In our proposed method an automatic Osteoporosis classification system is developed. The input of the system is Lumbar spine digital radiograph, which is subjected to pre-processing which includes conversion of grayscale image to binary image and enhancement using Contrast Limited Adaptive Histogram Equalization technique(CLAHE). Further Fractal Texture features(SFTA) are extracted, then the image is classified as Osteoporosis, Osteopenia and Normal using a Probabilistic Neural Network(PNN). A total of 158 images have been used, out of which 86 images are used for training the network and 32 images for testing and 40 images for validation. The network is evaluated using a confusion matrix and evaluation parameters like Sensitivity, Specificity, precision and Accuracy are computed fractal feature extraction techniques.
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.
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
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.
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.
Detection and classification of brain tumor are very important because it provides anatomical information of normal and abnormal tissues which helps in early treatment planning and patient's case follow-up. There is a number of techniques for medical image classification. We used PNN (Probabilistic Neural Network Algorithm) for image classification technique based on Genetic Algorithm (GA) and K-Nearest Neighbor (K-NN) classifier for feature selection is proposed in this paper. The searching capabilities of genetic algorithms are explored for appropriate selection of features from input data and to obtain an optimal classification. The method is implemented to classify and label brain MRI images into seven tumor types. A number of texture features (Gray Level Co-occurrence Matrix (GLCM)) can be extracted from an image, so choosing the best features to avoid poor generalization and over specialization is of paramount importance then the classification of the image and compare results based on the PNN algorithm.
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.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
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
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
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.
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.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
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
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.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
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
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
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.
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.
Brain Image Fusion using DWT and Laplacian Pyramid Approach and Tumor Detecti...INFOGAIN PUBLICATION
Image fusion is the process of combining important information from two or more images into a single image. The resulting image will be more enhanced than any of the input pictures. The idea of combining multiple image modalities to furnish a single, more enhanced image is well established, special fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and Discreet Wavelet Transform (DWT) methods. This system uses an easy and effective algorithm for multi-focus image fusion which uses fusion rules to create fused image. Subsequently, the fused image is obtained by applying inverse discreet wavelet transform. After fused image is obtained, watershed segmentation algorithm is applied to detect the tumor part in fused image.
Brain tumour segmentation based on local independent projection based classif...eSAT Journals
Abstract
Brain tumour detection and segmentation is most important and challenging task in early tumour diagnosis. There are various
segmentation methods available but they are still challenging methods because of its complex characteristics such as ambiguous
boundaries and high diversity. To overcome this problem we are going to implement automatic brain tumour detection and
segmentation method by using local independent projection based classification. In this method we are going to consider tumour
segmentation as a classification problem. In this paper locality is important in calculations of projections. Also local anchor
embedding is used to solve linear projection weights. The softmax regression model is used to improve classification performance.
In this study we used MRI images as training and testing data. Finally the brain tumour is classified into tumour and edema
region. The area of tumour region is calculated in pixels.
Key Words: Brain tumour detection & segmentation, local independent projection based classification, local anchor
embedding and softmax regression.
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
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.
A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentati...inventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Area of Work: Digital Image Processing
Software Used: MATLAB
This was my Minor Project of (7th Semester), I made in final year based on Image Processing, I have taken Bio-Medical Image called CT Images used for performing different operations on the image.
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
Ensemble Classifications of Wavelets based GLCM Texture Feature from MR Human...rahulmonikasharma
This paper presents an automatic image analysis of multi-model views of MR brain using ensemble classifications of wavelets based texture feature. Primarily, an input MR image has pre-processed for an enhancement process. Then, the pre-processed image is decomposed into different frequency sub-band image using 2D stationary and discrete wavelet transform. The GLCM texture feature information is extracted from the above low-frequency sub band image of 2D discrete and stationary wavelet transform. The extracted texture features are given as an input to ensemble classifiers of Gentle Boost and Bagged Tree classifiers to recognize the appropriate image samples. Image abnormality has extracted from the recognized abnormal image samples of classifiers using multi-level Otsu thresholding. Finally, the performance of two ensemble classifiers performance has analyzed using sensitivity, specificity, accuracy, and MCC measures of two different wavelet based GLCM texture features. The resultant proposed feature extraction technique achieves the maximum level of accuracy is 90.70% with the fraction of 0.78 MCC value.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. Classification of Osteoporosis using Fractal Texture Features Page 12
International Journal for Modern Trends in Science and Technology
ISSN: 2455-3778 |Volume No: 02 | Issue No: 02 | February 2016
I. INTRODUCTION
Osteoporosis is a progressive bone disease that
is characterized by a decrease in bone mass and
density which can lead to an increased risk
of fracture. In osteoporosis, the Bone Mineral
Density (BMD) is reduced, bone micro architecture
deteriorates, and the amount and variety of
proteins in bone are altered. The form of
osteoporosis most common in women after
menopause is referred to as primary type 1 or
postmenopausal osteoporosis, which is attributable
to the decrease in oestrogen production after
menopause. Primary type 2 osteoporosis or senile
osteoporosis occurs after age 75 and is seen in
both females and males at a ratio of 2:1. Secondary
osteoporosis may arise at any age and affect men
and women equally, this form results from chronic
predisposing medical problems or disease, or
prolonged use of medications such as
glucocorticoid, when the disease is called steroid or
glucocorticoid-induced osteoporosis. So in order to
detect the presence of osteoporosis we have to use
image processing techniques on Digital
radiographers contribute to the test values used for
training the neural network. Thus Image
processing is used to monitor the traits of the X-
rays for possible infirmities that might occur and
provide the necessary data for further treatment.
II. LITERATURE SURVEY
According to AlceuFerraz Costa[1], a new and
efficient texture feature extraction method called
the Segmentation-based Fractal Texture Analysis,
or SFTA is preferred than Haralick and Gabor filter.
The extraction algorithm consists in decomposing
the input image into a set of binary images from
which the fractal dimensions of the resulting
regions are computed in order to describe
segmented texture patterns. The decomposition of
the input image is achieved by the Two- Threshold
Binary Decomposition (TTBD) algorithm, which we
also propose in this work. The SFTA is evaluated
for the tasks of content-based image retrieval
(CBIR) and image classification, comparing its
performance to that of other widely employed
feature extraction methods such as Haralick and
Gabor filter banks. SFTA achieved higher precision
and accuracy for CBIR and image classification.
Additionally, SFTA was at least 3.7 times faster
than Gabor and 1.6 times faster than Haralick with
respect to feature extraction time.
Histogram equalization, which stretches the
dynamic range of intensity, is the most common
method for enhancing the contrast of an image. An
adaptive method to avoid this drawback is block-
based processing of histogram equalization. In
block-based processing, image is divided into sub-
images or blocks, and histogram equalization is
performed to each sub-images or blocks. Contrast
Limited Adaptive Histogram Equalization (CLAHE),
proposed by K. Zuierveld, has two key parameters:
block size and clip limit. These parameters are
mainly used to control image quality. In this paper,
a new novel method was proposed by ByongSeok
Min [2] to determine two parameters of the CLAHE
using entropy of an image.
An Automatic Brain Tumor Classification using
PNN and Clustering developed by P.Sangeetha [3]
describes the Probabilistic Neural Network (PNN)
will be employed to classify the various stages of
Tumor cut levels such as Benign, Malignant or
Normal. Probabilistic Neural Network with Radial
Basis Function will be applied to implement tumor
cells segmentation and classification. Decision
should be made to classify the input image as
normal or abnormal cells. Prediction of malignant
cells or non-tumor cells can be executed using two
variants: i) Feature extraction and ii) classification
using Probabilistic Neural Network (PNN). The
ability of their proposed Brain Tumor Classification
method is demonstrated on the basis of obtained
results on Brain Tumor image database. In their
proposed method, only 5 classes of Brain tumors
are considered, with respect to an example of 20
test images for instance but this method can be
extended to more classes of Brain tumors.
Since medical X-Ray images are grayscale images
with almost the same texture characteristics,
conventional color or texture features cannot be
used for appropriate categorization in medical X-
Ray image archives. Therefore, a novel feature is
proposed by Seyyed Mohammad Mohammadi [4]
which is the combination of shape and texture
features. The feature extraction process is started
by edge and shape information extraction from
original medical X-Ray image. Finally, Gabor filter
is used to extract spectral texture features from
shape images. In order to study the effect of feature
3. Classification of Osteoporosis using Fractal Texture Features Page 13
International Journal for Modern Trends in Science and Technology
ISSN: 2455-3778 |Volume No: 02 | Issue No: 02 | February 2016
fusion on the classification performance, different
effective features like local binary pattern. It
provides low computation complexity and
straightforward implementation. Due to following
advantages we can strongly claim that these
features are the most powerful and reliable features
for medical image X-Ray classification.
III. PROPOSED METHOD
The proposed approach starts first from
preprocessing. It is then followed by Grayscale
Conversion and the image is enhanced using a
CLAHE filter. Then the features namely fractal
features are extracted from the enhanced image
and the fractal features are used to determine the
category in which they fall determined by the PNN
classifier for one sample. This is repeated for the
stock of samples in which 2/3 of the samples are
taken as Database samples.
Methodologies
A. Pre-processing
B. Grayscale conversion
C. Image Enhancement.
D. Feature Extraction
E. PNN Classifier.
Figure 1.1 Block Diagram of proposed work
IV. RESEARCH METHODOLOGY
A. Preprocessing
Steps which are done prior to processing of an
image are called preprocessing. It includes image
enhancement and resizing. These are done in order
to make the image more suitable than an original
image for specific applications.
B. Grayscale Conversion
If the image selected is in three dimensions, it is
converted to a grayscale image using ‘rgb2gray’
conversion command for feature extraction. Then
the intensity variation of gray level image is shown
in the graph. Its value varies from 0 to 255.Resizing
of image is done for accurate processing of image.
Image can be resized to any size of our interest.
C, Image Enhancement
Image enhancement is the process of adjusting
digital images so that the results are more suitable
for display or further image analysis. After resizing
the image, we go for image enhancement. We use
CLAHE (Contrast Limited Adaptive Histogram
Equalization) technique. This method separates the
image into a number of tiles, and then adjusts the
contrast such that the tile histogram has the
desired shape. The tiles are then stitched together
using bilinear interpolation. The transformation
function modifies the pixels based on the gray level
content of an image. These techniques are used to
enhance details over small areas in an image.
Figure 1.2 Image after Enhancement
D. Feature Extraction
Feature is a parameter of interest to describe an
image. Transforming the input data into the set of
features is called feature extraction. If the features
extracted are carefully chosen it is expected that
the features set will extract the relevant
information from the input data in order to perform
the desired task. The Segmentation-based Fractal
Texture Analysis or SFTA method is a feature
extraction algorithm that decomposes a given
image into a set of binary images through the
application of what the authors call the Two
Threshold Binary Decomposition (TTBD). For each
resulting binary image, fractal dimensions of its
Training
Classification
(PNN Classifier)
Osteopenia Osteoporosis
Pre-
Processing(Image
Enhancement)
Feature Extraction(
SFTA Features)
Input X-Ray image Input X-Ray image
Pre-
Processing(Image
Enhancement )
Feature Extraction(SFTA
Features)
Features Database
Test Image
Normal
4. Classification of Osteoporosis using Fractal Texture Features Page 14
International Journal for Modern Trends in Science and Technology
ISSN: 2455-3778 |Volume No: 02 | Issue No: 02 | February 2016
region boundaries are calculated that describe the
texture patterns.TTBD takes an input greyscale
image and returns a set of binary images by first
computing a set of T threshold values from the gray
level distribution information in an input image.
This is accomplished by recursively applying to
each image region the multilevel Otsu algorithm,
an algorithm that quickly finds the threshold that
minimizes the input image intra-class variance
until the desired number of thresholds is obtained.
The input image is decomposed into a set of binary
images by selecting pairs of thresholds from T and
applying two-threshold segmentation.Fractal
measurements are used to describe the boundary
complexity of objects, with each region boundaries
of a binary image represented as a border image.
The fractal dimension is computed from each
boarder image using a box counting algorithm.
A. Haussdorf fractal dimension
Haussdorf dimension serves as a measure of the
local size of a set of numbers, taking into account
the distance between each of its members. The
Haussdorf dimension of an n-dimensional inner
product space equals n. This underlies the earlier
statement that the Haussdorf dimension of a point
is zero, of a line is one, etc., and that irregular sets
can have non integer Haussdorf dimensions.
Haussdorf fractal dimension of an object
represented by the binary image. Non-zero pixel
belongs to an object and zero pixel constitute the
background.
𝑑𝑖𝑚 𝐻 𝑋 = inf{𝑑 ≥ 0: 𝐶 𝐻
𝑑
𝑋 − 0} (1)
Where 〖 dim〗 _H(X) is the infimum of the set of
d∈ [0, ∞) such that the d-dimensional Haussdorf
measure of X is zero.
B. OTSU’S Segmentation
Otsu's method is used to automatically perform
clustering-based image thresholding i.e., the
reduction of a graylevel image to a binary image.
The algorithm assumes that the image contains
two classes of pixels following bi-modal histogram,
it then calculates the optimum threshold
separating the two classes so that their combined
spread is minimal.In Otsu's method, we
exhaustively search for the threshold that
minimizes the intra-class variance (the variance
within the class), defined as a weighted sum of
variances of the two classes.
𝜎 𝑤
2
𝑡 = 𝜔1 𝑡 𝜎1
2
𝑡 + 𝜔2 𝑡 𝜎2
2
𝑡 (2)
Weights ω_i are the probabilities of the two
classes separated by a threshold t and
σ_i^2variances of these classes. Otsu thresholding
returns a set of thresholds for the input image
employing the multilevel Otsu algorithm. The
multilevel Otsu algorithm consist in finding the
threshold that minimizes the input image intra-
class variants. Then, recursively, the Otsu
algorithm is applied to each image region until total
thresholds are found.
Figure 1.3 Image after OTSU Segmentation
C. Edge detection
Edge detection is an image processing technique
for finding the boundaries of objects within images.
It works by detecting discontinuities in brightness.
Edge detection is used for image segmentation and
data extraction in areas such as image processing,
computer vision, and machine vision.Edge
detection returns a binary image with the regions
boundaries of the input image. The input image
must be a binary image. The returned image takes
the value 1 if the corresponding pixel in the image
has the value 1 and at least one neighbouring pixel
with value 0. Otherwise takes value 0.
E. Artificial Neural Network
Artificial neural networks (ANNs) are a family of
statistical learning algorithms inspired by biological
neural networks. They are used to estimate or
approximate functions that can depend on a large
number of inputs and are generally unknown.
Artificial neural networks are generally presented
as systems of interconnected "neurons" which can
compute values from inputs, and are capable of
machine learning, as well as pattern recognition
thanks to their adaptive nature. After being
weighted and transformed by a function
(determined by the network's designer), the
activations of these neurons are then passed on to
other neurons. This process is repeated until
finally, an output neuron is activated.
A.PNN classifier
PNN is a useful neural network architecture with
5. Classification of Osteoporosis using Fractal Texture Features Page 15
International Journal for Modern Trends in Science and Technology
ISSN: 2455-3778 |Volume No: 02 | Issue No: 02 | February 2016
h
1
h
2
h
3
h
4
x1
x2
x3 c2
c1
z
slightly different in fundamentals from the back
propagation. The architecture is feed forward in
nature which is similar to back propagation, but
differs in the way that learning occurs. PNN is
supervised learning algorithm but includes no
weights in its hidden layer.
Input nodes Hidden nodes Class nodes
Decision node
Figure 1.4 Block Diagram of PNN Classifier.
Basically, PNN consists of an input layer, which
represents the input pattern or feature vector. The
input layer is fully interconnected with the hidden
layer, which consists of the example vectors (the
training set for the PNN). The actual example vector
serves as the weights as applied to the input layer.
Finally, an output layer represents each of the
possible classes for which the input data can be
classified. The output class node with the largest
activation represents the winning class. In PNN
algorithm, calculating the class-node activations is
a simple process. For each class node, the example
vector activations are summed, which are the sum
of the products of the example vector and the input
vector. The hidden node activation, shown in the
following equation, is simply the product of the two
vectors (E is the example vector, and F is the input
feature vector).
ℎ𝑖 = 𝐸𝑖 𝐹 (3)
The class output activations are then defined as:
𝑐𝑗 =
𝑒
(ℎ 𝑖−1)
𝑦2𝑁
𝑖=1
𝑁
(4)
Where N is the total number of example vectors
for this class, ℎ𝑖 is the hidden-node activation, and
γ is a smoothing factor. The smoothing factor is
chosen through experimentation. If the smoothing
factor is too large, details can be lost, but if the
smoothing factor is too small, the classifier may not
generalize well . It's also very easy to add new
examples to the network by simply add the new
hidden node, and its output is used by the
particular class node. This can be done
dynamically as new classified examples are found.
The PNN also generalizes very well, even in the
context of noisy data.
V. RESULTS AND DISCUSSION
Here, totally 158 images have been used out of
which 86 are taken for training and remaining have
been used for testing and validation. Calculated
feature values of various input X-Ray images are
tabulated. Results show that images are normal,
are Osteopenia and osteoporosis. Comparison of
various features for normal, Osteopenia and
osteoporosis images can be seen.
Table 1.1 Table Showing Result Set
Class Number Of Images
Normal 55
Osteopenia 46
Osteoporosis 57
Figure 1.5 GUI Result shows Normal
Figure 1.6 GUI Result shows Osteopenia
6. Classification of Osteoporosis using Fractal Texture Features Page 16
International Journal for Modern Trends in Science and Technology
ISSN: 2455-3778 |Volume No: 02 | Issue No: 02 | February 2016
Figure 1.7 GUI Result shows Osteoporosis
A. CONFUSION MATRIX
Confusion matrix is a specific table layout that
allows visualization of the performance of an
algorithm, typically a supervised learning one.
Each column of the matrix represents the
instances in a predicted class, while each row
represents the instances in an actual class.
Evaluation parameters such as Sensitivity,
Specificity, Precision and Accuracy are calculated
for the confusion matrix.
Table 1.2 Three Class Confusion Matrix of SFTA
45 2 0
4 65 4
0 2 34
Table 1.3 Table Showing Validation result of
SFTA
Parameter Formula SFTA
Sensitivity TP/ (TP+FN) 88.2%
Specificity TN/ (FP+TN) 94.2%
Precision TP/ (TP+FP) 97%
Accuracy (TP+TN)/ (TP+TN+FP+FN) 94.3%
VI. CONCLUSION AND FUTURE WORK
In this work, the suitability of texture features in
classification of Lumbar spine digital radiographs is
analyzed. The Experimental results during testing
and theoretical analysis prove in perspective of
time, accuracy of system being a main concern the
fractal features are more precise and more
accurate. In medical imaging or in diagnosis, the
important factor is accuracy rather than speed,
hence present fractal features as a more suitable
technique for feature extraction. The results on
classification have a combined accuracy of 93%.
This value is obtained by validating our practical
results with DEXA results. Hence our system can
assist in diagnosis of osteoporosis. In future the
work can be implemented for distal and thoracic
digital radiograph, the system can also be
embedded in a Digital radiography machine to
diagnose osteoporosis on the fly.
REFERENCES
[1] AlceuFerraz Costa, Gabriel Humpire-Mamani
andAgmaJuci Machado Traina, (2012) ‘An Efficient
Algorithm for Fractal Analysis of Textures’,
Conference on Graphics, Patterns and Images
(SIBGRAPI), pp. 39-46.
[2] ByongSeok Min, Dong Kyun Lim, Seung Jong Kim
and Joo Heung Lee, (2013)‘A Novel Method of
Determining Parameters of CLAHE Based on Image
Entropy’, International Journal of Software
Engineering and Its Applications, Vol.7, No.5,
pp.113-120.
[3] Sangeetha. P, (2014) ‘Brain Tumor Classification
using PNN and Clustering’, International Journal of
Innovative Research in Science, Engineering and
Technology, Vol. 3, No.3, Mar 2014.
[4] Seyyed Mohammad Mohammadi, Mohammad
SadeghHelfroush and Kamran kazemi, (2012) ‘Novel
shape-texture feature extraction for medical X-Ray
image classification’, International Journal of
Innovation Computing, Information and Control
(ICIC), Vol.8, No.1, ISSN 1349-4198.