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.
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
A new hybrid method for the segmentation of the brain mrissipij
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.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
Hybrid medical image compression method using quincunx wavelet and geometric ...journalBEEI
The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.
Global threshold and region based active contour model for accurate image seg...sipij
In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new
edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or
blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering
regularized level set (SBGFRLS) method. The method uses either a selective local or global segmentation
property. It penalizes the level set function to force it to become a binary function. This procedure is followed by
using a regularisation Gaussian. The Gaussian filters smooth the level set function and stabilises the evolution
process. One of the merits of our proposed model stems from the ability to initialise the contour anywhere inside
the image to extract object boundaries. The proposed method is found to perform well, notably when the
intensities inside and outside the object are homogenous. Our method is applied with satisfactory results on
various types of images, including synthetic, medical and Arabic-characters images.
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
A new hybrid method for the segmentation of the brain mrissipij
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.
MULTIFOCUS IMAGE FUSION USING MULTIRESOLUTION APPROACH WITH BILATERAL GRADIEN...cscpconf
The fusion of two or more images is required for images captured using different sensors,
different modalities or different camera settings to produce the image which is more suitable for
computer processing and human visual perception. The optical lenses in the cameras are having
limited depth of focus so it is not possible to acquire an image that contains all the objects infocus.
In this case we need a Multifocus image fusion technique to create a single image where
all objects are in-focus by combining relevant information in the two or more images. As the
sharp images contain more information than blurred images image sharpness will be taken as
one of the relevant information in framing the fusion rule. Many existing algorithms use
contrast or high local energy as a measure of local sharpness (relevant information). In
practice particularly in multimodal image fusion this assumption is not true. Here in this paper
we are proposing the method which combines the multiresolution transform and local phase
coherence measure to measure the sharpness in the images. The performance of the fusion
process was evaluated with mutual information, edge-association and spatial frequency as
quality metrics and compared with Laplacian pyramid, DWT (Discrete Wavelet Transform) and
bilateral gradient based sharpness criterion methods etc. The results showed that the proposed
algorithm is performing better than the existing ones.
A MORPHOLOGICAL MULTIPHASE ACTIVE CONTOUR FOR VASCULAR SEGMENTATIONijbbjournal
This paper presents a morphological active contour ideal for vascular segmentation in biomedical images.
The unenhanced images of vessels and background are successfully segmented using a two-step
morphological active contour based upon Chan and Vese’s Active Contour without Edges. Using dilation
and erosion as an approximation of curve evolution, the contour provides an efficient, simple, and robust
alternative to solving partial differential equations used by traditional level-set Active Contour models. The
proposed method is demonstrated with segmented data set images and compared to results garnered from
multiphase Active Contour without Edges, morphological watershed, and Fuzzy C-means segmentations.
Hybrid medical image compression method using quincunx wavelet and geometric ...journalBEEI
The purpose of this article is to find an efficient and optimal method of compression by reducing the file size while retaining the information for a good quality processing and to produce credible pathological reports, based on the extraction of the information characteristics contained in medical images. In this article, we proposed a novel medical image compression that combines geometric active contour model and quincunx wavelet transform. In this method it is necessary to localize the region of interest, where we tried to localize all the part that contain the pathological, using the level set for an optimal reduction, then we use the quincunx wavelet coupled with the set partitioning in hierarchical trees (SPIHT) algorithm. After testing several algorithms we noticed that the proposed method gives satisfactory results. The comparison of the experimental results is based on parameters of evaluation.
Global threshold and region based active contour model for accurate image seg...sipij
In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new
edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or
blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering
regularized level set (SBGFRLS) method. The method uses either a selective local or global segmentation
property. It penalizes the level set function to force it to become a binary function. This procedure is followed by
using a regularisation Gaussian. The Gaussian filters smooth the level set function and stabilises the evolution
process. One of the merits of our proposed model stems from the ability to initialise the contour anywhere inside
the image to extract object boundaries. The proposed method is found to perform well, notably when the
intensities inside and outside the object are homogenous. Our method is applied with satisfactory results on
various types of images, including synthetic, medical and Arabic-characters images.
External Force for Deformable Models in Medical Image Segmentation: A Surveysipij
Many image segmentation techniques are available in the literature. Among the available techniques, parametric deformable models play an important role in many medical imaging applications. These models have been proved to be effective in segmenting an anatomic structure using constraints derived from the image data along with a prior knowledge about the location, size and shape of these structures. These models also support highly intuitive interaction mechanisms, which helps medical practitioners to bring their expertise to bear on the model-based image interpretation task. In this paper, a review of nineteen different external forces for the parametric deformable model applied to the medical image segmentation is presented. The main purpose of survey is to identify and discuss each category with its principle, mathematical model, advantages, disadvantages and applications to medical image analysis.
Weighted Performance comparison of DWT and LWT with PCA for Face Image Retrie...cscpconf
This paper compares the performance of face image retrieval system based on discrete wavelet
transforms and Lifting wavelet transforms with principal component analysis (PCA). These
techniques are implemented and their performances are investigated using frontal facial images
from the ORL database. The Discrete Wavelet Transform is effective in representing image
features and is suitable in Face image retrieval, it still encounters problems especially in
implementation; e.g. Floating point operation and decomposition speed. We use the advantages
of lifting scheme, a spatial approach for constructing wavelet filters, which provides feasible
alternative for problems facing its classical counterpart. Lifting scheme has such intriguing
properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image
retrieval. Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives
less computation and DWT-PCA gives high retrieval rate.. Especially ‘sym2’ wavelet
outperforms well comparing to all other wavelets.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
Face recognition based on curvelets, invariant moments features and SVMTELKOMNIKA JOURNAL
Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production.
Comparative analysis and implementation of structured edge active contour IJECEIAES
This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image. In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared. It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
In the near future, there is an eminent demand for High Resolution images. In order to fulfil this
demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more
Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR
image in that set and combine the information into a single HR image. Conventional interpolation methods can
produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome
the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically
verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily,
outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable
for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim.
Image fusion technology is also used to fuse two processed images obtained through the algorithm
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
Abstract: In the near future, there is an eminent demand for High Resolution images. In order to fulfil this demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR image in that set and combine the information into a single HR image. Conventional interpolation methods can produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily, outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim. Image fusion technology is also used to fuse two processed images obtained through the algorithm. Keywords: Super Resolution, Interpolation, EESM, Image Fusion
Adaptive Multiscale Stereo Images Matching Based on Wavelet Transform Modulus...CSCJournals
In this paper we propose a multiscale stereo correspondence matching method based on wavelets transform modulus maxima. Exploitation of maxima modulus chains has given us the opportunity to refine the search for corresponding. Based on the wavelet transform we construct maps of modules and phases for different scales, then extracted the maxima and then we build chains of maxima. Points constituents maxima modulus chains will be considered as points of interest in matching processes. The availability of all its multiscale information, allows searching under geometric constraints, for each point of interest in the left image corresponding one of the best points of constituent chains of the right image. The experiment results demonstrate that the number of corresponding has a very clear decrease when the scale increases. In several tests we obtained the uniqueness of the corresponding by browsing through the fine to coarse scales and calculations remain very reasonable. Abdelhak EZZINE aezzine@uae.ac.ma 39 imm serghiniya Rue liban ENSAT/ SIC/LABTIC Abdelmalek ESSAADI University Tangier, 99000, Morocco
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
Image segmentation plays a vital role in image processing over the last few years. The goal of image
segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
without re- initialization to segment the MRI image and to implement a competent medical diagnosis
system by using MATLAB. Here we have used the speed function and the signed distance function of the
image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique
and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising
results by detecting the normal or abnormal condition specially the existence of tumers. This system will be
applied to both simulated and real images with promising results.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
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.
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External Force for Deformable Models in Medical Image Segmentation: A Surveysipij
Many image segmentation techniques are available in the literature. Among the available techniques, parametric deformable models play an important role in many medical imaging applications. These models have been proved to be effective in segmenting an anatomic structure using constraints derived from the image data along with a prior knowledge about the location, size and shape of these structures. These models also support highly intuitive interaction mechanisms, which helps medical practitioners to bring their expertise to bear on the model-based image interpretation task. In this paper, a review of nineteen different external forces for the parametric deformable model applied to the medical image segmentation is presented. The main purpose of survey is to identify and discuss each category with its principle, mathematical model, advantages, disadvantages and applications to medical image analysis.
Weighted Performance comparison of DWT and LWT with PCA for Face Image Retrie...cscpconf
This paper compares the performance of face image retrieval system based on discrete wavelet
transforms and Lifting wavelet transforms with principal component analysis (PCA). These
techniques are implemented and their performances are investigated using frontal facial images
from the ORL database. The Discrete Wavelet Transform is effective in representing image
features and is suitable in Face image retrieval, it still encounters problems especially in
implementation; e.g. Floating point operation and decomposition speed. We use the advantages
of lifting scheme, a spatial approach for constructing wavelet filters, which provides feasible
alternative for problems facing its classical counterpart. Lifting scheme has such intriguing
properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image
retrieval. Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives
less computation and DWT-PCA gives high retrieval rate.. Especially ‘sym2’ wavelet
outperforms well comparing to all other wavelets.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
Face recognition based on curvelets, invariant moments features and SVMTELKOMNIKA JOURNAL
Recent studies highlighted on face recognition methods. In this paper, a new algorithm is proposed for face recognition by combining Fast Discrete Curvelet Transform (FDCvT) and Invariant Moments with Support vector machine (SVM), which improves rate of face recognition in various situations. The reason of using this approach depends on two things. first, Curvelet transform which is a multi-resolution method, that can efficiently represent image edge discontinuities; Second, the Invariant Moments analysis which is a statistical method that meets with the translation, rotation and scale invariance in the image. Furthermore, SVM is employed to classify the face image based on the extracted features. This process is applied on each of ORL and Yale databases to evaluate the performance of the suggested method. Experimentally, the proposed method results show that our system can compose efficient and reasonable face recognition feature, and obtain useful recognition accuracy, which is able to face and side-face states detection of persons to decrease fault rate of production.
Comparative analysis and implementation of structured edge active contour IJECEIAES
This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image. In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared. It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
In the near future, there is an eminent demand for High Resolution images. In order to fulfil this
demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more
Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR
image in that set and combine the information into a single HR image. Conventional interpolation methods can
produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome
the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically
verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily,
outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable
for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim.
Image fusion technology is also used to fuse two processed images obtained through the algorithm
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
Abstract: In the near future, there is an eminent demand for High Resolution images. In order to fulfil this demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR image in that set and combine the information into a single HR image. Conventional interpolation methods can produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily, outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim. Image fusion technology is also used to fuse two processed images obtained through the algorithm. Keywords: Super Resolution, Interpolation, EESM, Image Fusion
Adaptive Multiscale Stereo Images Matching Based on Wavelet Transform Modulus...CSCJournals
In this paper we propose a multiscale stereo correspondence matching method based on wavelets transform modulus maxima. Exploitation of maxima modulus chains has given us the opportunity to refine the search for corresponding. Based on the wavelet transform we construct maps of modules and phases for different scales, then extracted the maxima and then we build chains of maxima. Points constituents maxima modulus chains will be considered as points of interest in matching processes. The availability of all its multiscale information, allows searching under geometric constraints, for each point of interest in the left image corresponding one of the best points of constituent chains of the right image. The experiment results demonstrate that the number of corresponding has a very clear decrease when the scale increases. In several tests we obtained the uniqueness of the corresponding by browsing through the fine to coarse scales and calculations remain very reasonable. Abdelhak EZZINE aezzine@uae.ac.ma 39 imm serghiniya Rue liban ENSAT/ SIC/LABTIC Abdelmalek ESSAADI University Tangier, 99000, Morocco
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
Image segmentation plays a vital role in image processing over the last few years. The goal of image
segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
without re- initialization to segment the MRI image and to implement a competent medical diagnosis
system by using MATLAB. Here we have used the speed function and the signed distance function of the
image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique
and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising
results by detecting the normal or abnormal condition specially the existence of tumers. This system will be
applied to both simulated and real images with promising results.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
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.
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.
ResNet-n/DR: Automated diagnosis of diabetic retinopathy using a residual neu...TELKOMNIKA JOURNAL
Diabetic retinopathy (DR) is a progressive eye disease associated with diabetes, resulting in blindness or blurred vision. The risk of vision loss was dramatically decreased with early diagnosis and treatment. Doctors diagnose DR by examining the fundus retinal images to develop lesions associated with the disease. However, this diagnosis is a tedious and challenging task due to growing undiagnosed and untreated DR cases and the variability of retinal changes across disease stages. Manually analyzing the images has become an expensive and time-consuming task, not to mention that training new specialists takes time and requires daily practice. Our work investigates deep learning methods, particularly convolutional neural network (CNN), for DR diagnosis in the disease’s five stages. A pre-trained residual neural network (ResNet-34) was trained and tested for DR. Then, we develop computationally efficient and scalable methods after modifying a ResNet-34 with three additional residual units as a novel ResNet-n/DR. The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset was used to evaluate the performance of models after applying multiple pre-processing steps to eliminate image noise and improve color contrast, thereby increasing efficiency. Our findings achieved state-of-the-art results compared to previous studies that used the same dataset. It had 90.7% sensitivity, 93.5% accuracy, 98.2% specificity, 89.5% precision, and 90.1% F1 score.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Adaptive segmentation algorithm based on level set model in medical imaging
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 5, October 2023, pp. 1130~1138
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i5.22365 1130
Journal homepage: http://telkomnika.uad.ac.id
Adaptive segmentation algorithm based on level set model in
medical imaging
Boualem Mansouri1
, Abdelkader Khobzaoui2
, Mehdi Damou1
, Mohammed Chetioui1
,
Abdelhakim Boudkhil1
1
Laboratory of Electronics, Advanced Signal Processing and Microwaves (LESM), Faculty of Technology, TM University of Saida,
Saida, Algéria
2
Mathematics Laboratory, Faculty of Exact Sciences, Djillali LIABES University of Sidi Bel Abbes, Algéria
Article Info ABSTRACT
Article history:
Received Dec 04, 2021
Revised Jan 01, 2023
Accepted Feb 16, 2023
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.
Keywords:
Active contour
Anisotropic diffusion
Euler’s equation
Medical image segmentation
Variational level set
This is an open access article under the CC BY-SA license.
Corresponding Author:
Boualem Mansouri
Laboratory of Electronics, Advanced Signal Processing and Microwaves (LESM)
Faculty of Technology, TM University of Saida, Saida, Algéria
Email: mansourieln@yahoo.fr
1. INTRODUCTION
In the field of imaging, the process of image segmentation involves splitting image into areas sharing
the same properties. The task of image segmentation is very important in process of medical image treatment
or analysis such as in computer-aided diagnostic (CAD) systems. Several CAD systems that work on medical
images [1] have successfully applied segmentation. In the traditional way, clustering approaches, such fuzzy
mean, are used to achieve image segmentation [2] and many manually created low-level features, like pixel
value distribution and gradient histogram, can be clustered using a genetic algorithm [3]. In image
segmentation, probabilistic techniques are also frequently employed [4]−[6]. In [7] a framework for regression
segmentation is proposed is proposed for detection of vascular abnormalities in cardiac magnetic resonance
imaging by delimiting the two ventricles’ boundaries. Among the primary difficulties in using automatic
medical image segmentation for magnetic resonance imaging (MRI) and computed tomography (CT) scans is
the defect with imaging process that frequently lead to inconsistencies brightness and contrast levels as well as
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low sharpness of image of borders. On the other hand, deformable active contours are an efficient tool for
image segmentation and pattern recognition [8]−[11] and represents explicitly the object’s shape and boundary,
they combine many souhaible characteristics. Level set models are also known as geometric deformable
models, rovide better solutions to overcom the main drawbacks of parametric deformable models.
The idea of the level set method is based on the initialization of a two dimensional (2D) closed curve,
or a three-dimensional surface this curve has a potential that allows it shifting at a given speed perpendicular
to itself [12]. The level set approach is employed in image processing as a segmentation tool through the
evolution of a contour utilizing the image properties. We represent an interface 𝐶 known in this approach as a
level set function of higher dimension. We define this level set over the rest of the image as the signed distance
function from the zero level set. Conventionally, this distance takes positive values for pixels inside 𝐶 and
negative values for pixels outside 𝐶. Unfortunately, the level set function frequently develops irregularities
during its evolution and thus causes numerical errors which reaches the stability of the level set evolution,
a numerical solution, known as reinitialization [13], [14], is introduced to overcome this undesirable situation
and maintain stable level set evolution, but the problem that arises when applying reinitialization is how and
when it ought to be carried out which affects the numerical precision. In [15], distance regularized level set
evolution is a novel sort of level set evolution that Li et al. [15] proposed in which level set model is presented
by the following formulation in distance regularized level set evolution.
2. LEVEL SET FORMULATION WITH DISTANCE REGULARIZED
Considering the following equation:
Ԑ(∅) = 𝜇𝑅𝑝(∅) + Ԑ𝑒𝑥𝑡(∅) (1)
Where 𝜇 > 0 is constant, 𝜀𝑒𝑥𝑡(∅) represent the external energy and 𝑅𝑝(∅) is the level set regularization term
which was also called penalty term defined by:
𝑅𝑝(∅) ≜ ∫𝛺
𝑝(|𝛻∅|)𝑑𝑥 (2)
Here 𝑝 represent a potential function 𝑝: [0 ∞] → . To maintain such a profile of the level set function, the
potential function 𝑝(𝑠) must have minimum points at 𝑠 = 0 and 𝑠 = 1, 𝑝(𝑠) is a double-well potential since it
has two minimum points defined as:
𝑝2(s) = {
1
(2ᴨ)2 (1 − cos(2ᴫ𝑠)) if s ≤ 1
1
2
(s − 1)2
, if s ≥ 1
(3)
Where 𝑑𝑝(𝑠) = 𝑝2
′
(𝑠)/𝑠 satisfies |𝑑𝑝(𝑠)| < 1 and lim
𝑠→0
𝑑𝑝(𝑠) = lim
𝑠→∞
𝑑𝑝(𝑠) = 1. Here 𝑝2’(𝑠) is the derivative
of 𝑝2(𝑠). Consequently |𝜇𝑑𝑝(|𝛻∅|| ≤ 𝜇, which confirms the diffusion rate’s boundedness for the potential 𝑝2.
We can write (1) as:
𝜕Ԑ
𝜕∅
= 𝜇
𝜕𝑅𝑝
𝜕∅
+
𝜕Ԑ𝑒𝑥𝑡
𝜕∅
(4)
Where
𝜕𝜀𝑒𝑥𝑡
𝜕∅
is the external energy functional’s Gâteaux derivatives and
𝜕𝑅𝑝
𝜕∅
is the level set regularization. Using
the following evolution equation:
𝜕∅
𝜕𝑡
= −
𝜕𝜀
𝜕∅
(5)
The energy’s gradient flow becomes:
𝜕∅
𝜕𝑡
= −𝜇
𝜕𝑅𝑝
𝜕∅
−
𝜕𝜀𝑒𝑥𝑡
𝜕∅
(6)
Knowing that
𝜕𝑅𝑝
𝜕∅
= −𝑑𝑖𝑣(𝑑𝑝(|𝛻∅|)𝛻∅) the (1) becomes:
∂∅
∂t
= 𝜇𝑑𝑖𝑣(𝑑𝑝(|𝛻∅|)𝛻∅) −
𝜕Ԑ𝑒𝑥𝑡
𝜕∅
(7)
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The level set evolution (7) is known as a distance regularized level set evolution, this formulation can
be used in image segmentation application using edge-based information 𝑔. In this case a functional energy
𝜀(∅) is defined by:
Ԑ(∅) = 𝜇𝑅𝑝(∅) + 𝜆ℒ𝑔(∅) + 𝛼𝒜𝑔(∅) (8)
Where 𝜆 > 0, 𝛼𝜖ℛ constants, the terms ℒ𝑔(∅) and 𝒜𝑔(∅) are defined by: ℒ𝑔(∅) ≜ ∫Ω
𝑔𝛿(∅)|∇∅|𝑑𝑥
and 𝒜𝑔 ≜ ∫Ω
𝑔𝐻(−∅)𝑑𝑥. Where is the Dirac delta function and 𝐻 represent the Heaviside function.
The functional energy 𝜀(∅)can be minimized by solving the following gradient flow:
𝜕∅
𝜕𝑡
= 𝜇𝑑𝑖𝑣(𝑑𝑝(|𝛻∅|)𝛻∅) + 𝜆𝛿(∅)𝑑𝑖𝑣 (𝑔
𝛻∅
|𝛻∅|
) + 𝛼𝑔𝛿(∅) (9)
Model in (9) is an edge-based geometric active contour, which is an image segmentation application
of the general distance regularized level set evolution (8). According to the theory presented above, the distance
regularization effect eliminates the need for reinitialization and therefore avoids its induced numerical errors.
The diffusion rate is transformed into a bounded constant by optimizing the penalty term’s function, and
adequate numerical precision was achieved. Unfortunately, this model could not escape the following
inconvenients:
a) When using real medical images or noisy images, this model will produce blurred edge since it utilizes a
Gaussian filter to decrease the noise.
b) This model cannot segment in a correct way because it must artificially determine the model’s constant
evolution speed’s symbol based on the location of the initial curve.
c) The background boundaries and target boundaries are not distinguished by the edge indicator function 𝑔,
however, in a single image, the target boundaries and background boundaries typically have completely
different gradient directions.
To overcome these disadvantages, several methods are proposed for example paper [16] demonstrat,
both theoretically and experimentally, that indirect regularization has some advantages over direct
regularization, Yu et al. [17] suggest novel active contour model (R-DRLSE model) for image segmentation
and Young et al. [18] develop a new approach to contour evolution. Liu and Xu [19] propose oriented distance
regularized level evolution and Cai [20] propose a coupled model for image segmentation and restoration.
Messaouda et al. [21] present a novel level set method driven by new signed pressure force function
(SPF) for image segmentation. In this paper a novel method is proposed to simultaneously solve a two-dimensional
partial differential equations (PDE) system, make a compromise between image restorations and keep edges and
correct segmentation. The first PDE of the system allows the restoration of the image by adopting regularized
Perona and Malik equation filter that removes noise and preserves edge information in accordance with the
detected contours in the second PDE, the second equation is based on level set model which uses the evolution of
a curve propagating in a plane of its normal with a given speed.
This evolution is guided by a function that allows to stop the curve on the edges of objects to be detected
in the image restored by the first equation [22]. This paper is organized as: after presenting the introduction in this
section, the proposed method will be presented in section 3. Section 4 deals with simulation experiments that
justify the paper contribution in applied field. Section 5 provides a conclusion for the achieved results.
3. METHOD
Usually, in all the active contour models, an edge detector is used to stop the evolving curve on the
boundaries of the desired object. This is a positive and regular edge-function 𝑔(⎸𝛻𝑓 ⎸). Where lim
𝑡→∞
𝑔(𝑡) = 0
and 𝑔(⎸𝛻𝑓 ⎸) =
1
1+|𝛻𝐺𝜎∗𝑓|2 with 𝐺𝜎(𝑥, 𝑦) = √𝜎 exp (−
|𝑥2+𝑦2|
4𝜎
). Where 𝐺𝜎 × 𝑓 is the convolution of the image
𝑓 with the Gaussian kernel (𝐺, 𝜎), which give a smoother version of the image. The edge-function 𝑔(⎸𝛻𝑓⎸) is
strictly positive in homogeneous regions, and near zero on the edges. All these classical active contour models
are based on this edge-function which depend to the gradient of the image to stop the curve evolution. But
during the implementation of those methods that the discrete gradients are limited and then the stopping
function 𝑔(⎸𝛻𝑓⎸) is never zero at the edges, and the curve may exceed the limits. In other words, if the image
is heavily noisy, then the smoothing process has to be strong, which will smooth the edges too. To resolve this
problem, a new approach is proposed to overcome the disadvantages of this model.
The aim of this approach is to unify the image restoration and segmentation to achieve those two tasks
at the same time. Often with a Gaussian kernel (𝐺, 𝜎) the choice of the variance σ is difficult: if the smoothing
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is too large, the edges of the image is lost; if the smoothing is too low, the spread of the curve is determined by
the noise before the contours are achieved. So, the results are not always satisfactory in this kind of filtering,
which adversely affects the results of segmentation.
In order to estimate image 𝑓 and reducing image noise while preserving edge details which facilitates
a correct segmentation, a new method is proposed to jointly perform a restoration using an anisotropic
smoothing based on Euler’s equation as well as make the restoration results more continuous and smooth [23].
For this, a regularized method with contour preservation is used [24]. These contours are detected by the
segmentation performed at the same time. In this case we can estimate the image 𝑓 using the following PDE as:
𝐻∗(𝐻𝑓 − 𝑦) + 𝜆𝑑𝑖𝑣(𝐾𝛻𝑓) = 0 (10)
Where 𝑓 and 𝑦 denote vectors containing the true and the observed image respectively, 𝐻 is the
observation matrix and 𝜆 is a hyper-parameter, or regularization parameter and 𝐾 allows the preservation of
discontinuities. Euler’s (10) is the PDE associated with the minimization of the criterion.
𝐽(𝑓) = ∫|𝐻𝑓 − 𝑦|2
+ 𝜆2
∫ 𝜑(|∇𝑓)| (11)
𝜑 is a regularizing function; in this case:
𝐾 =
𝜑′(|∇𝑓|)
2|∇𝑓|
(12)
In equation (10) is similar to the anisotropic diffusion in [25], [26], in which 𝐾 = 𝑐(|∇𝑓|) is the
coefficient of heat transmission. For our application, 𝐾 depends on the contours calculated by (1). We have
then, 𝐾 = 𝑘(∅) where the function 𝑘 satisfies the following conditions: 𝑘(∅) is close to 0 near 𝐶 (𝐶 is
represented as a level set of a function ∅) and near 1 elsewhere.
The function 𝑘 evolves at the same time that the algorithm converges. Initially, the contour determined by
𝐶 is not well localized, 𝑘 is then a blurred version of ∅ so 𝑘(∅) away from 𝐶 and slowly decreases to 0 near 𝐶. Then,
as the convergence of the algorithm advance, 𝐶 tends toward the contours of objects and 𝑘 tends to a Boolean
function where 𝑘(∅) = 0 on 𝐶 (the contours) and 𝑘(∅) = 1 on homogeneous areas of the image. We use then a
continuous function that checks:
{
𝑘(∅) = 1 𝑖𝑓∅ ≥ 𝑒
𝑘(∅)𝑙𝑖𝑛𝑒𝑎𝑟𝑒 0 < ∅ < 𝑒
𝑘(0) = 1 −
1
𝑒
(13)
The 𝑒 decreases towards 1 as and as the algorithm evolves. The end result is a Boolean function if
𝑒 = 1. By coupling (9) with (10), the new system of two PDE is:
{
𝜕𝑓
𝜕𝑡
= 𝐻∗(𝑦 − 𝐻𝑓) + 𝜆𝑑𝑖𝑣(𝑘(∅)𝛻𝑓) (𝑎)
𝜕∅
𝜕𝑡
= 𝜇𝑑𝑖𝑣(𝑑𝑝(|𝛻∅|)𝛻∅) + 𝜆𝛿(∅)𝑑𝑖𝑣 (𝑔
𝛻∅
|𝛻∅|
) + 𝛼𝑔𝛿(∅) (𝑏)
(14)
With the boundary conditions defined previously and the edge stop function 𝑔(⎸𝛻𝑓) =
1
1+|𝛻𝑓 /γ|2 .
Where 𝛾 is a parameter which sets a threshold on the gradient of the objects to be detected.
Proposed Algorithm
The proposed algorithm consists of solving the system of two PDE:
− (14.a) processes the image 𝑓 according to 𝜙
− (14.b) processes the image distances 𝜙 of 𝑐 according to 𝑓
Those two PDE are alternatevly resolved as:
Initialization 𝑓0 = 0 ; 𝜙0 = signed distances of C0
Repeat
Iterate (14.a) until convergence on 𝑓, with 𝜙 fixed
Iterate (14.b) until convergence on 𝜙, with 𝑓 fixed
Repeat until convergence on 𝑓 and 𝜙.
End process
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4. RESULTS AND DISCUSSION
The spot’s contour represents a very important characteristic in medical images. The extracted
pathology’s contour can help doctors to quantitate the spots, analyses the pathology, and conclude the
diagnosis. In this experiment several medical images are used to check the robustness of the proposed approach.
In the first experimental stage, the distance regularized level set evolution model is used for an application of
the segmentation. In the second one, the proposed approach is applied considering the following parameters:
𝜇, 𝜆 and 𝛼 for this model, and time delay ∆𝑡 for the implementation. Seting 𝜆 = 5, 𝜇 = 0.04, ∆𝑡 = 10 and 𝛼
is variable depends on the image used.
Figure 1(a) to Figure 1(c) and Figure 2(a) to Figure 2(c) show employed medical images representing
pathologies. Figure 1 represents a tumor of the liver, seen asa black spot. Image in Figure 2 represents a
particular real medical image of GE system database, this image show the sagittal T1-weighted brain registered
through an MRI scanner, contains black spots represent tumors. We see that level set model fail to settle on the
correct boundary see Figure 1, and Figure 2, but the application of our proposed approach have been successful
to detect the real edges see Figure 1 and Figure 2.
Taking at the results from level set model, we observe that this method can’t give satisfactory results for
such images. For further confirmation of the efficiency of our approach, we have tested our algorithm on two
other images whose segmentation results are presented in Figure 3 including the three sub-figures Figure 3(a) to
Figure 3(c). In Figure 4(a) to Figure 4(c) a heart’s MRI image is used. From Figure 4(a) to Figure 4(c) and
Table 1, it can be deduced that the proposed algorithm protects the edge information, needs less iteration times.
Compared to other models, the proposed approach extracts the contour with greater accuracy.
(a) (b) (c)
Figure 1. Results of segmentation using level set model and proposed approach: (a) the input image
with initial contour; (b) image segmented with level set model; and (c) image segmented with
proposed approach
(a) (b) (c) (d)
Figure 2. Results of segmentation using level set model and proposed approach: (a) the input image with
pathology that we want to segment; (b) initial contour; (c) image segmented with level set; and (d) image
segmented with proposed approach
Figure 5(a), Figure 5(b), Figure 5(c), and Figure 5(d) presents results of the segmentation of the
hippocampus correspond to a subject with Alzheimer’s in advanced stage. From Figure 6(a), Figure 6(b),
Figure 6(c), and Figure 6(d), it can be conclude that the proposed algorithm has the ability to provide good
image segmentation. which allows to reach a great contour accuracy for noisy medical images.
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(a) (b) (c)
Figure 3. Results of segmentation using level set and proposed approach: (a) the input images; (b) image
segmented with level set; and (c) image segmented with proposed approach
(a) (b) (c)
Figure 4. Results of segmentation: (a) the input image; (b) image segmented with level set; and (c) image
segmented with proposed approach
Table1. Data of experiments presented in Figure 4
Segmentation methods Initial contour Iteration Cost Time Segmentation state
Level set Internal − 680 90 s Not
Proposed approach Internal 178 − 17 s Achieved
(a) (b) (c) (d)
Figure 5. The results of the segmentation of the hippocampus correspond to a subject with Alzheimer’s
(advanced stage): (a) the input image; (b) manual image segmentation; (c) image segmented with lev set;
and (d) image segmented with our proposed approach
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(a) (b)
(c) (d)
Figure 6. Evolution results of the level with DRLSE method, the ODRLSE method and our approach: (a) initial
image (an ultrasound image of liver tumor); (b) segmentation result DRLSE; (c) segmentation result ODRLSE
method; and (d) segmentation result in proposed approach
5. CONCLUSION
In this paper, a level set method is used with distance regularized level set evolution applied on real
medical images to detect pathologies. After several experiments, results still not satisfactory for the studied
model because there is a trade-off between the noise elimination rate in image and good segmentation results.
The proposed algorithm has the ability to provide good image segmentation. It shows great accuracy in
extracting the contours of noisy medical images that allows reducing human intervention in the segmention
process through applying the proposed approach to computer medical diagnosis to help improving image
interpretation and investigation.
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BIOGRAPHIES OF AUTHORS
Boualem Mansouri received the Eng degree in electronic engineering from
UDL University, Algeria, in 1992, Magister degree in 2007 and Ph.D degree in signal and
telecommunication from UDL University, Algeria, in 2014. Currently, he is an Associate
Professor at the Department of Electronics of Saida University. His research interests include
electronics, image processing, medical instruments and artificial intelligence applied in
medical ingeneering. He is working on investigating inverse problems, image segmentation,
Bayesian inference and vision. He can be contacted at email: mansourieln@yahoo.fr.
Abdelkader Khobzaoui holds an engineering diploma and a PhD in computer
science from Djillali Liabes University (Sidi-Belabbes, Algeria). He is currently Associate
Professor and Head of the Computer Science Department at the same university. His research
interests include Data Mining, machine learning, artificial intelligence, (medical) image
processing, cryptography, IoT and network security. He can be contacted at email:
akhobzaoui@yahoo.fr.
9. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 5, October 2023: 1130-1138
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Mehdi Damou received the doctorate of ES-science degree in
Telecommunications from Abu Bakr Belkaid University of Tlemcen, Algeria in 2017. He is
a lecturer and the Head of Electronics department at Dr. Tahar Moulay University of Saida,
Algeria. His research interests include microwave and RF devices and components. He is
working on developing antennas and filters based on SIW technologies and efficient EM
modeling techniques. He can be contacted at email: bouazzamehdi@yahoo.fr.
Mohammed Chetioui received the doctorate of ES-Sc degree in
Telecommunications from Abu Bakr Belkaid University of Tlemcen, Algeria in 2018. He is
a lecturer at Electronics department of Dr. Tahar Moulay University of Saida, Algeria since
2008. His research interests include digital signal processing, microwave devices and circuits
andartificial intelligence applied in MW/RF systems. He is working on designing
passive/active microwave devices such as filters and antennas using recent microstrip
technology and accurate optimization techniques. He can be contacted at email:
chetioui.mohammed@yahoo.fr.
Abdelhakim Boudkhil received the doctorate of ES-science degree in
Electronics from Abu Bakr Belkaid University of Tlemcen, Algeria in 2018. He is an assistant
professor at Electronics department at Dr. Tahar Moulay University of Saida, Algeria. His
research experience concerns several fields including digital, optical, microwave, and RF
communication systems. His area of interests focuses more on optimizing and developing
antennas based on integrated technology and advanced techniques. He can be contacted at
email: boudkhil.abdelhakim@yahoo.fr.