Computerized medical image segmentation is a challenging area because of poor resolution
and weak contrast. The predominantly used conventional clustering techniques and the
thresholding methods suffer from limitations owing to their heavy dependence on user
interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The
performance further deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering
for segmenting vertebral body from magnetic resonance images. The motivation for this work is
that spine appearance, shape and geometry measurements are necessary for abnormality
detection and thus proper localisation and labelling will enhance the diagnostic output of a
physician. The method is compared with Otsu thresholding and K-means clustering to illustrate
the robustness. The reference standard for validation was the annotated images from the
radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.
Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Imagesijfls
Image segmentation in the medical domain is a challenging field owing to poor resolution and limited
contrast. The predominantly used conventional segmentation techniques and the thresholding methods
suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an
image cannot be captured by these techniques. The performance further deteriorates when the images are
corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust
fuzzy C- means clustering for segmenting vertebral body from magnetic resonance image owing to its
unsupervised form of learning. The motivation for this work is detection of spine geometry and proper
localisation and labelling will enhance the diagnostic output of a physician. The method is compared with
Otsu thresholding and K-means clustering to illustrate the robustness.The reference standard for validation
was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures
were used to evaluate the segmentation
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
The accurate determination of the sex and age of human skull is a critical challenge in skeleton anthropology and crime department. In the forensic
laboratory they determine both the sex and age of skeleton using carbon content of the bones. The teeth, pelvis and skull are the most widely used sites
for determination of sex and age of the skeleton. This paper introduces a technique for objective qualification of age and sexual dimorphic features
using wavelet transformation, it is a multiscale mathematical technique that allows determination of shape variation that are hide at various scale of
resolution. We use a 2D discrete wavelet transform in the proposed method. In the skull the supraorbital margin is consider to determine sex of skull
and the area occupation of upper part of skull is used to estimate the age of the skull. SVM is a classifier used for classification. We used both
supervised and unsupervised SVM for both sex and age detection of the skull.
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
Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Imagesijfls
Image segmentation in the medical domain is a challenging field owing to poor resolution and limited
contrast. The predominantly used conventional segmentation techniques and the thresholding methods
suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an
image cannot be captured by these techniques. The performance further deteriorates when the images are
corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust
fuzzy C- means clustering for segmenting vertebral body from magnetic resonance image owing to its
unsupervised form of learning. The motivation for this work is detection of spine geometry and proper
localisation and labelling will enhance the diagnostic output of a physician. The method is compared with
Otsu thresholding and K-means clustering to illustrate the robustness.The reference standard for validation
was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures
were used to evaluate the segmentation
PERFORMANCE ANALYSIS OF TEXTURE IMAGE RETRIEVAL FOR CURVELET, CONTOURLET TRAN...ijfcstjournal
Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an
abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to
detect the MRI brain tumor images. There are two parts, namely; feature extraction process and
classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet
transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural
network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of
1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform
technique provides better than curvelet transform and Local ternary pattern.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
The accurate determination of the sex and age of human skull is a critical challenge in skeleton anthropology and crime department. In the forensic
laboratory they determine both the sex and age of skeleton using carbon content of the bones. The teeth, pelvis and skull are the most widely used sites
for determination of sex and age of the skeleton. This paper introduces a technique for objective qualification of age and sexual dimorphic features
using wavelet transformation, it is a multiscale mathematical technique that allows determination of shape variation that are hide at various scale of
resolution. We use a 2D discrete wavelet transform in the proposed method. In the skull the supraorbital margin is consider to determine sex of skull
and the area occupation of upper part of skull is used to estimate the age of the skull. SVM is a classifier used for classification. We used both
supervised and unsupervised SVM for both sex and age detection of the skull.
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
Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
Medical Image Segmentation by Transferring Ground Truth Segmentation Based up...csandit
In this paper, we present a novel method for image segmentation of the hip joint structure. The
key idea is to transfer the ground truth segmentation from the database to the test image. The
ground truth segmentation of MR images is done by medical experts. The process includes the
top down approach which register the shape of the test image globally and locally with the
database of train images. The goal of top down approach is to find the best train image for each
of the local test image parts. The bottom up approach replaces the local test parts by best train
image parts, and inverse transform the best train image parts to represent a test image by the
mosaic of best train image parts. The ground truth segmentation is transferred from best train
image parts to their corresponding location in the test image.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
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.
Signs of Benign Breast Disease in 3D TomosynthesisApollo Hospitals
The role of three dimensional tomosynthesis in margin analysis especially for malignant lesions is well known (reference 2) .Three dimensional tomosynthesis has specific signs in lesions for categorization of Benign & Malignant pathology resulting in efficient & effective diagnosis. Three dimensional Tomosynthesis has been found to reduce recall rates(reference 3 , 4 ) because of better assessment and categorisation of breast lesions.
Hierarchical Vertebral Body Segmentation Using Graph Cuts and Statistical Sha...IJTET Journal
Abstract— Bone Mineral Density (BMD) estimations and fracture investigation of the spine bones are retrained to the vertebral bodies (VBs).A contemporary shape and appearance based method is proposed to segment VBs in clinical Computed Tomography (CT) images without any user arbitration. The proposed approach depends on both image appearance and shape information. Shape knowledge is aggregated from a set of training shapes. Then shape variations are estimated using statistical shape model which approximates the shape variations of the vertebral bodies and its background in the variability region. To segment a VB, the graph cut method used to detect the VB region automatically. Detected contours are aligned and mean shape model is created. The spatial interaction between the neighboring pixels is identified. The statistical shape model is used to produce the deformable shape model and all instances of the shape lies with the current estimate of the mean shape.
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
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HA...csandit
Optical Character recognition is the method of digitalization of hand and type written or
printed text into machine-encoded form and is superfluity of the various applications of envision
of human’s life. In present human life OCR has been successfully using in finance, legal,
banking, health care and home need appliances. India is a multi cultural, literature and
traditional scripted country. Telugu is the southern Indian language, it is a syllabic language,
symbol script represents a complete syllable and formed with the conjunct mixed consonants in
their representation. Recognition of mixed conjunct consonants is critical than the normal
consonants, because of their variation in written strokes, conjunct maxing with pre and post
level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient
classification of the hand written and printed conjunct consonants. This paper implements the
Advanced Fuzzy Logic system controller to take the text in the form of written or printed,
collected the text images from the scanned file, digital camera, Processing the Image with
Examine the high intensity of images based on the quality ration, Extract the image characters
depends on the quality then check the character orientation and alignment then to check the
character thickness, base and print ration. The input image characters can classify into the two
ways, first way represents the normal consonants and the second way represents conjunct
consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here
recognition process starts from middle layer, and then it continues to check the top and bottom
layers. The recognition process treat as conjunct consonants when it can detect any symbolic
characters in top and bottom layers of present base character otherwise treats as normal
consonants. The post processing technique applied to all three layered characters. Post
processing of the image: concentrated on the image text readability and compatibility, if the
readability is not process then repeat the process again. In this recognition process includes
slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character
recognition and post-processing modules were discussed. The main objectives to the
development of this paper are: To develop the classification, identification of deference
prototyping for written and printed consonants, conjunct consonants and symbols based on 3
layered approaches with different measurable area by using fuzzy logic and to determine
suitable features for handwritten character recognition.
A Cross Layer Based Scalable Channel Slot Re-Utilization Technique for Wirele...csandit
Due to tremendous growth of the wireless based application services are increasing the demand
for wireless communication techniques that use bandwidth more effectively. Channel slot reutilization
in multi-radio wireless mesh networks is a very challenging problem. WMNs have
been adopted as back haul to connect various networks such as Wi-Fi (802.11), WI-MAX
(802.16e) etc. to the internet. The slot re-utilization technique proposed so far suffer due to high
collision due to improper channel slot usage approximation error. To overcome this here the
author propose the cross layer optimization technique by designing a device classification
based channel slot re-utilization routing strategy which considers the channel slot and node
information from various layers and use some of these parameters to approximate the risk
involve in channel slot re-utilization in order to improve the QoS of the network. The simulation
and analytical results show the effectiveness of our proposed approach in term of channel slot
re-utilization efficiency and thus helps in reducing latency for data transmission and reduce
channel slot collision.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
FILESHADER: ENTRUSTED DATA INTEGRATION USING HASH SERVER csandit
The importance of security is increasing in a current network system. We have found a big
security weakness at the file integration when the people download or upload a file and propose
a novel solution how to ensure the security of a file. In particular, hash value can be applied to
ensure a file due to a speed and architecture of file transfer. Hash server stores all the hash
values which are updated by file provider and client can use these values to entrust file when it
downloads. FileShader detects to file changes correctly, and we observed that it did not show
big performance degradation. We expect FileShader can be applied current network systems
practically, and it can increase a security level of all internet users.
Literature survey for 3 d reconstruction of brain mri imageseSAT Journals
Abstract
Since Doctors had only the 2D Image Data to visualize the tumors in the MRI images, which never gave the actual feel of how the tumor would exactly look like . The doctors were deprived from the exact visualization of the tumor the amount of the tumor to be removed by operation was not known, which caused a lot of deformation in the faces and structure of the patients face or skull. The diversity and complexity of tumor cells makes it very challenging to visualize tumor present in magnetic resonance image (MRI) data. Hence to visualize the tumor properly 2D MRI image has to be converted to 3D image. With the development of computer image processing technology, three-dimensional (3D) visualization has become an important method of the medical diagnose, it offers abundant and accurate information for medical experts. Three-dimensional (3-D) reconstruction of medical images is widely applied to tumor localization; surgical planning and brain electromagnetic field computation etc. The brain MR images have unique characteristics, i.e., very complicated changes of the gray-scales and highly irregular boundaries. Traditional 3-D reconstruction algorithms are challenged in solving this problem. Many reconstruction algorithms, such as marching cubes and dividing cubes, need to establish the topological relationship between the slices of images. The results of these traditional approaches vary depending on the number of input sections, their positions, the shape of the original body and the applied interpolation technique. These make the task tedious and time-consuming. Moreover, satisfied reconstruction result may not even be obtained when the highly irregular objects such as the encephalic tissues are considered. Due to complexity and irregularity of each encephalic tissue boundary, three-dimensional (3D) reconstruction for MRI image is necessary. A Literature survey is done to study different methods of 3D reconstruction of brain images from MRI images. Keywords: 3-D reconstruction, region growing, segmentation method, immune algorithm (IA), one class support vector machine (OCSVM) and sphere shaped support vector machine (SSSVM).
This paper primarily focuses on to employ a novel approach to classify the brain tumor and its area. The Tumor is an uncontrolled enlargement of tissues in any portion of the human body. Tumors are of several types and have some different characteristics. According to their characteristics some of them are avoidable and some are unavoidable. Brain tumor is serious and life threatening issues now days, because of today’s hectic lifestyle. Medical imaging play important role to diagnose brain tumor .In this study an automated system has been proposed to detect and calculate the area of tumor. For proposed system the experiment carried out with 150 T1 weighted MRI images. The edge based segmentation, watershed segmentation has applied for tumor, and watershed segmentation has used to extract abnormal cells from the normal cells to get the tumor identification of involved and noninvolved areas so that the radiologist differentiate the affected area. The experiment result shows tumor extraction and area of tumor find the weather it is benign and malignant.
Medical Image Segmentation by Transferring Ground Truth Segmentation Based up...csandit
In this paper, we present a novel method for image segmentation of the hip joint structure. The
key idea is to transfer the ground truth segmentation from the database to the test image. The
ground truth segmentation of MR images is done by medical experts. The process includes the
top down approach which register the shape of the test image globally and locally with the
database of train images. The goal of top down approach is to find the best train image for each
of the local test image parts. The bottom up approach replaces the local test parts by best train
image parts, and inverse transform the best train image parts to represent a test image by the
mosaic of best train image parts. The ground truth segmentation is transferred from best train
image parts to their corresponding location in the test image.
Medical Image segmentation using Image Mining conceptsEditor IJMTER
Image differencing is usually done by subtracting the low-level skin texture like strength
in images that are already associated. This paper extracts high-level skin texture in order to find out
an efficient image differencing method for the analysis of Brain Tumor. On the other hand, this
produces sets of skin texture that are both spatial. We demonstrate a technique that avoids arbitrary
spatial constraints and is robust in the presence of sound, outliers, and imaging artifact, while
outperforming even profitable products in the analysis of Brain Tumor images. First, the landmark
are establish, and then the top entrant are sorted into a end set. Second, the top sets of the two
descriptions are then differenced through a cluster judgment. The symmetry of the human body is
utilized to increase the accuracy of the finding. We imitate this technique in an effort to understand
and ultimately capture the judgment of the radiologist. The image differencing with clustered
contrast process determines the being there of Brain Tumor. Using the most favorable features
extracted from normal and tumor regions of MRI by using arithmetical features, classifiers are used
to categorize and segment the tumor portion in irregular images. Both the difficult and preparation
phase gives the proportion of accuracy on each parameter in neural networks, which gives the idea to
decide the best one to be used in supplementary works. The results showed outperformance of
algorithm when compared with classification accuracy which works as shows potential tool for
classification and requires extension in brain tumor analysis.
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.
Signs of Benign Breast Disease in 3D TomosynthesisApollo Hospitals
The role of three dimensional tomosynthesis in margin analysis especially for malignant lesions is well known (reference 2) .Three dimensional tomosynthesis has specific signs in lesions for categorization of Benign & Malignant pathology resulting in efficient & effective diagnosis. Three dimensional Tomosynthesis has been found to reduce recall rates(reference 3 , 4 ) because of better assessment and categorisation of breast lesions.
Hierarchical Vertebral Body Segmentation Using Graph Cuts and Statistical Sha...IJTET Journal
Abstract— Bone Mineral Density (BMD) estimations and fracture investigation of the spine bones are retrained to the vertebral bodies (VBs).A contemporary shape and appearance based method is proposed to segment VBs in clinical Computed Tomography (CT) images without any user arbitration. The proposed approach depends on both image appearance and shape information. Shape knowledge is aggregated from a set of training shapes. Then shape variations are estimated using statistical shape model which approximates the shape variations of the vertebral bodies and its background in the variability region. To segment a VB, the graph cut method used to detect the VB region automatically. Detected contours are aligned and mean shape model is created. The spatial interaction between the neighboring pixels is identified. The statistical shape model is used to produce the deformable shape model and all instances of the shape lies with the current estimate of the mean shape.
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
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
OCR-THE 3 LAYERED APPROACH FOR CLASSIFICATION AND IDENTIFICATION OF TELUGU HA...csandit
Optical Character recognition is the method of digitalization of hand and type written or
printed text into machine-encoded form and is superfluity of the various applications of envision
of human’s life. In present human life OCR has been successfully using in finance, legal,
banking, health care and home need appliances. India is a multi cultural, literature and
traditional scripted country. Telugu is the southern Indian language, it is a syllabic language,
symbol script represents a complete syllable and formed with the conjunct mixed consonants in
their representation. Recognition of mixed conjunct consonants is critical than the normal
consonants, because of their variation in written strokes, conjunct maxing with pre and post
level of consonants. This paper proposes the layered approach methodology to recognize the
characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient
classification of the hand written and printed conjunct consonants. This paper implements the
Advanced Fuzzy Logic system controller to take the text in the form of written or printed,
collected the text images from the scanned file, digital camera, Processing the Image with
Examine the high intensity of images based on the quality ration, Extract the image characters
depends on the quality then check the character orientation and alignment then to check the
character thickness, base and print ration. The input image characters can classify into the two
ways, first way represents the normal consonants and the second way represents conjunct
consonants. Digitalized image text divided into three layers, the middle layer represents normal
consonants and the top and bottom layer represents mixed conjunct consonants. Here
recognition process starts from middle layer, and then it continues to check the top and bottom
layers. The recognition process treat as conjunct consonants when it can detect any symbolic
characters in top and bottom layers of present base character otherwise treats as normal
consonants. The post processing technique applied to all three layered characters. Post
processing of the image: concentrated on the image text readability and compatibility, if the
readability is not process then repeat the process again. In this recognition process includes
slant correction, thinning, normalization, segmentation, feature extraction and classification. In
the process of development of the algorithm the pre-processing, segmentation, character
recognition and post-processing modules were discussed. The main objectives to the
development of this paper are: To develop the classification, identification of deference
prototyping for written and printed consonants, conjunct consonants and symbols based on 3
layered approaches with different measurable area by using fuzzy logic and to determine
suitable features for handwritten character recognition.
A Cross Layer Based Scalable Channel Slot Re-Utilization Technique for Wirele...csandit
Due to tremendous growth of the wireless based application services are increasing the demand
for wireless communication techniques that use bandwidth more effectively. Channel slot reutilization
in multi-radio wireless mesh networks is a very challenging problem. WMNs have
been adopted as back haul to connect various networks such as Wi-Fi (802.11), WI-MAX
(802.16e) etc. to the internet. The slot re-utilization technique proposed so far suffer due to high
collision due to improper channel slot usage approximation error. To overcome this here the
author propose the cross layer optimization technique by designing a device classification
based channel slot re-utilization routing strategy which considers the channel slot and node
information from various layers and use some of these parameters to approximate the risk
involve in channel slot re-utilization in order to improve the QoS of the network. The simulation
and analytical results show the effectiveness of our proposed approach in term of channel slot
re-utilization efficiency and thus helps in reducing latency for data transmission and reduce
channel slot collision.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
FILESHADER: ENTRUSTED DATA INTEGRATION USING HASH SERVER csandit
The importance of security is increasing in a current network system. We have found a big
security weakness at the file integration when the people download or upload a file and propose
a novel solution how to ensure the security of a file. In particular, hash value can be applied to
ensure a file due to a speed and architecture of file transfer. Hash server stores all the hash
values which are updated by file provider and client can use these values to entrust file when it
downloads. FileShader detects to file changes correctly, and we observed that it did not show
big performance degradation. We expect FileShader can be applied current network systems
practically, and it can increase a security level of all internet users.
Geometric Correction for Braille Document Images csandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
COQUEL: A CONCEPTUAL QUERY LANGUAGE BASED ON THE ENTITYRELATIONSHIP MODELcsandit
As more and more collections of data are available on the Internet, end users but not experts in
Computer Science demand easy solutions for retrieving data from these collections. A good
solution for these users is the conceptual query languages, which facilitate the composition of
queries by means of a graphical interface. In this paper, we present (1) CoQueL, a conceptual
query language specified on E/R models and (2) a translation architecture for translating
CoQueL queries into languages such as XQuery or SQL..
Associative Regressive Decision Rule Mining for Predicting Customer Satisfact...csandit
Opinion mining also known as sentiment analysis, involves customer satisfactory patterns,
sentiments and attitudes toward entities, products, services and their attributes. With the rapid
development in the field of Internet, potential customer’s provides a satisfactory level of
product/service reviews. The high volume of customer reviews were developed for
product/review through taxonomy-aware processing but, it was difficult to identify the best
reviews. In this paper, an Associative Regression Decision Rule Mining (ARDRM) technique is
developed to predict the pattern for service provider and to improve customer satisfaction based
on the review comments. Associative Regression based Decision Rule Mining performs twosteps
for improving the customer satisfactory level. Initially, the Machine Learning Bayes
Sentiment Classifier (MLBSC) is used to classify the class labels for each service reviews. After
that, Regressive factor of the opinion words and Class labels were checked for Association
between the words by using various probabilistic rules. Based on the probabilistic rules, the
opinion and sentiments effect on customer reviews, are analyzed to arrive at specific set of
service preferred by the customers with their review comments. The Associative Regressive
Decision Rule helps the service provider to take decision on improving the customer satisfactory
level. The experimental results reveal that the Associative Regression Decision Rule Mining
(ARDRM) technique improved the performance in terms of true positive rate, Associative
Regression factor, Regressive Decision Rule Generation time and Review Detection Accuracy of
similar pattern.
GAUSSIAN KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM FOR IMAGE SEGMENTATIONcsandit
Image processing is an important research area in computer vision. clustering is an unsupervised
study. clustering can also be used for image segmentation. there exist so many methods for image
segmentation. image segmentation plays an important role in image analysis.it is one of the first
and the most important tasks in image analysis and computer vision. this proposed system
presents a variation of fuzzy c-means algorithm that provides image clustering. the kernel fuzzy
c-means clustering algorithm (kfcm) is derived from the fuzzy c-means clustering
algorithm(fcm).the kfcm algorithm that provides image clustering and improves accuracy
significantly compared with classical fuzzy c-means algorithm. the new algorithm is called
gaussian kernel based fuzzy c-means clustering algorithm (gkfcm)the major characteristic of
gkfcm is the use of a fuzzy clustering approach ,aiming to guarantee noise insensitiveness and
image detail preservation.. the objective of the work is to cluster the low intensity in homogeneity
area from the noisy images, using the clustering method, segmenting that portion separately using
content level set approach. the purpose of designing this system is to produce better segmentation
results for images corrupted by noise, so that it can be useful in various fields like medical image
analysis, such as tumor detection, study of anatomical structure, and treatment planning.
Mining Fuzzy Association Rules from Web Usage Quantitative Data csandit
Web usage mining is the method of extracting interesting patterns from Web usage log file. Web
usage mining is subfield of data mining uses various data mining techniques to produce
association rules. Data mining techniques are used to generate association rules from
transaction data. Most of the time transactions are boolean transactions, whereas Web usage
data consists of quantitative values. To handle these real world quantitative data we used fuzzy
data mining algorithm for extraction of association rules from quantitative Web log file. To
generate fuzzy association rules first we designed membership function. This membership
function is used to transform quantitative values into fuzzy terms. Experiments are carried out
on different support and confidence. Experimental results show the performance of the
algorithm with varied supports and confidence.
GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
A Routing Protocol Orphan-Leach to Join Orphan Nodes in Wireless Sensor Netwo...csandit
The hierarchical routing protocol LEACH (Low Energy Adaptive Clustering Hierarchy) is
referred to as the basic algorithm of distributed clustering protocols. LEACH allows clusters
formation. Each cluster has a leader called Cluster Head (CH). The selection of CHs is made
with a probabilistic calculation. It is supposed that each non-CH node join a cluster and
becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the
network. Thus several sensor nodes cannot reach any CH. As a result, the remaining part of the
controlled field will not be covered; some sensor nodes will be outside the network. To solve this
problem, we propose O-LEACH (Orphan Low Energy Adaptive Clustering Hierarchy) a routing
protocol that takes into account the orphan nodes. Indeed, a cluster member will be able to play
the role of a gateway which allows the joining of orphan nodes. If a gateway node has to
connect a important number of orphan nodes, thus a sub-cluster is created and the gateway
node is considered as a CH’ for connected orphans. As a result, orphan nodes become able to
send their data messages to the CH which performs in turn data aggregation and send
aggregated data message to the CH. The WSN application receives data from the entire network
including orphan nodes.
The simulation results show that O-LEACH performs better than LEACH in terms of
connectivity rate, energy, scalability and coverage.
REVIEW PAPER ON NEW TECHNOLOGY BASED NANOSCALE TRANSISTORmsejjournal
Owing to the fact that MOSFETs can be effortlessly assimilated into ICs, they have become the heart of the
growing semiconductor industry. The need to procure low power dissipation, high operating speed and
small size requires the scaling down of these devices. This fully serves the Moore’s Law. But scaling down
comes with its own drawbacks which can be substantiated as the Short Channel Effect. The working of the
device deteriorates owing to SCE. In this paper, the problems of device downsizing as well as how the use
of SED based devices prove to be a better solution to device downsizing has been presented. As such the
study of Short Channel effects as well as the issues associated with a nanoMOSFET is provided. The study
of the properties of several Quantum dot materials and how to choose the best material depending on the
observation of clear Coulomb blockade is done. Specifically, a study of a graphene single electron
transistor is reviewed. Also a theoretical explanation to a model designed to tune the movement of
electrons with the help of a quantum wire has been presented.
FUZZY CLUSTERING BASED SEGMENTATION OF VERTEBRAE IN T1-WEIGHTED SPINAL MR IMA...Wireilla
Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C- means clustering for segmenting vertebral body from magnetic resonance image owing to its unsupervised form of learning. The motivation for this work is detection of spine geometry and proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness.The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.
Osteoarthritis (OA) is the most common form of arthritis seen in aged or older populations. It is caused
because of a degeneration of articular cartilage, which functions as shock absorption cushion in knee joint. OA
also leads sliding of bones together, cause swelling, pain, eventually and loss of motion. Nowadays, magnetic
resonance imaging (MRI) technique is widely used in the progression of osteoarthritis diagnosis due to the ability
to display the contrast between bone and cartilage. Usually, analysis of MRI image is done manually by a
physician which is very unpredictable, subjective and time consuming. Hence, there is need to develop automated
system to reduce the processing time. In this paper, a new automatic knee OA detection system based on feature
extraction and artificial neural network is developed. The different features viz GLCM texture, statistical, shape
etc. is extracted by using different image processing algorithms. This detection system consists of 4 stages, which
are pre-processing with ROI cropping, segmentation, feature extraction, and classification by neural network. This
technique results 98.5% of classification accuracy at training stage and 92% at testing stage.
Keywords — Artificial Neural Network (ANN), Gray Level Co-occurrence Matrix (GLCM),Knee
Joint, Magnetic Resonance Imaging (MRI), Osteoarthritis(OA).
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herni...IJECEIAES
This article studies and analyzes the use of 3D models, built from magnetic reso- nance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
Mri image registration based segmentation framework for whole hearteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
Fully Automatic Method for 3D T1-Weighted Brain Magnetic Resonance Images Seg...CSCJournals
In the domain of medical imaging, accurate segmentation of brain MR images is of interest for many brain disorders. However, due to several factors such noise, imaging artefacts, intrinsic tissue variation and partial volume effects, tissue segmentation remains a challenging task. So, in this paper, a full automatic method for segmentation of brain MR images is presented. The method consists of four steps segmentation procedure. First, noise removing by median filtering is done; second segmentation of brain/non-brain tissue is performed by using a Threshold Morphologic Brain Extraction method (TMBE). Then initial centroids estimation by gray level histogram analysis based is executed. Finally, Fuzzy C-means Algorithm is used for MRI tissue segmentation. The efficiency of the proposed method is demonstrated by extensive segmentation experiments using simulated and real MR images.
Brain Tumor Segmentation and Volume Estimation from T1-Contrasted and T2 MRIsCSCJournals
Amid the variations of the cancer disease, brain tumors account for the majority deaths among young people. To diagnose and treat this deadly disease effectively, analysis of hundreds of medical images such as Magnetic Resonance Imaging (MRI) scans is usually performed. However, the analyses of these scans are still mainly performed manually, making the procedure not only very tedious and time-consuming for doctors, but also error prone and non-repeatable. Attempts have been made to automate this procedure by performing image processing techniques such as thresholding, region-growing, unsupervised learning (e.g. k-means, fuzzy c-means clustering), and supervised learning (e.g. support vector machines). Some require human interaction. The techniques may be applied on one or more MRI sequence scans. Unfortunately, these automated attempts still result in a high level of error, and more computationally complex algorithms do not guarantee an increase in accuracy. This paper presents a novel, fully automatic brain tumor segmentation and volume estimation method using simple techniques on T1-contrasted and T2 MRIs. This new approach implemented five main steps: preprocessing using anisotropic diffusion, segmentation of tumor regions using k-means clustering, region combination using logical and Morphological operations, error checking using temporal smoothing, and volumetric measurement. When compared with five state-of-the-art algorithms, the proposed algorithm outperformed those in past works. Advances were seen by its noise reduction, increase in accuracy and closeness to actual tumor volume.
AUTOMATIC SEGMENTATION IN BREAST CANCER USING WATERSHED ALGORITHMijbesjournal
Accurate and reproducible delineation of breast lesions can be challenging, as the lesions may have complicated topological structures and heterogeneous intensity distributions. Diagnosis using magnetic resonance imaging (MRI) with an appropriate automatic segmentation algorithm can be a better imaging technique for the early detection of malignant breast tumours. The main objective of this system is to develop a method for automatic segmentation and the early detection of breast cancer based on the application of the watershed transform to MRI images. The algorithm was separated into three major sections: pre-processing, watershed and post-processing. After computing different segments, the final image was cleared of all noise and superimposed on the original MRI image to generate the final modified image. The algorithm successfully resulted in the automatic segmentation of the MRI images, and this can be a beneficial tool for the early detection of breast cancer. This study showed that the automatic results correctly agree with manual detection
A UTOMATIC S EGMENTATION IN B REAST C ANCER U SING W ATERSHED A LGORITHMijbesjournal
Accurate and reproducible delineation of breast les
ions can be challenging, as the lesions may have
complicated topological structures and heterogeneou
s intensity distributions. Diagnosis using magnetic
resonance imaging (MRI) with an appropriate automat
ic segmentation algorithm can be a better imaging
technique for the early detection of malignant brea
st tumours. The main objective of this system is to
develop a method for automatic segmentation and the
early detection of breast cancer based on the
application of the watershed transform to MRI image
s. The algorithm was separated into three major
sections: pre-processing, watershed and post-proces
sing. After computing different segments, the final
image was cleared of all noise and superimposed on
the original MRI image to generate the final modifi
ed image. The algorithm successfully resulted in the a
utomatic segmentation of the MRI images, and this c
an be a beneficial tool for the early detection of bre
ast cancer. This study showed that the automatic re
sults correctly agree with manual detection.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This paper presents an automated segmentation of brain tumors in computed tomography images (CT) using combination of Wavelet Statistical Texture features (WST) obtained from 2-level Discrete Wavelet Transformed (DWT) low and high frequency sub bands and Wavelet Co-occurrence Texture features (WCT) obtained from two level Discrete Wavelet Transformed (DWT) high frequency sub bands. In the proposed method, the wavelet based optimal texture features that distinguish between the brain tissue, benign tumor and malignant tumor tissue is found. Comparative studies of texture analysis is performed for the proposed combined wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Classification and evaluation. The combined Wavelet Statistical Texture feature set (WST) and Wavelet Co-occurrence Texture feature (WCT) sets are derived from normal and tumor regions. Feature selection is performed by Genetic Algorithm (GA). These optimal features are used to segment the tumor. An Probabilistic Neural Network (PNN) classifier is employed to evaluate the performance of these features and by comparing the classification results of the PNN classifier with the Feed Forward Neural Network classifier(FFNN).The results of the Probabilistic Neural Network, FFNN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. The performance of the algorithm is evaluated on a series of brain tumor images. The results illustrate that the proposed method outperforms the existing methods.
Implementation of Medical Image Analysis using Image Processing TechniquesYogeshIJTSRD
Clinical imaging is playing a fundamental limit in assessment and patching of affliction and discovering tumors and finding of threatening cells in less than ideal stage. As a standard system for perceiving bone features, is minute pictures were used. These photos are secured by using small radiography, where it expected to reiterated, drawn out and work raised measure. This method cant recognize the destructive cells because of the presence of uproar in the photos. Hence there is a necessity for automated and strong strategies to finish the image planning examination. As a first stage, the most fundamental piece of picture planning is to denoising without barging in on the diagnostics information during the clearing of commotion. The past collaboration disposes of the uproar and present fog in the image. To get precise picture getting ready, we have executed fragile and hard breaking point with various coefficients and to check the edge Visu wither was used. It was found that the Wavelet deionsing gadget was a helpful resource for picture improvement. In the gathering, our proposed work was connected with pre planning methodology to wipe out the noise and to get smooth pictures. This collaboration will help with improving the idea of the image and besides take out the fake areas. To recognize the presence of bone illness and to choose its stage, K infers estimation was used and thusly to get smooth picture, edge division measure was performed. Miss. Kode Keerthi | Mr. Parasurama N "Implementation of Medical Image Analysis using Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39893.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39893/implementation-of-medical-image-analysis-using-image-processing-techniques/miss-kode-keerthi
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of 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
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
2. 100 Computer Science & Information Technology (CS & IT)
patients with excruciating back pain. MR imaging of spine is formally identified with IR
(Inversion Recovery), T1 and T2 weighted images. While water content appears bright in T2 (in
medical lingo, its hyper intense which is clearly seen in the spinal canal), the same appears dark
(hypo intense) in T1 images. MR can detect early signs of bone marrow degeneration with high
spatial resolution where fat and water protons are found in abundance.
Degenerative lumbar spine disease (DLSD) includes spondylotic (arthritic) and degenerative disc
disease of the lumbar spine with or without neuronal compression or spinal instability. Accurate
diagnosis remains a challenge without manual intervention in segmenting the vertebral features. It
can be seen from fig 1. the degenerated state of L5 vertebrae and the associated intensity changes
prevalent. These are primarily due to the end plate degeneration.
Figure 1. Degenerated L5 vertebra in MR sagittal plane
While degenerative changes are a biological phenomena occurring in spinal structure that are
imaged using radiological equipments, certain irrelevant processes are also captured. These
constitute the artifacts caused due to intensity inhomogenities shown in fig 2. The segmentation
process is highly affected by these complexities present in MR images.
Figure 2. Intensity inhomogenity captured in lumbar vertebrae
The current work deals with segmentation of spinal column from MR image using fuzzy means
clustering for identification and labelling of individual vertebral structures. The segmented output
can be refined further and used for classification of degenerative state as well as to diagnose
deformities.
3. Computer Science & Information Technology (CS & IT) 101
2. LITERATURE
The commonly used segmentation methods are global thresholding, multilevel thresholding and
supervised clustering techniques. In intensity thresholding, the level determined from the grey-
level histogram of the image. The distribution of intensities in medical images, especially in MRI
images is random, and hence global thresholding methods fail due to lack of determining optimal
threshold. In addition, intensity thresholding methods have disadvantage of spatial uncertainty as
the pixel location information is ignored[2]. An edge detection scheme can be used for identifying
contour boundaries of the region of interest(ROI). The guarantee of these lines being contiguous
is very sleek. Also, these methods usually require computationally expensive post-processing to
obtain hole free representation of the objects.
The region growing methods extend the thresholding by integrating it with connectivity by means
of an intensity similarity measure. These methods assume an initial seed position and using
connected neighbourhood, expand the intensity column over surrounding regions. However, they
are highly sensitive to initial seeds and noise. In classification-based segmentation method, the
fuzzy C-means (FCM) clustering algorithm [3], is more effective with considerable amount of
benefits. Unlike hard clustering methods, like k-means algorithm, which assign pixels
exclusively to one cluster, the FCM algorithm allows pixels to have dependence with multiple
clusters with varying degree of memberships and thus more reasonable in real applications. Using
intuitionistic fuzzy clustering(IFC), where apart from membership functions(MF), non
membership values are also defined, [4]have segmented MR images of brain. The heuristic based
segmentation also considers the hesitation degree for each pixel. A similar study on generic gray
scale images is put forth in [5] where the IFC combines several MF's and the uncertainty in
choosing the best MF.
The article deals with elementary fuzzy C-means clustering, attempting to segment vertebral
bodies(VB) with morphological post processing. Also the VB's are labelled accordingly which
can reduce the burden of radiologist while classifying the degenerations involved.
3. METHODS
The proposed method is schematically depicted in fig.3. The input image(s) have been collected
from Apollo Speciality Hospitals, Chennai after going through a formal ethical clearance process.
The T1 weighted images, served as the initial dataset for the proposed algorithm.
Figure 3. Schematic of the proposed segmentation method
3.1. Pre-Processing
The method first smooths the image using the edge preserving anisotropic diffusion filter
presented in. It serves the dual purpose of removing inhomogenities and as an enhancer as well.
4. 102 Computer Science & Information Technology (CS & IT)
3.2. Fuzzy C-Means Clustering
The fuzzy c-means algorithm [2]has been broadly used in various pattern and image processing
studies [6]–[8]. According to fuzzy c-means algorithm, the clustering of a dataset can be obtained
by minimizing an objective function for a known number of clusters. Fuzzy C-means is based on
minimization of the following objective function:
ܬ = ݑ
ெ
ୀଵ
ே
ୀଵ
ฮݔ − ݒฮ
ଶ
, 1 ≤ ݇ < ∞
where ;
k is any real number known as the weighting factor,
ݑ is degree of membership of ݔ in the cluster j
ݔ is the ith
of p-dimensional measured intensity data
ݒ is the p-dimensional center of the jth
cluster
‖∗‖ is any norm expressing the similarity between measured intensity data and center
N represents number of pixels while M represents the number of cluster centers
Fuzzy clustering is performed through an iterative optimisation of objective function shown
above with update of membership function uij and cluster centers vj by
ݑ =
1
∑ ቀቛ
ݔ − ݒ
ݔ − ݒ
ቛቁ
ଶ
(ିଵ)ெ
ୀଵ
ݒ =
∑ ݑ
ݔ
ே
ୀଵ
∑ ݑ
ே
ୀଵ
The algorithm is terminated when maxij{uij at t+1 - uij at t} ≤ ϵ which is between 0 and 1.
3.3. Post Processing
A series of morphological operations are executed for extracting the vertebral bodies (VB) from
the clustered output. Hole filling is the preliminary step followed by an erosion to remove islands.
An area metric is used to extract only Vertebrae from surrounding muscular region Shape
analysis [9] reveals that the aspect ratio of VB varies between 1.5 and 2. This helps in isolating
the ligaments and spinal muscles associated with the spine in the region of interest.
3.4. Labelling
The segmented vertebrae are labelled using the connected component entity. Each VB is
identified with a group number. Starting from L5(Lumbar), the vertebrae are labelled
successively till L1 and then, the thoracic region begin. If the sacrum remains due to improper
segmentation, it can be eliminated based on aspect ration or area criteria. A colored schematic is
also presented for visual calibration.
3.5. Validation
The proposed method was validated using Dice coefficient (DC) and Hausdorff distance (HD) .
The reference standard for comparison was the annotated images from the radiologist. DC
measures the set agreement as described in following equations, where the images constitute the
5. Computer Science & Information Technology (CS & IT) 103
two sets. The generalized HD provides a means of determining the similarity between two binary
images. The two parameters used for matching the resemblance between the given images are,
• Maximum distance of separation between points, yet that can still be considered close.
• The fraction that determines how much one point set is far apart from the other.
,ܣ(ܦ )ܤ =
ଶ|∩|
||ା||
(Dice Coefficient)
,ܣ(ܦ )ܤ = ݔܽܯถ
∈
{݊݅ܯถ
∈
{݀(ܽ, ܾ)ሽሽ (Hausdorff Distance)
where, a, b are points from the images A,B respectively.
4. RESULTS AND DISCUSSION
The method is tested on sagittal cross-section of T1-weighted MR images of spine.The goal is to
segment the vertebral bodies from the muscular background.
4.1 Fuzzy segmentation
The input MR sagittal slice of spine considered for the current study is shown in fig 4. After the
pre-processing stage, the enhanced input is clustered using the Fuzzy C-means technique and the
final output derived is shown in fig 5(d).
Figure 4. Sagittal plane MR T1 image
The intermediate steps involving the morphological operations are depicted in fig 4. It can be
seen that, the fuzzy clustering provides a closer disjoint VB's owing to which we can erode the
muscular region and thus arrive at delineating the same.
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(a) fuzzy c-means (b) Erosion (c) Filtering using (d) Aspect ratio
area criteria based elimination
Figure 5. Post processed output using morphological operations
4.2 Labeling of VB
Automatic labeling of vertebrae is usually performed to reduce the manual effort put in by the
radiologist. It can be seen from fig 6, the labeled vertebrae and its color scheme can help in better
diagnosis given that geometric attributes are also extracted.
Figure 6. Labeling of VB after segmentation
4.3 Case study
Around 4 cases were used for the entire study. The patients complained of mild lower back pain
and are in the age group between 45-60. The population included 2 female and 2 male. An image
overlay of the input and segmented output for various cases is presented in fig 7.
7. Computer Science & Information Technology (CS & IT) 105
Figure 7. Overlay of segmented image with input for various case studies
4.4 Comparative Analysis
A comparative tabulation amongst the global thresholding, a simple clustering and the Fuzzy
clustering is illustrated in Table 1.
Table 1. Comparison of segmentation methods
Cases SI Segmentation methods
Otsu thresholding
K- Means
Clustering
Fuzzy C Means
Clustering
Case I
DC 0.36 0.622 0.835
HD 10.23 7.338 3.97
Case II
DC 0.43 0.618 0.90
HD 16.9 6.142 4.03
Case III
DC 0.57 0.714 0.852
HD 15.8 5.48 3.62
Case IV
DC 0.437 0.773 0.83
HD 15.2 5.7 3.95
The ground truth image was manually segmented by the radiologist and is used as the gold
standard for validation. It can be observed that the Fuzzy method provides better DC value (closer
8. 106 Computer Science & Information Technology (CS & IT)
to 1) and HD value (closer to 0) than compared to the rest thus affirming the robustness in
segmentation. Images obtained using Otsu's thresholding and K-means is shown in fig 8.
(a) fuzzy c means (b) Erosion (c) Filtering using (d) Aspect ratio
area criteria based elimination
Figure 8. Comparative analysis using Otsu and K-means
4.5 Failure Case
The method was tested on several images and in some images the segmentation failed to provide
quality results. The transverse and spinous processes are a part of vertebral bodies. Thus, when
they start emerging, with disruption in intensity as well as structure, the fuzzy clustering method
fails to adapt to the complex topology. Apart from this, the presence of anterior and posterior
ligaments also significantly affects the results of the segmentation. fig 9. shows the results of
segmentation of one such case where the ROI has not been delineated clearly.
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Figure 9. Failure case of proposed segmentation
5. CONCLUSIONS
In this paper, a fuzzy C-means clustering algorithm followed by morphological operations and
labelling has been presented for segmentation of spine MR images. It is compared with the simple
K-means clustering and Otsu thresholding scheme. Upon validation, it is observed that the fuzzy
C-means gives improved segmentation results as compared to the counterparts.As a part of future
work, we would like to incorporate intuitionistic fuzzy clustering to check if it can enhance the
accuracy. Also extract features from the segmented VB for classifying various deformity.
ACKNOWLEDGEMENTS
The first author would like to thank the Department of Science and Technology [DST], India, for
supporting the research through INSPIRE fellowship
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