The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
41 9147 quantization encoding algorithm based edit tyasIAESIJEECS
In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
Efficient contrast enhancement using gamma correction with multilevel thresho...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The Effectiveness and Efficiency of Medical Images after Special Filtration f...Editor IJCATR
There are many factors which have influences on the quality of medical images, so this paper gives a brief narration on the important techniques that produce acceptable quality to medical images. To ensure the validity of this techniques towards medical images, a questionnaire was designed and distributed to a number of doctors and professionals. The survey aims to assess the medical image specialists by regarding their point of views towards the impact of filtering medical images after processing using these techniques. MatLab package used to apply the techniques.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
41 9147 quantization encoding algorithm based edit tyasIAESIJEECS
In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.
CONTRAST ENHANCEMENT TECHNIQUES USING HISTOGRAM EQUALIZATION METHODS ON COLOR...IJCSEA Journal
Histogram equalization (HE) is a simple and widely used image contrast enhancement technique. The basic disadvantage of HE is it changes the brightness of the image. In order to overcome this drawback, various HE methods have been proposed. These methods preserves the brightness on the output image but, does not have a natural look. In order to overcome this problem the, present paper uses Multi-HE methods, which decompose the image into several sub images, and classical HE method is applied to each sub image. The algorithm is applied on various images and has been analysed using both objective and subjective assessment.
Efficient contrast enhancement using gamma correction with multilevel thresho...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The Effectiveness and Efficiency of Medical Images after Special Filtration f...Editor IJCATR
There are many factors which have influences on the quality of medical images, so this paper gives a brief narration on the important techniques that produce acceptable quality to medical images. To ensure the validity of this techniques towards medical images, a questionnaire was designed and distributed to a number of doctors and professionals. The survey aims to assess the medical image specialists by regarding their point of views towards the impact of filtering medical images after processing using these techniques. MatLab package used to apply the techniques.
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
HUMAN VISION THRESHOLDING WITH ENHANCEMENT FOR DARK BLURRED IMAGES FOR LOCAL ...cscpconf
There are several images that do not have uniform brightness which pose a challenging problem
for image enhancement systems. As histogram equalization has been successfully used to correct
for uniform brightness problems, a histogram equalization method that utilizes human visual
system based thresholding(human vision thresholding) as well as logarithmic processing
techniques were introduced later . But these methods are not good for preserving the local
content of the image which is a major factor for various images like medical images.Therefore
new method is proposed here. This method is referred as “Human vision thresholding with
enhancement technique for dark blurred images for local content preservation”. It uses human
vision thresholding together with an existing enhancement method for dark blurred images.
Experimental results shows that the proposed method outperforms the former existing methods in
preserving the local content for standard images and medical images
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
Image Contrast Enhancement for Brightness Preservation Based on Dynamic Stret...CSCJournals
Histogram equalization is an efficient process often employed in consumer electronic systems for image contrast enhancement. In addition to an increase in contrast, it is also required to preserve the mean brightness of an image in order to convey the true scene information to the viewer. A conventional approach is to separate the image into sub-images and then process independently by histogram equalization towards a modified profile. However, due to the variations in image contents, the histogram separation threshold greatly influences the level of shift in mean brightness with respect to the uniform histogram in the equalization process. Therefore, the choice of a proper threshold, to separate the input image into sub-images, is very critical in order to preserve the mean brightness of the output image. In this research work, a dynamic range stretching approach is adopted to reduce the shift in output image mean brightness. Moreover, the computationally efficient golden section search algorithm is applied to obtain a proper separation into sub-images to preserve the mean brightness. Experiments were carried out on a large number of color images of natural scenes. Results, as compared to current available approaches, showed that the proposed method performed satisfactorily in terms of mean brightness preservation and enhancement in image contrast.
Optimized Histogram Based Contrast Limited Enhancement for Mammogram ImagesIDES Editor
Detection of breast cancer in its early stage is very
important in the field of medicine. Optimal Contrast
Enhancement is essential for the detection of mass and micro
calcification in mammogram images. The standard histogram
equalization is effective and simple method for contrast
enhancement but for medical images most of the time it
produces excessive contrast enhancement due to lack of control
for the level of enhancement. In this paper image
enhancement is considered as an optimization problem and
an optimization technique based on entropy and edge
information of the image is presented. The enhancement
function used in the paper is Contrast Limited Adaptive
Histogram Equalization (CLAHE) based on local contrast
modification (LCM). Its enhancement potential is tested by
sobel operator for the detection of microcalcification. Results
are compared with other enhancement techniques such as
Histogram Equalization, Unsharp Masking and CLAHE.
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
Intensity Preserving Cast Removal in Color Images Using Particle Swarm Optimi...IJECEIAES
In this paper, we present an optimal image enhancement technique for color cast images by preserving their intensity. There are methods which improves the appearance of the affected images under different cast like red, green, blue etc but up to some extent. The proposed color cast method is corrected by using transformation function based on gamma values. These optimal values of gamma are obtained through particle swarm optimization (PSO). This technique preserves the image intensity and maintains the originality of color by satisfying the modified gray world assumptions. For the performance analysis, the image distance metric criteria of CIELAB color space is used. The effectiveness of the proposed approach is illustrated by testing the proposed method on color cast images. It has been found that distance between the reference image and the corrected proposed image is negligible. The calculated value of image distance depicts that the enhanced image results of the proposed algorithm are closer to the reference images in comparison with other existing methods.
Fast Segmentation of Sub-cellular OrganellesCSCJournals
Segmentation and counting sub-cellular structure is a very challenging problem even for medical experts. A fast and efficient method for segmentation and counting of sub-cellular structure is proposed. The proposed method uses a hybrid combination of several image processing techniques and is effective in segmenting the sub-cellular structures in a fast and effective manner.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
En noviembre de 2015, a petición de la asociación de alumnos UniON Campus, acudí a darles una breve charla sobre las posibilidades de la gamificación en marketing online.
Comunicado primera reunion regional autonomaerik arellana
Víctimas y organizaciones de derechos humanos hacen un llamado a las partes a agilizar la implementación de las medidas inmediatas de búsqueda de personas desaparecidas
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
Image enhancement is one of the challenging issues in image processing. The objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a lot of choices for improving the visual quality of images. Appropriate choice of such techniques is very important. This paper will provide an overview and analysis of different techniques commonly used for image enhancement. Image enhancement plays a fundamental role in vision applications. Recently much work is completed in the field of images enhancement. Many techniques have previously been proposed up to now for enhancing the digital images. In this paper, a survey on various image enhancement techniques has been done.
HUMAN VISION THRESHOLDING WITH ENHANCEMENT FOR DARK BLURRED IMAGES FOR LOCAL ...cscpconf
There are several images that do not have uniform brightness which pose a challenging problem
for image enhancement systems. As histogram equalization has been successfully used to correct
for uniform brightness problems, a histogram equalization method that utilizes human visual
system based thresholding(human vision thresholding) as well as logarithmic processing
techniques were introduced later . But these methods are not good for preserving the local
content of the image which is a major factor for various images like medical images.Therefore
new method is proposed here. This method is referred as “Human vision thresholding with
enhancement technique for dark blurred images for local content preservation”. It uses human
vision thresholding together with an existing enhancement method for dark blurred images.
Experimental results shows that the proposed method outperforms the former existing methods in
preserving the local content for standard images and medical images
EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE...cscpconf
This paper proposes an exemplar based image inpainting using extended wavelet transform. The
Image inpainting modifies an image with the available information outside the region to be
inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The
Laplacian pyramid is first used to capture the point discontinuities, and then followed by a
directional filter bank to link point discontinuities into linear structures. The proposed model
effectively captures the edges and contours of natural scene images
Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. This paper aims at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.
Image Contrast Enhancement for Brightness Preservation Based on Dynamic Stret...CSCJournals
Histogram equalization is an efficient process often employed in consumer electronic systems for image contrast enhancement. In addition to an increase in contrast, it is also required to preserve the mean brightness of an image in order to convey the true scene information to the viewer. A conventional approach is to separate the image into sub-images and then process independently by histogram equalization towards a modified profile. However, due to the variations in image contents, the histogram separation threshold greatly influences the level of shift in mean brightness with respect to the uniform histogram in the equalization process. Therefore, the choice of a proper threshold, to separate the input image into sub-images, is very critical in order to preserve the mean brightness of the output image. In this research work, a dynamic range stretching approach is adopted to reduce the shift in output image mean brightness. Moreover, the computationally efficient golden section search algorithm is applied to obtain a proper separation into sub-images to preserve the mean brightness. Experiments were carried out on a large number of color images of natural scenes. Results, as compared to current available approaches, showed that the proposed method performed satisfactorily in terms of mean brightness preservation and enhancement in image contrast.
Optimized Histogram Based Contrast Limited Enhancement for Mammogram ImagesIDES Editor
Detection of breast cancer in its early stage is very
important in the field of medicine. Optimal Contrast
Enhancement is essential for the detection of mass and micro
calcification in mammogram images. The standard histogram
equalization is effective and simple method for contrast
enhancement but for medical images most of the time it
produces excessive contrast enhancement due to lack of control
for the level of enhancement. In this paper image
enhancement is considered as an optimization problem and
an optimization technique based on entropy and edge
information of the image is presented. The enhancement
function used in the paper is Contrast Limited Adaptive
Histogram Equalization (CLAHE) based on local contrast
modification (LCM). Its enhancement potential is tested by
sobel operator for the detection of microcalcification. Results
are compared with other enhancement techniques such as
Histogram Equalization, Unsharp Masking and CLAHE.
MIP AND UNSUPERVISED CLUSTERING FOR THE DETECTION OF BRAIN TUMOUR CELLSAM Publications
Image processing is widely used in biomedical applications. Image processing can be used to analyze
different MRI brain images in order to get the abnormality in the image .The objective is to extract meaningful
information from the imaged signals. Image segmentation is a process of partitioning an image in to different parts.
The division in to parts is often based on the characteristics of the pixels in the image. In our paper the segmentation
of the tumour tissues is carried out using k-means and fuzzy c-means clustering.Tumour can be found and faster
detection is achieved with only few seconds for execution. The input image of the brain is taken from the available
database and the presence of tumourin input image can be detected.
Intensity Preserving Cast Removal in Color Images Using Particle Swarm Optimi...IJECEIAES
In this paper, we present an optimal image enhancement technique for color cast images by preserving their intensity. There are methods which improves the appearance of the affected images under different cast like red, green, blue etc but up to some extent. The proposed color cast method is corrected by using transformation function based on gamma values. These optimal values of gamma are obtained through particle swarm optimization (PSO). This technique preserves the image intensity and maintains the originality of color by satisfying the modified gray world assumptions. For the performance analysis, the image distance metric criteria of CIELAB color space is used. The effectiveness of the proposed approach is illustrated by testing the proposed method on color cast images. It has been found that distance between the reference image and the corrected proposed image is negligible. The calculated value of image distance depicts that the enhanced image results of the proposed algorithm are closer to the reference images in comparison with other existing methods.
Fast Segmentation of Sub-cellular OrganellesCSCJournals
Segmentation and counting sub-cellular structure is a very challenging problem even for medical experts. A fast and efficient method for segmentation and counting of sub-cellular structure is proposed. The proposed method uses a hybrid combination of several image processing techniques and is effective in segmenting the sub-cellular structures in a fast and effective manner.
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
En noviembre de 2015, a petición de la asociación de alumnos UniON Campus, acudí a darles una breve charla sobre las posibilidades de la gamificación en marketing online.
Comunicado primera reunion regional autonomaerik arellana
Víctimas y organizaciones de derechos humanos hacen un llamado a las partes a agilizar la implementación de las medidas inmediatas de búsqueda de personas desaparecidas
In this webinar Nicole Steele and David Costin from Maximizer CRM talk about what the CRM landscape will look like this year and how Maximizer is positioned to deliver value.
A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHMcscpconf
The contrast enhancement of medical images has an important role in diseases diagnostic,
specially, cancer cases. Histogram equalization is considered as the most popular algorithm for
contrast enhancement according to its effectiveness and simplicity. In this paper, we present a
modified version of the Histogram Based Fast Enhancement Algorithm. This algorithm
enhances the areas of interest with less complexity. It is applied only to CT head images and its
idea based on treating with the soft tissues and ignoring other details in the image. The
proposed modification make the algorithm is valid for most CT image types with enhanced
results.
A study of a modified histogram based fast enhancement algorithm (mhbfe)sipij
Image enhancement is one of the most important issues in low-level image processing. The goal of image
enhancement is to improve the quality of an image such that enhanced image is better than the original
image. Conventional Histogram equalization (HE) is one of the most algorithms used in the contrast
enhancement of medical images, this due to its simplicity and effectiveness. However, it causes the
unnatural look and visual artefacts, where it tends to change the brightness of an images. The Histogram
Based Fast Enhancement Algorithm (HBFE) tries to enhance the CT head images, where it improves the
water-washed effect caused by conventional histogram equalization algorithms with less complexity. It
depends on using full gray levels to enhance the soft tissues ignoring other image details. We present a
modification of this algorithm to be valid for most CT image types with keeping the degree of simplicity.
Experimental results show that The Modified Histogram Based Fast Enhancement Algorithm (MHBFE)
enhances the results in term of PSNR, AMBE and entropy. We use also the Statistical analysis to ensure
the improvement of the proposed modification that can be generalized. ANalysis Of VAriance (ANOVA) is
used as first to test whether or not all the results have the same average. Then we find the significant
improvement of the modification.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
E FFECTIVE P ROCESSING A ND A NALYSIS OF R ADIOTHERAPY I MAGESsipij
a-Si Electronic Portal Imaging Device (EPID) is an
important tool to verify the location of the radiat
ion
therapy beam with respect to the patient anatomy. B
ut, Electronic Portal Images (EPI) suffer from low
contrast. In order to have better in-treatment imag
es to extract relevant features of the anatomy, ima
ge
processing tools need to be integrated in the Radio
logy systems. The goal of this research work is to
inspect
several image processing techniques for contrast en
hancement of electronic portal images and gauge
parameters like mean, variance, standard deviation,
MSE, RMSE, entropy, PSNR, AMBE, normalised cross
correlation, average difference, structural content
(SC), maximum difference and normalised absolute
error (NAE) to study their visual quality improvem
ent. In addition, by adding salt and pepper noise,
Gaussian noise and motion blur, we calculate error
measurement parameters like Universal Image Quality
(UIQ) index, Enhancement Measurement Error (EME), P
earson Correlation Coefficient, SNR and Mean
Absolute error (MAE). The improved results point ou
t that image processing tools need to be incorporat
ed
into radiology for accurate delivery of dose
Contrast enhancement using various statistical operations and neighborhood pr...sipij
Histogram Equalization is a simple and effective contrast enhancement technique. In spite of its popularity
Histogram Equalization still have some limitations –produces artifacts, unnatural images and the local
details are not considered, therefore due to these limitations many other Equalization techniques have been
derived from it with some up gradation. In this proposed method statistics play an important role in image
processing, where statistical operations is applied to the image to get the desired result such as
manipulation of brightness and contrast. Thus, a novel algorithm using statistical operations and
neighborhood processing has been proposed in this paper where the algorithm has proven to be effective in
contrast enhancement based on the theory and experiment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
In this technical article, we present a Novel algorithm for the lossy compression method, where the performance and storage has been proscribed with hardware descriptive language (HDL).
MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE CONTRAST ENHANCEMENT USING PARTICLE...ijcseit
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this
paper. This technique modifies the probability density function of an image by introducing constraints prior
to the process of histogram equalization (HE). These constraints are formulated using two parameters
which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment
factor is then added to the result to normalize the change in the luminance level after enhancement. This
factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of
highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to
various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
MODIFIED HISTOGRAM EQUALIZATION FOR IMAGE CONTRAST ENHANCEMENT USING PARTICLE...ijcseit
A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that the proposed technique gives better results in
terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.
Optimal Coefficient Selection For Medical Image FusionIJERA Editor
Medical image fusion is one of the major research fields in image processing. Medical imaging has become a
vital component in major clinical applications such as detection/ diagnosis and treatment. Joint analysis of
medical data collected from same patient using different modalities is required in many clinical applications.
This paper introduces an optimal fusion technique for multiscale-decomposition based fusion of medical images
and measuring its performance with existing fusion techniques. This approach incorporates genetic algorithm
for optimal coefficient selection and employ various multiscale filters for noise removal. Experiments
demonstrate that proposed fusion technique generate better results than existing rules. The performance of
proposed system is found to be superior to existing schemes used in this literature.
Engineering Research Publication
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Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Computer Science & Information Technology (CS & IT)
levels based on the probability distribution of the input gray levels. It flattens and stretches the
dynamic range of the image's histogram, resulting in an overall contrast improvement.HE has
been applied in various fields such as medical image processing and radar image processing [3,
4]. The two categories of histogram equalization are. Global histogram equalization, which is
simple and fast, but its contrast-enhancement power is relatively low. Local histogram
equalization, on the other hand, can effectively enhance contrast, but it requires more
computations.
Global Histogram equalization is powerful in highlighting the borders and edges between
different objects, but may reduce the local details within these objects [5] to overcome HE's
problems. Ketcham and et al invented Local Histogram Equalization (LHE); the algorithm uses
the histogram of a window of a predetermined size to determine the transformation of each pixel
in the image. LHE succeeded in enhancing local details, but it depends on fixed size for windows
where it may distort the boundaries between regions. It also demands high computational cost and
sometimes causes over-enhancement in some portion of the image [6, 7].
There are many algorithms trying to preserve the brightness of the output image like BBHE
(Brightness preserving Bi-Histogram Equalization) which separates the input image histogram
into two parts based on the mean of the input image and then each part is equalized
independently. There are many methods similar to BBHE like, DSIHE (Dualistic Sub-Image
Histogram Equalization) where, it divides the histogram based on the median value. MDSIHE
(Modified Dualistic Sub Image Histogram Equalization), A. Zadbuke made a modification on
DSIHE and obtainedgood results [8]. MMBEBHE (Minimum Mean Brightness Error BiHistogram Equalization) provides maximal brightness preservation, but its resultsare foundnot
good for the image with a lot details. To overcome these drawbacks, P. Jagatheeswari and et al
proposed a modification to this method. They enhanced images by passing the enhanced ones
through a median filter. The median filter is an effective method for the removal of impulse based
noise on the images [9]. Recursive Mean-Separate Histogram Equalization (RMSHE) is also
considered asan extension to BBHE. All these methods achieve good contrast but they have some
problems in gray level variation [7].
The rest of the paper is organized as follows. in section 2, the idea of A Histogram-Based Fast
Enhancement Algorithm will be introduced. Then, the problems were found in this algorithm and
the suggested modification is presented in section 3. Experimental results using clinical data of
CT images is discussed in section 4 to demonstrate the usefulness of the proposed method.
Concluding remarks ispresented in section 5.
2. A HISTOGRAM BASED FAST ENHANCEMENT ALGORITHM
J. Yin and et al proposed an algorithm to enhance local interested areas in CT head images; they
tried to improve the water-washed effect caused by the conventional histogram equalization
algorithms as shown in Figure1. The algorithm succeeded in removing water-washed effect.
There are some important features for this algorithm like the speed and the simplicity. Its idea
depends on that, most CT head images occupy the gray level 0 so they try to deal with the soft
tissues by enhancing the region by using full range of all possible gray levels to enhance it in the
CT head images. They analyzed these images and found that more than half of the whole range of
gray levels occupies 0 level, and all CT head images have three major peaks in their histograms.
The left peak is formed by background pixels, the middle peak is usually formed by soft tissues in
3. Computer Science & Information Technology (CS & IT)
263
the CT head images, and the right peak is formed mostly by bone. For enhancement details, we
need only the middle peak which formed by soft tissue [10].
Figure 1. (a) an original CT head image (b) enhanced by conventional histogram equalization algorithm (c)
Histogram-Based Fast Enhancement Algorithm.
3. A MODIFIED HISTOGRAM BASED FAST ENHANCEMENT ALGORITHM
The idea of the algorithm depends on the characteristics of CT head images. This makesthe
algorithm is suitable for special type of images, so we tried to make a modification to this
algorithm to be more appropriate for a wide range of CT images with enhanced results.The
calculations of Histogram-Based Fast Enhancement Algorithm depends on a constant value k
(0<k<0.4) to evaluate how many gray levels should be ignored. This means that k remains
constant for all images regardlessof image characteristics, so we calculated the value of k to
change with the gray levels of the picture.
First, we evaluated k as a ratio of the mean value of histogram values, which is considered as an
importantfeature of the histogram then we recorded these results, and compared it with the
Histogram-Based Fast Enhancement Algorithm; we found that there is a valuable enhancement in
results. Thesteps of our proposed solution remained as in the Histogram-Based Fast Enhancement
Algorithm, but the change will be occurred in determining k value as below.
݇ = ܪ ∗ ݅ݐܽݎ
(1)
Where Hmean is the mean value of the histogram, which is the sum of the values divided by the
number of values.Second, we performedanother modification by using k as a ratio of median
value of the histogram and found that the results become better that because the value depends on
the characteristic of image.
݇ = ܪ ∗ ݅ݐܽݎௗ
(2)
WhereHmedian is the median value of the histogram, it is the value which divides the values into
two equal halves.At the last, we use the mode value as the most frequently occurring value in the
histogram.
݇ = ܪ ∗ ݅ݐܽݎௗ
(3)
We applied the modified algorithm to large varieties of CT images including head and lung
images. To evaluate the effectiveness of the modification we use three widely-used metrics;
PSNR (Peak Signal-to-Noise Ratio), AMBE (Absolute Mean Brightness Error), and the entropy,
in addition to Inspection of Visual Quality. We will show briefly how to evaluate these metrics in
the next section.
4. 264
Computer Science & Information Technology (CS & IT)
3.1 Peak Signal to Noise Ratio (PSNR)
PSNR is the evaluation standard of the reconstructed image quality, and is an important
measurement feature. PSNR is measured in decibels (dB). If we suppose a reference image f and
a test image t, both of size M×N, the PSNR between f and g is defined by.
ܴܲܵܰ(݂, ݈݃ = )ݐଵ ()1 − ܮଶ /)ݐ ,݂(ܧܵܯ
(4)
Where L is gray levels and MSE (Mean square error), is then defined as.
ெ
ே
1
ଶ
ࡹܵ= )ݐ ,݂(ܧ
൫݂ − ݐ ൯
ܰܯ
ୀଵ ୀଵ
(5)
Note that the greater the PSNR, the better the output image quality.
3.2 Absolute Mean Brightness Error (AMBE)
It is the difference between original and enhanced image and is given as.
|ܯܻ − ܯܺ| = )ܻ ,ܺ(ܧܤܯܣ
(6)
Where XM is the mean of the input image X = {X (i, j)} and YM is the mean of the output image
Y = {Y (i, j)}.
We try to preserve the brightness of the image to keep the image details, so if we reduce the
difference this preserve the brightness of the image.
3.3 Entropy
Entropy is a statistical measure of randomness that can be used to characterize the texture of the
input image. It is a useful tool to measure the Richness of the details in the output image [11].
ݐ݊ܧሾܲሿ = (ܲ log ଶ (ܲ ))
ୀଵ
(7)
3.4 Inspection of Visual Quality
In addition to the quantitative evaluation of contrast enhancement using the PSNR and entropy
values, it is also important to qualitatively assess the contrast enhancement. The major goal of
the qualitative assessment is to judge if the output image is visually acceptable to human eyes and
has a natural appearance [8].
5. Computer Science & Information Technology (CS & IT)
265
4. EXPERIMENTAL RESULTS
To show the effect of the proposed modification, we apply it on different types of CT images. We
use head images like the original algorithmin addition to the lung images to be validate for more
image types.
Figure 2. (a) Original CT head image (b) enhanced by conventional histogram equalization algorithm (c)
enhanced by Histogram-Based Fast Enhancement Algorithm. (d) Modified Histogram-Based Fast
Enhancement Algorithm using mean value (e) Modified Histogram-Based Fast Enhancement Algorithm
using median value. (f) Modified Histogram-Based Fast Enhancement Algorithm using mode value
Figure 3. (a) Original CT lung image (b) enhanced by conventional histogram equalization algorithm (c)
enhanced by Histogram-Based Fast Enhancement Algorithm. (d) Modified Histogram-Based Fast
Enhancement Algorithm using mean value (e) Modified Histogram-Based Fast Enhancement Algorithm
using median value (f) Modified Histogram-Based Fast Enhancement Algorithm using mode value
6. 266
Computer Science & Information Technology (CS & IT)
Table 1. PSNR measurement
Image
CThead1
CThead2
CThead3
CThead4
CTlung1
CTlung2
CTlung3
CTlung4
Conventional
Histogram
Equalization
Algorithm
HistogramBased Fast
Enhanceme
nt
Algorithm
Modified Histogram-Based Fast Enhancement
Algorithm
6.6589
6.7181
4.2788
6.7181
17.9699
19.3186
8.839
15.3099
12.1687
12.3103
9.0203
12.3103
26.9840
30.1420
13.8357
21.5727
13.9531
14.8156
9.8088
14.8156
28.6100
32.3924
13.4644
26.8872
Using
Using
Mean
Median
Using Mode
14.6326
14.81723
11.22233
14.81723
32.35675
41.8492
14.50487
31.9475
14.63262
14.81723
11.22233
14.81723
34.2496
43.58417
14.5052
35.1296
As we mention before, the increase in the value of PSNR is considered as an enhancement in the
algorithm. From Table 1 we find that there is an enhancement using the proposed modified
algorithm.
Table 2. AMBE measurement.
Image
CThead1
CThead2
CThead3
CThead4
CTlung1
CTlung2
CTlung3
CTlung4
Conventional
Histogram
Equalization
Algorithm
Histogram-Based
Fast
Enhancement
Algorithm
Modified
Histogram-Based
Fast
Enhancement Algorithm
Using
Using Median Using Mode
Mean
111.8702
97.365
150.4411
112.059
13.4576
15.1077
76.9961
13.977
48.14349
41.37939
78.69606
47.4835
4.9821
4.1489
41.85713
11.785
38.0466
133.7495
70.7215
33.5147
3.427556
3.2355
43.53475
5.95788
34.55872
30.06355
57.66641
33.43602
1.969327
1.5521
35.29687
3.7327
34.55872
13.3852
57.66641
33.43602
1.6462
1.369413
35.26233
2.9718
Our Proposed algorithm is considered one of brightness persevered algorithm so we try to reduce
the difference between the brightness of input and the result image. From Table 2, we can
conclude that there is an enhancement in AMBE values using the proposed algorithm.
As we will see in Table 3, there is a small increase in the Entropy values especially using the
median and the mode where we have found there is a great convergence between median and
mode values. As for the Inspection of Visual Quality, as we see in Figure2 and Figure3 there are
some details appeared in the proposed algorithm which help in diagnostic diseases more accurate.
7. Computer Science & Information Technology (CS & IT)
267
Table 3. Entropy measurement.
Original
Image
Image
CThead1
CThead2
CThead3
CThead4
CTlung1
CTlung2
CTlung3
CTlung4
0.9991
0.8993
0.9169
0.9997
0.0022
0.0695
0.1575
0.2065
Conventional
Histogram
Equalizatio
n
Algorithm
HistogramBased Fast
Enhancem
ent
Algorithm
Modified
Histogram-Based
Enhancement Algorithm
3.3235
4.5394
2.3727
3.3564
5.8899
5.9528
3.3206
2.5454
4.608886
5.144703
2.812813
4.4211
6.9246
6. 8429
3.517687
6.4606
4.912558
2.077121
2.9972
5.0363
7.08508
6.9279
3.409321
6.687
Using
Mean
Using
Median
5.133528
5.391977
3.377863
5.058119
7.102342
7.1620
4.483833
6.687
Fast
Using Mode
5.13352
5.8384
3.37786
5.05811
7.0458
7.20219
4.50024
6.6064
We can exclude some points from the previous results that the modified algorithm achieves
greater values of PSNR, AMBE and entropy compared with Histogram-Based Fast Enhancement
Algorithm. The first metric of PSNR; the propose algorithm have increased the values of PSNR;
this means that less noise in the resulted image. The second metric is AMBE, it has been
minimized and this means that it has preserved the brightness of the image. The third metric of
entropy where it has increased; this means that more information can be extracted from the output
image. We also performed statistical analysis for the results in Figure4, Figure5, and Figure6,
where Figure4 shows the increment in PSNR values due to using the modification with mean,
median and mode.Figure5 shows the enhancement in entropy values and Figure6 show the
decrement of AMBE. There is a valuable improvement in the three parameters for the
modification especially the mode where give the best results. We found that there is a range of
ratio values that gives the best results for the three parameters and outside this range there arenot
good results. This gives us the ability to control this ratio to obtain the best results.
Figure4. The effect of modification on PSNR values
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Computer Science & Information Technology (CS & IT)
Figure5.The effect of modification on entropy values
Figure6.The effect of modification on AMBE values
5. CONCLUSION
In this paper, we have presented a simple modificationof Histogram Based Fast Enhancement
Algorithm. First, we have showed how it succeeded in removing water-washed effect. Then
discuss the proposed modification which enhances the PSNR, AMBE and entropy parameters
values to be more appropriate for a wide range of CT images.In addition to the enhancements
occurred to the Histogram-Based Fast Enhancement Algorithm. There are some advantages of the
algorithm compared to other algorithms. It still keeps the advantage of simplicity due to less
complex calculations used in the algorithm. There is another advantage of this algorithm due to
its idea of using global histogram and not based on local histogram. This decreases the used time
for running.
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