Video 1 Video 2
0
1
2
3
Based on Table 2, that can be seen from process calculate mean, standard deviation
etc. We get the real shape from the heart cavity area and from this shape we do the optical flow
to view the heart rate motion in video output to help the doctor analyze the heart patient.
Table 2. Result of Segmentation
Video Result
1 (a) Frame 0 (b) Frame 1
(c) Frame 2 (d) Frame 3
2
(a)
Frame 0 (b) Frame 2
(c) Frame ke-3 (d) Frame ke-4
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Based on Figure 9 the method of ASM and Optical Flow represented the maximum
results so that when done widespread calculations using Monte Carlo method generated graph
data above that is a very small error value.
Figure 9. Chart from calculate the cavity area from the heart
4. Conclusion
The solution method is proposed to help doctors analyze heart disease is
echocardiograph segmentation of videos with the combine any methods. The Median High
Boost Filter is the result of research that is able to reduce noise and used to increase the light
intensity value of the image. The segmentation using Active Shape Model and Optical Flow. The
experimental result is able to detect heart rate-motion and heart cavity area. Methods used in
this paper is effective because it is based on the results obtained is a comparison of the
diagnosis made by the doctor is not much different from the results obtained from this system.
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