A comparison of ultrasound intima media thickness measurements of the left and right common carotid artery
•Download as DOCX, PDF•
1 like•142 views
This document describes a study that compares ultrasound measurements of intima-media thickness (IMT) in the left and right common carotid arteries. The study analyzed ultrasound images of 1104 subjects and found that (1) there was no significant difference between IMT measurements on the left and right sides for healthy subjects, (2) IMT measurements were significantly higher for subjects with cardiovascular disease on both sides, and (3) IMT increased with age for both healthy and diseased groups. An automated system was developed and achieved comparable IMT measurements to manual delineation. The system achieved 64% accuracy in distinguishing healthy from diseased subjects using IMT from both carotid arteries.
Report
Share
Report
Share
Recommended
Computer aided diagnosis for liver cancer using statistical model
Abstract Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes generally increasing. The signs and the symptoms of the liver cancer are not known, till the cancer is in its advanced stage. So, early detection is the main problem. If it is detected earlier then it can be helpful for the Medical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long processing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence. Keywords—HMM, Segmentation, Feature Extraction.
Computer aided diagnosis for liver cancer using
This document describes a computer-aided diagnosis system for detecting liver cancer in early stages from CT chest images. The system uses a Hidden Markov Model (HMM) for classification. It involves preprocessing the CT image through segmentation, noise removal and morphological operations. Features are then extracted and classified using HMM. The system achieved a high detection rate of 96.5% on 100 cases, outperforming other methods. This automated early detection system could help reduce risks for liver cancer patients by enabling earlier medical treatment.
Thesis_Censored_version - R Sjoukes
1) The student developed a pipeline to test the ability to measure strain in carotid artery plaques using MRI.
2) Finite element models of plaques under different pressures were used to simulate MRI images.
3) Image registration software was used to measure strain by registering images at different pressures, and results were compared to the finite element models.
4) Promising results were found when the image registration grid spacing parameter was optimized, but more research is still needed to determine the effects of other MRI and registration parameters on the ability to accurately measure strain from MRI data.
Detection of acute leukemia using white blood cells segmentation based on
This document describes a study that developed an automated system for detecting acute leukemia using white blood cell segmentation from blood samples. The system applies image segmentation, feature extraction and cell classification techniques to differentiate normal cells from blast cells. It was tested on 108 images from a public dataset. The segmentation approach involved preprocessing, thresholding, and morphological operations to isolate white blood cells. Features like area, perimeter and circularity were then extracted and a k-nearest neighbor classifier was used to classify cells as normal or blast. Experimental results found the segmentation approach could accurately isolate white blood cells within milliseconds to aid in acute leukemia detection.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herni...
This document describes research on improving radiologists' and orthopedists' quality of experience (QoE) in diagnosing lumbar disk herniation using 3D modeling. The researchers built 3D models from MRI scans and developed an automated diagnosis framework. They evaluated the 3D models on 14 medical specialists and found it increased their QoE in 95% of cases. The automated framework was trained on 83 cases and tested on new cases, achieving 90% diagnosis accuracy.
Computers in medicine and biology
This document discusses the use of computers in medicine and biology. In medicine, computers are used for X-rays and CT scans to produce images of internal structures, MRI to map internal structures without radiation, monitoring patient vital signs, and robotic surgery. Computers also store diagnostic information in databases and allow it to be instantly accessible worldwide. In biology, computers enable visual simulations and illustrations to help understand concepts, and are used for research through accessing online information and experimental simulations.
F0363029031
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
BaraAldasouqiCMBBE2015
This document presents a method to automatically measure abdominal aortic aneurysm (AAA) wall thickness from medical images using a regression algorithm. Two reference models (a porcine aorta and synthetic AAA phantom) were used to train and validate the algorithm. A support vector machine regression model using normalized pixel intensity profiles achieved a mean error of 0.262 mm, providing more accurate wall thickness estimates than direct measurements from low-resolution CT images. Further optimization and testing on real patient data is needed to validate the algorithm's ability to provide new insights into AAA development and rupture risk.
Recommended
Computer aided diagnosis for liver cancer using statistical model
Abstract Liver Cancer is one of the most difficult cancer to cure and the number of deaths that it causes generally increasing. The signs and the symptoms of the liver cancer are not known, till the cancer is in its advanced stage. So, early detection is the main problem. If it is detected earlier then it can be helpful for the Medical treatment to limit the danger, but it is a challenging task due to the Cancer cell structure. Interpretation of Medical image is often difficult and time consuming, even for the experienced Physicians. Most traditional medical diagnosis systems founded needs huge quantity of training data and takes long processing time. Focused on the solution to these problems, a Medical Diagnosis System based on Hidden Markov Model (HMM) is presented. This paperdescribes a computer aided diagnosis system for liver cancer that detects the liver tumor at an early stage from the chest CT images. This automation process reduces the time complexity and increases the diagnosis confidence. Keywords—HMM, Segmentation, Feature Extraction.
Computer aided diagnosis for liver cancer using
This document describes a computer-aided diagnosis system for detecting liver cancer in early stages from CT chest images. The system uses a Hidden Markov Model (HMM) for classification. It involves preprocessing the CT image through segmentation, noise removal and morphological operations. Features are then extracted and classified using HMM. The system achieved a high detection rate of 96.5% on 100 cases, outperforming other methods. This automated early detection system could help reduce risks for liver cancer patients by enabling earlier medical treatment.
Thesis_Censored_version - R Sjoukes
1) The student developed a pipeline to test the ability to measure strain in carotid artery plaques using MRI.
2) Finite element models of plaques under different pressures were used to simulate MRI images.
3) Image registration software was used to measure strain by registering images at different pressures, and results were compared to the finite element models.
4) Promising results were found when the image registration grid spacing parameter was optimized, but more research is still needed to determine the effects of other MRI and registration parameters on the ability to accurately measure strain from MRI data.
Detection of acute leukemia using white blood cells segmentation based on
This document describes a study that developed an automated system for detecting acute leukemia using white blood cell segmentation from blood samples. The system applies image segmentation, feature extraction and cell classification techniques to differentiate normal cells from blast cells. It was tested on 108 images from a public dataset. The segmentation approach involved preprocessing, thresholding, and morphological operations to isolate white blood cells. Features like area, perimeter and circularity were then extracted and a k-nearest neighbor classifier was used to classify cells as normal or blast. Experimental results found the segmentation approach could accurately isolate white blood cells within milliseconds to aid in acute leukemia detection.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herni...
This document describes research on improving radiologists' and orthopedists' quality of experience (QoE) in diagnosing lumbar disk herniation using 3D modeling. The researchers built 3D models from MRI scans and developed an automated diagnosis framework. They evaluated the 3D models on 14 medical specialists and found it increased their QoE in 95% of cases. The automated framework was trained on 83 cases and tested on new cases, achieving 90% diagnosis accuracy.
Computers in medicine and biology
This document discusses the use of computers in medicine and biology. In medicine, computers are used for X-rays and CT scans to produce images of internal structures, MRI to map internal structures without radiation, monitoring patient vital signs, and robotic surgery. Computers also store diagnostic information in databases and allow it to be instantly accessible worldwide. In biology, computers enable visual simulations and illustrations to help understand concepts, and are used for research through accessing online information and experimental simulations.
F0363029031
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
BaraAldasouqiCMBBE2015
This document presents a method to automatically measure abdominal aortic aneurysm (AAA) wall thickness from medical images using a regression algorithm. Two reference models (a porcine aorta and synthetic AAA phantom) were used to train and validate the algorithm. A support vector machine regression model using normalized pixel intensity profiles achieved a mean error of 0.262 mm, providing more accurate wall thickness estimates than direct measurements from low-resolution CT images. Further optimization and testing on real patient data is needed to validate the algorithm's ability to provide new insights into AAA development and rupture risk.
Segmentation of cysts in kidney and 3 d volume calculation from ct images
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
Segmentation of cysts in kidney and 3 d volume calculation from ct images
This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice
images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the
same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented
from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of
non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in
the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated
by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D
visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume
calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide practical supports to surgery options and medical
practice to medical students
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSIS
Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver
segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples
collected from ten patients and received 90% accuracy rate.
A novel automated identification of intima media thickness in ultrasound
A NOVEL AUTOMATED IDENTIFICATION OF INTIMA MEDIA THICKNESS IN
ULTRASOUND ARTERY IMAGES USING GREY AND WHITE MATTER
The International Journal of Engineering and Science (The IJES)
This document summarizes a research paper on developing a computer-aided diagnosis system for early detection of liver cancer from CT chest images. The proposed system involves extracting features from segmented liver regions of CT images using techniques like noise removal, segmentation, and morphological operations. Features are then extracted and can be classified using Hidden Markov Models to identify liver cancer cells at an early stage and improve diagnosis. The authors suggest future work to refine cancer cell classification and reduce time complexity for improved diagnosis confidence.
Liver extraction using histogram and morphology
Abstract
Liver is the largest glandular organ important for survival in human body. Computed tomography is generally used to image liver
due to its precision. This paper presents a method to extract liver from computed tomography (CT) abdomen images in axial
orientation. A traditional segmentation method based on histogram and morphology is proposed herein. Histogram is used to
analyze the intensity distribution, morphological operations are used to disconnect liver from the neighboring organs and greatest
connected pixels are extracted. The experimental results of the proposed method when applied to CT abdomen images with
contrast are presented and the effectiveness is discussed in accordance to the manual tracing obtained from the radiologist. Dice
similarity co-efficient amounts to 94% in the proposed method.
Keywords: Segmentation, Extraction, Histogram, Morphology, Connected Component, CT Liver
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Intensity Non-uniformity Correction for Image Segmentation
This document discusses the application of various image segmentation techniques, including Otsu thresholding, fuzzy C-means clustering, and marker-controlled watershed segmentation, to atherosclerosis images. Atherosclerosis is the buildup of plaque in artery walls that can lead to coronary heart disease. Accurately segmenting atherosclerosis images is an important preprocessing step for analysis. The document proposes using a marker-controlled watershed segmentation algorithm to segment the images, arguing it can construct full divisions of color images while preventing oversegmentation compared to conventional watershed algorithms. It then applies several segmentation techniques to atherosclerosis images and evaluates the results.
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventri...
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventricular Dysfunction undergoing Coronary Revascularization. A Longitudinal Study
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Visible watermarking within the region of non interest of medical images base...
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
50120130405027 2-3
This document summarizes a research study that used artificial neural networks (ANN) to classify ultrasound images of normal and abnormal kidneys. The study involved the following key steps:
1. Ultrasound images of normal and abnormal kidneys were collected and preprocessed, including cropping, rotation, edge detection and background removal.
2. Texture features were extracted from the preprocessed images using four different techniques: intensity histogram, gray-level co-occurrence matrix, gray-level run length matrix, and invariant moments. This resulted in a total of 4324 features.
3. Feature selection was performed to identify the most important features, resulting in a reduced feature set.
4. An ANN with a back
ECSS final poster
This study aimed to examine the applicability of ultrasonography (ULT) for estimating human muscle volume by comparing it to the gold standard of magnetic resonance imaging (MRI). The tibialis anterior muscle volume of 6 subjects was estimated using both ULT and MRI. ULT produced highly reproducible results but tended to underestimate volumes below 120cm3 and overestimate volumes above. Overall, ULT was found to be a valid and reproducible method for estimating volume in superficial muscles, though it was more time consuming than MRI and restricted to superficial muscles only. ULT may introduce a measurement error of around 7% for muscle volumes between 70-400cm3.
IRJET-Bone Tumor Detection from MRI Images using Machine Learning: A Review
1. The document proposes a method for detecting bone tumors using machine learning algorithms applied to MRI images.
2. The method involves preprocessing images using denoising filters, segmenting images using k-means clustering and fuzzy c-means, extracting features using machine learning algorithms, identifying tumors by calculating mean pixel intensity of segmented regions, and detecting tumors by identifying the largest connected component.
3. The goal is to develop a robust system for accurately detecting bone tumors like enchondroma in MRI images by addressing noise and obtaining the exact tumor location.
Feature Selection Mammogram based on Breast Cancer Mining
The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.
Survey of Feature Selection / Extraction Methods used in Biomedical Imaging
Survey of Feature Selection / Extraction Methods used in Biomedical ImagingIJCSIS Research Publications
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.Gold Logo
Millworks is a company that provides woodworking services. They have over 20 years of experience crafting custom cabinets, furniture, stairs, and other wood projects for both residential and commercial clients. Millworks prides itself on quality workmanship and using sustainable wood materials for all their projects.
North East Meet the Buyer seminar slideshow - 7 Nov 2013
These are the slides from the seminar section of our North East Meet the Buyer event. These talks included: Gateshead Council, Eight Degrees,
International Energy Day
This document does not contain any meaningful information to summarize. It consists of the word "selected" repeated multiple times without any additional context.
Coordinate geometry
This document defines and explains coordinate geometry. It begins by defining a grid, coordinate grid, x-axis, and y-axis. It then discusses plotting and finding ordered pairs on a coordinate grid with four quadrants by following steps of first locating the x-value and then the y-value, and whether numbers are positive or negative determines the direction. Examples are provided to demonstrate plotting various ordered pairs in all four quadrants of the coordinate system.
Fast Facts - Metal Manufacturers
The document provides information about The Hartford insurance company and the metal manufacturing industry. It discusses that most U.S. manufacturers are small businesses and that outsourcing is reversing as costs rise overseas. The Hartford is interested in providing insurance to metal manufacturers that have low hazard risks and controlled special hazards. Characteristics like automated processes and known product end-uses are attractive, while uncontrolled environments and occupational disease exposure are less attractive. The Hartford can provide tailored business owner's policies, workers' compensation, and business auto insurance to metal manufacturers.
Minerais, 1º D
El documento proporciona información sobre las propiedades de varios minerales, incluyendo cuarzo, mica, biotita, ortosa, caolinita, olivino, cobre, azufre, fluorita, halita, pirita, cinabrio, galena, blenda, yeso, calcita, aragonita y magnetita. Para cada mineral, se especifican su color, raya, brillo, dureza y otras propiedades distintivas. El documento cubre tanto silicatos como no silicatos.
More Related Content
What's hot
Segmentation of cysts in kidney and 3 d volume calculation from ct images
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
Segmentation of cysts in kidney and 3 d volume calculation from ct images
This paper proposes a segmentation method and a three-dimensional (3-D) volume calculation method of
cysts in kidney from a number of computer tomography (CT) slice images. The input CT slice images
contain both sides of kidneys. There are two segmentation steps used in the proposed method: kidney
segmentation and cyst segmentation. For kidney segmentation, kidney regions are segmented from CT slice
images by using a graph-cut method that is applied to the middle slice of input CT slice images. Then, the
same method is used for the remaining CT slice images. In cyst segmentation, cyst regions are segmented
from the kidney regions by using fuzzy C-means clustering and level-set methods that can reduce noise of
non-cyst regions. For 3-D volume calculation, cyst volume calculation and 3-D volume visualization are
used. In cyst volume calculation, the area of cyst in each CT slice image equals to the number of pixels in
the cyst regions multiplied by spatial density of CT slice images, and then the volume of cysts is calculated
by multiplying the cyst area and thickness (interval) of CT slice images. In 3-D volume visualization, a 3-D
visualization technique is used to show the distribution of cysts in kidneys by using the result of cyst volume
calculation. The total 3-D volume is the sum of the calculated cyst volume in each CT slice image.
Experimental results show a good performance of 3-D volume calculation. The proposed cyst segmentation
and 3-D volume calculation methods can provide practical supports to surgery options and medical
practice to medical students
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSIS
Apply the Image processing techniques to analyse the medical images may assist medical professionals as well as patients, especially in this research apply the algorithms to diagnose the liver tumours from the abdominal CT image. This research proposes a software solution to illustrate the automated liver
segmentation and tumour detection using artificial intelligent techniques. Evaluate the results of the liver segmentation and tumour detection, in-cooperation with the radiologists by using the prototype of the proposed system. This research overcomes the challenges in medical image processing. The 100 samples
collected from ten patients and received 90% accuracy rate.
A novel automated identification of intima media thickness in ultrasound
A NOVEL AUTOMATED IDENTIFICATION OF INTIMA MEDIA THICKNESS IN
ULTRASOUND ARTERY IMAGES USING GREY AND WHITE MATTER
The International Journal of Engineering and Science (The IJES)
This document summarizes a research paper on developing a computer-aided diagnosis system for early detection of liver cancer from CT chest images. The proposed system involves extracting features from segmented liver regions of CT images using techniques like noise removal, segmentation, and morphological operations. Features are then extracted and can be classified using Hidden Markov Models to identify liver cancer cells at an early stage and improve diagnosis. The authors suggest future work to refine cancer cell classification and reduce time complexity for improved diagnosis confidence.
Liver extraction using histogram and morphology
Abstract
Liver is the largest glandular organ important for survival in human body. Computed tomography is generally used to image liver
due to its precision. This paper presents a method to extract liver from computed tomography (CT) abdomen images in axial
orientation. A traditional segmentation method based on histogram and morphology is proposed herein. Histogram is used to
analyze the intensity distribution, morphological operations are used to disconnect liver from the neighboring organs and greatest
connected pixels are extracted. The experimental results of the proposed method when applied to CT abdomen images with
contrast are presented and the effectiveness is discussed in accordance to the manual tracing obtained from the radiologist. Dice
similarity co-efficient amounts to 94% in the proposed method.
Keywords: Segmentation, Extraction, Histogram, Morphology, Connected Component, CT Liver
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Intensity Non-uniformity Correction for Image Segmentation
This document discusses the application of various image segmentation techniques, including Otsu thresholding, fuzzy C-means clustering, and marker-controlled watershed segmentation, to atherosclerosis images. Atherosclerosis is the buildup of plaque in artery walls that can lead to coronary heart disease. Accurately segmenting atherosclerosis images is an important preprocessing step for analysis. The document proposes using a marker-controlled watershed segmentation algorithm to segment the images, arguing it can construct full divisions of color images while preventing oversegmentation compared to conventional watershed algorithms. It then applies several segmentation techniques to atherosclerosis images and evaluates the results.
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventri...
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventricular Dysfunction undergoing Coronary Revascularization. A Longitudinal Study
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
This document presents a method for automatically localizing fetal organs in MRI scans using random forests with steerable features. The method first normalizes fetal size, then uses a classification and regression pipeline with random forests to assign voxels to organs and vote for organ centers. Features are steered based on detected landmarks like the brain to account for unknown fetal orientation. Evaluation on two datasets found the heart was localized within 10mm of ground truth in 90% of cases, suggesting it could initialize motion correction. Future work will use the detections for slice-by-slice segmentation to improve motion correction quality.
Visible watermarking within the region of non interest of medical images base...
Transfer of medical information amongst various hospitals and diagnostic centers for mutual
availability of diagnostic and therapeutic case studies is a very common process. Watermarking
is adding “ownership” information in multimedia contents to verify signal integrity, prove
authenticity and achieve control over the copy process. Distortion in Region of Interest (ROI) of
a bio-medical image caused by watermarking may lead to wrong diagnosis and treatment.
Therefore, proper selection of Region of Non-Interest (RONI) in a medical image is very crucial
for adding watermark. First part of the present work proposes proper selection of Region of
Non-Interest based on Fuzzy C-Means segmentation and Harris corner detection, to improve
retention of diagnostic value lost in embedding ownership information. The second part of the
work presents watermark embedding in the selected area of RONI based on alpha blending
technique. In this approach, the generated watermarked image having an acceptable level of
imperceptibility and distortion is compared to the original image. The Peak Signal to Noise
Ratio (PSNR) of the original image vs. watermarked image is calculated to prove the efficacy of
the proposed method.
50120130405027 2-3
This document summarizes a research study that used artificial neural networks (ANN) to classify ultrasound images of normal and abnormal kidneys. The study involved the following key steps:
1. Ultrasound images of normal and abnormal kidneys were collected and preprocessed, including cropping, rotation, edge detection and background removal.
2. Texture features were extracted from the preprocessed images using four different techniques: intensity histogram, gray-level co-occurrence matrix, gray-level run length matrix, and invariant moments. This resulted in a total of 4324 features.
3. Feature selection was performed to identify the most important features, resulting in a reduced feature set.
4. An ANN with a back
ECSS final poster
This study aimed to examine the applicability of ultrasonography (ULT) for estimating human muscle volume by comparing it to the gold standard of magnetic resonance imaging (MRI). The tibialis anterior muscle volume of 6 subjects was estimated using both ULT and MRI. ULT produced highly reproducible results but tended to underestimate volumes below 120cm3 and overestimate volumes above. Overall, ULT was found to be a valid and reproducible method for estimating volume in superficial muscles, though it was more time consuming than MRI and restricted to superficial muscles only. ULT may introduce a measurement error of around 7% for muscle volumes between 70-400cm3.
IRJET-Bone Tumor Detection from MRI Images using Machine Learning: A Review
1. The document proposes a method for detecting bone tumors using machine learning algorithms applied to MRI images.
2. The method involves preprocessing images using denoising filters, segmenting images using k-means clustering and fuzzy c-means, extracting features using machine learning algorithms, identifying tumors by calculating mean pixel intensity of segmented regions, and detecting tumors by identifying the largest connected component.
3. The goal is to develop a robust system for accurately detecting bone tumors like enchondroma in MRI images by addressing noise and obtaining the exact tumor location.
Feature Selection Mammogram based on Breast Cancer Mining
The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.
Survey of Feature Selection / Extraction Methods used in Biomedical Imaging
Survey of Feature Selection / Extraction Methods used in Biomedical ImagingIJCSIS Research Publications
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.What's hot (16)
Segmentation of cysts in kidney and 3 d volume calculation from ct images
Segmentation of cysts in kidney and 3 d volume calculation from ct images
Segmentation of cysts in kidney and 3 d volume calculation from ct images
Segmentation of cysts in kidney and 3 d volume calculation from ct images
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSIS
MEDICAL IMAGE PROCESSING METHODOLOGY FOR LIVER TUMOUR DIAGNOSIS
A novel automated identification of intima media thickness in ultrasound
A novel automated identification of intima media thickness in ultrasound
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering and Science (The IJES)
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES
Intensity Non-uniformity Correction for Image Segmentation
Intensity Non-uniformity Correction for Image Segmentation
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventri...
Prognostic Value of Right Ventricular Parameters in Patients with Left Ventri...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Automated Localization of Fetal Organs in MRI Using Random Forests with Steer...
Visible watermarking within the region of non interest of medical images base...
Visible watermarking within the region of non interest of medical images base...
IRJET-Bone Tumor Detection from MRI Images using Machine Learning: A Review
IRJET-Bone Tumor Detection from MRI Images using Machine Learning: A Review
Feature Selection Mammogram based on Breast Cancer Mining
Feature Selection Mammogram based on Breast Cancer Mining
Survey of Feature Selection / Extraction Methods used in Biomedical Imaging
Survey of Feature Selection / Extraction Methods used in Biomedical Imaging
Viewers also liked
Gold Logo
Millworks is a company that provides woodworking services. They have over 20 years of experience crafting custom cabinets, furniture, stairs, and other wood projects for both residential and commercial clients. Millworks prides itself on quality workmanship and using sustainable wood materials for all their projects.
North East Meet the Buyer seminar slideshow - 7 Nov 2013
These are the slides from the seminar section of our North East Meet the Buyer event. These talks included: Gateshead Council, Eight Degrees,
International Energy Day
This document does not contain any meaningful information to summarize. It consists of the word "selected" repeated multiple times without any additional context.
Coordinate geometry
This document defines and explains coordinate geometry. It begins by defining a grid, coordinate grid, x-axis, and y-axis. It then discusses plotting and finding ordered pairs on a coordinate grid with four quadrants by following steps of first locating the x-value and then the y-value, and whether numbers are positive or negative determines the direction. Examples are provided to demonstrate plotting various ordered pairs in all four quadrants of the coordinate system.
Fast Facts - Metal Manufacturers
The document provides information about The Hartford insurance company and the metal manufacturing industry. It discusses that most U.S. manufacturers are small businesses and that outsourcing is reversing as costs rise overseas. The Hartford is interested in providing insurance to metal manufacturers that have low hazard risks and controlled special hazards. Characteristics like automated processes and known product end-uses are attractive, while uncontrolled environments and occupational disease exposure are less attractive. The Hartford can provide tailored business owner's policies, workers' compensation, and business auto insurance to metal manufacturers.
Minerais, 1º D
El documento proporciona información sobre las propiedades de varios minerales, incluyendo cuarzo, mica, biotita, ortosa, caolinita, olivino, cobre, azufre, fluorita, halita, pirita, cinabrio, galena, blenda, yeso, calcita, aragonita y magnetita. Para cada mineral, se especifican su color, raya, brillo, dureza y otras propiedades distintivas. El documento cubre tanto silicatos como no silicatos.
finalpresentation3-151207154026-lva1-app6892-15121.doc
This document summarizes a comparative analysis of two Malaysian restaurants - Restoran The Scene located in Selangor and Restaurant Banyonya located in Malacca. It provides details on the nature of business, interviews with owners, company background, strategies, challenges, recommendations, and signatures dishes. The conclusion is that Restaurant Banyonya would be more commercially successful based on the analysis.
Papeleria antonio ofertas 2.1
La papelería Antonio ofrece una variedad de artículos de oficina como cajas de clips, tijeras y tarjetas de felicitación. También anuncia varias promociones, incluyendo una oferta 3x1 en cajas de clips, un 40% de descuento en tijeras y un regalo de una tarjeta por cada compra superior a 10 euros. La papelería está ubicada en la avenida Isaac Newton y abre de 09:00 a 14:00 y de 15:30 a 20:00.
Pg 18 executivo 1 convocação tiro de pt .40
O documento lista nomes e números de registro geral de servidores penitenciários que participarão de cursos de capacitação em manuseio e operação de pistolas semiautomáticas. Também lista servidores que faltaram ao trabalho e obtiveram conceito insatisfatório em avaliações.
GOTO Communications Strategy
The document analyzes the target audience for GOTO, a nonprofit that provides art summer camps to underprivileged middle school students. It recommends targeting young professionals in New York aged 21-35 who are unmarried, college educated, and financially stable. This demographic is more likely to volunteer their time and socialize at events due to fewer responsibilities. The analysis includes a population overview, segmentation of the target based on demographics, and a SWOT analysis of GOTO.
Día de las madres
El documento describe a las madres como aquellas que traen al mundo a sus hijos y los cuidan, protegen y aman incondicionalmente. Celebra a las madres por su dedicación a la felicidad y bienestar de sus hijos, estando con ellos en buenos y malos momentos. Resalta que una madre da todo sin esperar nada a cambio.
BBFC
The BBFC classifies films into ratings in order to protect different age groups from unsuitable content. DNA Productions is classifying their thriller film "Hinge" as a 15 rating due to the level of violence and gore it contains, including brief scenes of torture and frequent displays of injury. This 15 rating will help them target their audience of 15-33 year olds.
Delta d product presentation
Delta produces portable torque measurement devices for quality control. The Delta 1D, 6D, and 7D models vary in capabilities from measuring only torque to measuring both torque and angle. The 6D and 7D can store tool and process settings, measure tools and processes, perform statistical analysis, and export data via software. The 7D additionally checks residual torque values using a digital wrench. All models aim to help manufacturers ensure quality and productivity.
Socialize your event
L'intervento del Social Media Specialist Gianluca Punzi per classe V di SQcuola di Blog
Erasmus+/YOUTH in Action: info over Europees vrijwilligerswerk
Informatie over Europees Vrijwilligerswerk (EVS):
Voor Vlaamse jongeren die voor een periode van 2 maanden tot een ajar vrijwilligerswerk in het buitenland willen doen, met de steun van Youth in Action.
Meer info op www.youthinaction.be
Enchentes na cidade - Petrópolis
Desastres climáticos como enchentes costumam ser imprevisíveis, mas políticas públicas eficazes de prevenção podem reduzir danos. Petrópolis sofreu nova enchente poucos anos depois da tragédia anterior, indicando que lições não foram aprendidas e medidas como remoção de moradias em áreas de risco ainda são tabus. É preciso que autoridades cumpram seu papel para evitar novas tragédias.
Viewers also liked (20)
North East Meet the Buyer seminar slideshow - 7 Nov 2013
North East Meet the Buyer seminar slideshow - 7 Nov 2013
finalpresentation3-151207154026-lva1-app6892-15121.doc
finalpresentation3-151207154026-lva1-app6892-15121.doc
Erasmus+/YOUTH in Action: info over Europees vrijwilligerswerk
Erasmus+/YOUTH in Action: info over Europees vrijwilligerswerk
Similar to A comparison of ultrasound intima media thickness measurements of the left and right common carotid artery
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...
This document summarizes a study that aims to detect Alzheimer's disease through neuroimaging and subsequently fuse magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. The study first detects Alzheimer's from MRI scans by segmenting gray and white matter and calculating their volume ratios. Image fusion is then performed on the MRI scan detected with Alzheimer's and a corresponding PET scan. The fusion combines the two scans into a single image containing structural and anatomical information to aid clinical diagnosis. The proposed method achieved good performance metrics for the fused image, with a peak signal-to-noise ratio of 60.6 dB, mean square error of 0.0176, and other favorable values, suggesting it yields more informative results than individual scans
USP.ppt
This document discusses quantitative analysis of radiologic images using techniques like image segmentation, registration, and statistical atlases. It notes that computers can help address human limitations in measurement, detection of subtle abnormalities, and evaluation of complex patterns. Key techniques include using statistical atlases to capture normal anatomical variation, registering images to integrate data from multiple individuals, and performing pattern classification using machine learning to aid diagnosis. The goal is for computers to complement humans by providing increased reproducibility, quantification, and analysis of non-focal or complex spatio-temporal changes.
Neuroengineering.pdf
Dr. Kurt Schilling's research focuses on image processing, modeling MRI data, and mapping the human brain using diffusion MRI. His research objectives include developing new analysis techniques to model tissue architecture from quantitative and multi-modal MRI data, exploiting high performance computing to perform large-scale image analysis, and designing robust systems to characterize uncertainty in image analysis algorithms.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRI
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
Description of Different Phases of Brain Tumor Classification
The proposed approach makes contributions in various stages in the development of a computer-aided diagnosis (CAD) system of brain diseases, namely image preprocessing, intermediate processing, detection, segmentation, feature extraction, and classification. Literature study incorporates many important ideas for abnormalities detection and analysis with their advantages and disadvantages. Literature studies have pointed out the needs of dividing task and appropriate ways for accurate abnormality characterization to provide a proper clinical diagnosis.
Medical image processing
This document discusses medical image processing and its application to breast cancer detection. It provides an overview of digital image processing techniques used in medical imaging like X-rays, mammography, ultrasound, MRI and CT. Computer-aided diagnosis (CAD) helps in tasks like visualization, detection, localization, segmentation and classification of medical images. For breast cancer detection specifically, the document discusses mammography and challenges in detecting tumors in dense breast tissue. It also reviews several published methods for segmenting and analyzing lesions in mammograms and evaluates their performance based on parameters like true positives, false positives, etc.
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
The Computational Biomedicine Lab has the following members: a director, research scientists, PhD students, an MSc student, and undergraduate students. The lab's mission is to develop algorithms to analyze multidimensional data and extract relevant information to aid humans. The lab aims to establish interdisciplinary collaboration and work on "grand challenge problems".
Survey on Segmentation Techniques for Spinal Cord Images
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
SCCT presentation
This study evaluated the reproducibility of quantitative coronary plaque assessment between two experienced observers using a semi-automated plaque quantification software. The observers analyzed 139 coronary artery segments in 13 patients undergoing CCTA. There were no significant differences found between the observers' measurements of total plaque volume, non-calcified plaque volume, fibrous fatty volume, necrotic core volume, or dense calcium volume. Reasonable correlations were observed between the two observers. The software provides a novel and reproducible approach to quantifying coronary plaque composition.
IRJET - Effective Heart Disease Prediction using Distinct Machine Learning Te...
This document discusses using machine learning techniques to predict heart disease. It proposes a new hybrid method combining machine learning techniques to improve prediction accuracy. Specifically, it introduces a prediction model using different feature combinations and classification techniques, including a hybrid random forest with linear model (HRFLM) that achieves 88.7% accuracy. Previous studies predicting heart disease using machine learning are also discussed, but the authors aim to develop a unique method focusing on feature selection to further improve prediction accuracy of heart disease.
An internet of things-based automatic brain tumor detection system
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection system
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
IRJET- Brain Tumor Detection and Identification using Support Vector Machine
This document presents a system for detecting and identifying brain tumors using Support Vector Machine (SVM) classifiers. The system was trained on a dataset of CT scan images of normal and abnormal brains. A Linear Function SVM (LF SVM) classifier achieved 100% accuracy in detecting normal brains and was able to correctly identify 64% of brain tumors. The LF SVM performed better than other classifiers and could detect tumors within 0.3525 seconds. The proposed system provides radiologists an accurate and fast method for detecting brain diseases to aid in diagnosis and treatment.
2014 CAN J NEUROL SCI - AD Initial clinical implementation of automated volum...
1) The document evaluates the implementation of an automated volumetry tool called FreeSurfer to quantify brain atrophy in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI).
2) FreeSurfer was used to measure volumes and thicknesses of the hippocampus, entorhinal cortex, and supramarginal gyrus in two cases - a patient with AD and one with MCI. The results from both patients fell within established ranges for their respective conditions.
3) The study found that FreeSurfer showed good correlation with clinical diagnoses of AD and MCI. This suggests the automated tool could be useful for diagnosing these conditions in clinical practice.
A hybrid approach to medical decision-making: diagnosis of heart disease wit...
Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
Automated Intracranial Neoplasm Detection Using Convolutional Neural Networks
This document presents a study that uses convolutional neural networks to automatically detect intracranial neoplasms (brain tumors) from MRI scans. The researchers developed a CNN model that achieved 97.87% accuracy in identifying tumors. They used preprocessed MRI images to train and test the model for tumor detection. Convolutional neural networks are a type of deep learning that can provide efficient results for medical image classification tasks like tumor detection compared to traditional methods. The study demonstrates that CNNs are a promising approach for automated brain tumor identification from MRI scans.
Classification of Abnormalities in Brain MRI Images Using PCA and SVM
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
Bt36430432
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.
Identifying brain tumour from mri image using modified fcm and support
This document summarizes a research paper that proposes a technique for identifying brain tumors in MRI images. The technique involves 4 steps: 1) preprocessing the MRI image, 2) segmenting the image using a modified fuzzy C-means algorithm, 3) extracting features from the segmented regions like mean, standard deviation, and pixel orientation, and 4) classifying the image as tumorous or normal using support vector machine classification on the extracted features. The technique is evaluated on MRI brain images and achieves a testing accuracy of 93%, demonstrating its effectiveness at detecting brain tumors compared to other segmentation and classification methods.
An overview of meta analysis in the field of cardiovascular imaging using art...
• AI will completely change the era of medicine by doctors, mainly in cardiology and radiology.
• Pubrica is conducting a meta-analysis in quantitative research about cardiovascular imaging to help future medical researchers and doctors.
Full Information: https://bit.ly/2FvQ68c
Reference: https://pubrica.com/services/research-services/meta-analysis/
Why Pubrica?
When you order our services, we promise you the following – Plagiarism free, always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Similar to A comparison of ultrasound intima media thickness measurements of the left and right common carotid artery (20)
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...
Alzheimer’s detection through neuro imaging and subsequent fusion for clinica...
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRI
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRI
Description of Different Phases of Brain Tumor Classification
Description of Different Phases of Brain Tumor Classification
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Computational Biomedicine Lab: Current Members, pumpsandpipesmdhc
Survey on Segmentation Techniques for Spinal Cord Images
Survey on Segmentation Techniques for Spinal Cord Images
IRJET - Effective Heart Disease Prediction using Distinct Machine Learning Te...
IRJET - Effective Heart Disease Prediction using Distinct Machine Learning Te...
An internet of things-based automatic brain tumor detection system
An internet of things-based automatic brain tumor detection system
An internet of things-based automatic brain tumor detection system
An internet of things-based automatic brain tumor detection system
IRJET- Brain Tumor Detection and Identification using Support Vector Machine
IRJET- Brain Tumor Detection and Identification using Support Vector Machine
2014 CAN J NEUROL SCI - AD Initial clinical implementation of automated volum...
2014 CAN J NEUROL SCI - AD Initial clinical implementation of automated volum...
A hybrid approach to medical decision-making: diagnosis of heart disease wit...
A hybrid approach to medical decision-making: diagnosis of heart disease wit...
Automated Intracranial Neoplasm Detection Using Convolutional Neural Networks
Automated Intracranial Neoplasm Detection Using Convolutional Neural Networks
Classification of Abnormalities in Brain MRI Images Using PCA and SVM
Classification of Abnormalities in Brain MRI Images Using PCA and SVM
Identifying brain tumour from mri image using modified fcm and support
Identifying brain tumour from mri image using modified fcm and support
An overview of meta analysis in the field of cardiovascular imaging using art...
An overview of meta analysis in the field of cardiovascular imaging using art...
More from LogicMindtech Nologies
Utilizing image scales towards totally training free blind image quality asse...
Utilizing image scales towards totally training free blind image quality asse...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Thermal imaging as a biometrics approach to facial signature authentication
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Template ip
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Targeting accurate object extraction from an image a comprehensive study of ...
Targeting accurate object extraction from an image a comprehensive study of ...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Sparse dissimilarity constrained coding for glaucoma screening
The document describes a method for glaucoma screening using retinal fundus images. Glaucoma is an irreversible eye disease that can cause vision loss if not detected early. The proposed method uses a novel sparse dissimilarity-constrained coding approach to segment and reconstruct the optic disc from fundus images. Reconstruction coefficients are used to calculate the cup to disc ratio, a metric for detecting glaucoma. The method was tested on 650 images and achieved better accuracy than other methods, with an average error of 0.064 compared to manual measurements. It also achieved good performance in glaucoma screening tests on two datasets. The method shows potential for large-scale population-based glaucoma screening using low-cost retinal imaging.
Sar sift a sift like algorithm for sar images
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Optic disc boundary and vessel origin
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
On criminalidentificationin color skin images usingskin marks (rppvsm) and fu...
On criminalidentificationin color skin images usingskin marks (rppvsm) and fu...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Multi scale two-directional two-dimensional
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Measuring calorie and nutrition from food image
This document proposes a food calorie and nutrition measurement system that uses images taken by mobile device cameras to measure consumption and manage daily food intake. It analyzes food photos before and after eating using nutritional databases and image processing. The system aims to improve current manual calorie counting methods. It requires a PC with at least 2GB RAM and 100GB HDD along with MATLAB software and toolboxes to implement image and signal processing algorithms. The project will be developed over three reviews, first surveying existing methods, explaining the design, and enhancing the base paper. It will then implement 40% of the base paper in the second review and the remaining 60% with modifications in the third review.
Lossless and reversible data hiding in encrypted images with public key crypt...
Lossless and reversible data hiding in encrypted images with public key crypt...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Joint super resolution and denoising from a single depth image
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Iterative vessel segmentation of fundus images
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Influence of color to gray conversion on the performance of document image bi...
Influence of color to gray conversion on the performance of document image bi...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Improving pixel based change detection accuracy using an object-based approac...
Improving pixel based change detection accuracy using an object-based approac...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Image quality assessment for fake biometric detection application to iris f...
Image quality assessment for fake biometric detection application to iris f...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Image forgery detection using adaptive over segmentation and feature point ma...
Image forgery detection using adaptive over segmentation and feature point ma...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Haze removal for a single remote sensing image based on deformed haze imaging...
Haze removal for a single remote sensing image based on deformed haze imaging...LogicMindtech Nologies
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Face recognition by exploiting local gabor
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
Enhancing color images of extremely low light scenes based on rgb nir images ...
Enhancing color images of extremely low light scenes based on rgb nir images ...LogicMindtech Nologies
This document describes a project to enhance color images taken in extremely low light conditions. It proposes capturing RGB and NIR images simultaneously with different exposure times. An RGB image is taken with long exposure to acquire color information while NIR images are taken with short exposure to measure scene structure. These image pairs are then used to reconstruct a latent color image sequence using gradient and color correlations. The required hardware is a PC with at least 2GB RAM and 100GB HDD, and the necessary software includes MATLAB with signal, image, and mathematical toolboxes. The project flow involves a literature review, paper explanation, design, enhancement explanation, and implementation in stages.More from LogicMindtech Nologies (20)
Utilizing image scales towards totally training free blind image quality asse...
Utilizing image scales towards totally training free blind image quality asse...
Thermal imaging as a biometrics approach to facial signature authentication
Thermal imaging as a biometrics approach to facial signature authentication
Targeting accurate object extraction from an image a comprehensive study of ...
Targeting accurate object extraction from an image a comprehensive study of ...
Sparse dissimilarity constrained coding for glaucoma screening
Sparse dissimilarity constrained coding for glaucoma screening
On criminalidentificationin color skin images usingskin marks (rppvsm) and fu...
On criminalidentificationin color skin images usingskin marks (rppvsm) and fu...
Lossless and reversible data hiding in encrypted images with public key crypt...
Lossless and reversible data hiding in encrypted images with public key crypt...
Joint super resolution and denoising from a single depth image
Joint super resolution and denoising from a single depth image
Influence of color to gray conversion on the performance of document image bi...
Influence of color to gray conversion on the performance of document image bi...
Improving pixel based change detection accuracy using an object-based approac...
Improving pixel based change detection accuracy using an object-based approac...
Image quality assessment for fake biometric detection application to iris f...
Image quality assessment for fake biometric detection application to iris f...
Image forgery detection using adaptive over segmentation and feature point ma...
Image forgery detection using adaptive over segmentation and feature point ma...
Haze removal for a single remote sensing image based on deformed haze imaging...
Haze removal for a single remote sensing image based on deformed haze imaging...
Enhancing color images of extremely low light scenes based on rgb nir images ...
Enhancing color images of extremely low light scenes based on rgb nir images ...
Recently uploaded
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Software Engineering and Project Management - Introduction, Modeling Concepts...
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
artificial intelligence and data science contents.pptx
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Recently uploaded (20)
Software Engineering and Project Management - Introduction, Modeling Concepts...
Software Engineering and Project Management - Introduction, Modeling Concepts...
Rainfall intensity duration frequency curve statistical analysis and modeling...
Rainfall intensity duration frequency curve statistical analysis and modeling...
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
artificial intelligence and data science contents.pptx
artificial intelligence and data science contents.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Manufacturing Process of molasses based distillery ppt.pptx
Data Control Language.pptx Data Control Language.pptx
Data Control Language.pptx Data Control Language.pptx
A comparison of ultrasound intima media thickness measurements of the left and right common carotid artery
- 1. Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Title: A Comparison of ultrasound Intima- media Thickness Measurements of the Left and Right Common Carotid Artery Abstract— The intima–media thickness (IMT) of the common carotid artery (CCA) is an established indicator of cardiovascular disease (CVD). There have been reports about the difference between the left and the right sides of the CCA IMT and their relation with cardiovascular disease. In this study, we propose an automated system based on image normalisation, speckle reduction filtering and snakes segmentation, for segmenting the CCA, perform IMT measurements and provide the differences between the left and right sides. The study was performed on 1104 longitudinal-section ultrasound images acquired from 568 men and 536 women out of which 125 had cardiovascular symptoms (CVD). A cardiovascular expert manually delineated the IMT for the normal and the CVD groups. The corresponding (normal vs CVD) IMT mean ± standard deviation values for the left and right sides were 0.74±0.24 vs 0.87±0.24 mm and 0.70±0.17 vs 0.80±0.18 mm respectively. The main findings of this study can be summarised as follows: (1) there was no significant difference between the CCA left side IMT and the right side IMT. These findings suggest that the measurement of the CCA IMT on one side only is needed for the normal group (and this is in agreement with other studies); (2) there were statistical significant differences for the IMT measurements between the normal group and the CVD group for both the left and the right sides; (3) there was an increasing linear relationship of the left and right IMT measurements with age for the normal group; and to a lesser extend for the CVD group; (4) no statistical significant differences were found between the manual and the automated IMT measurements for both sides; (5) the best result for classification disease modelling, using support vector machines (SVM), to discriminate between the normal and the CVD groups was a 64±3.5% correct classifications score when using both the left and the right IMT automated measurements. Further research is required for estimating differences and similarities between left and right intima media complex structure and morphology and their variability with texture features for differentiating between the normal and the cardiovascular disease group.
- 2. Software & Hardware requirement: 1. Hardware requirement: 1. PC 2. RAM minimum 2GB 3. HDD minimum 100GB 2. Software requirement: 1. MATLAB 7.0 2. Signal processing toolbox 3. Image processing toolbox 4. Mathematical toolbox PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) For More Details please contact Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066