Skip to main content

G R Suresh

St.Peters University, Department of Biomedical Engineering, Faculty Member

Followers

6

Following

2

Co-author

1

Public Views

Dr. Chiranjeevi Kumar Endukuru

University of Central Lancashire

Priyanka Satish

Mariangela Caserta Caserta

Università degli Studi di Napoli "Federico II"

Bernard Loisiel

Alexis Arce Alvarez

Interests

Uploads

Papers by G R Suresh

Development of four stress levels in group stroop colour word test using HRV analysis

Background: Stress has become an integrated part of our life. Stress was modelled as four stages ... more Background: Stress has become an integrated part of our life. Stress was modelled as four stages by Hans Selye’s in his General Adaptation Syndrome (GAS) model. Objective: The main objective of this work is to design an efficient laboratory Group Stroop Colour Word Test (GSCWT) to induce distinct four levels of stress that is, no stress, low, medium and high stress, which mimics a real time working memory (WM) load for the subject and thus cause stress for the subject. Subjects and Method: Healthy and normal 50 subject’s Heart Rate Variability (HRV) is considered for analysis. GSCWT is developed using software Matlab 2013 and is validated by recording ECG both at rest and while performing the different levels of stroop task. The time domain, frequency domain parameters of HRV is determined using kubios software and the influence of GSCWT between the different levels of stress is analysed using statistical t-test to compare the features between each state and one-way Anova test is us...

Embedding data in audio signals using HSA-EMD algorithm

2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), 2017

Piracy on the internet has been a long plaguing problem and it has made digital watermarking sche... more Piracy on the internet has been a long plaguing problem and it has made digital watermarking schemes to play an important role in handling such issues. In this paper, an adaptive watermarking algorithm HSA-EMD is used. Each audio signal is divided into frames and decomposed into intrinsic mode function (IMF). Each IMF has its own number of extrema and minima. The watermark is embedded into the extrema of last IMF. Hence the location of watermark cannot be detected. The proposed scheme is highly robust against various attacks. It gives improved performance than the existing schemes. Thus watermarked IMF is less sensitive to common signal processing attacks.

Decision Support System for Diabetes using Tongue Images

The tongue is a major part of the human body to taste, speak and swallow the food. Tongue portion... more The tongue is a major part of the human body to taste, speak and swallow the food. Tongue portion is directly connected with our internal organ. If any problem in that it reflects the effect through the tongue. Tongue center portion is connected with stomach, pancreas. Side portions are connected with liver. Tongue tip is connected with the heart, etc. In this study, an efficient Decision Support System is used to diagnosis the diabetes based on Characterization of tongue images by analyzing tongue colour distribution and texture for diabetic patient and healthy person. Comparative analysis of tongue image of DM and Healthy Person by using Kernel Ensemble Classification (KEC) method. Also Show the lab values of Diabetes.

Study of Mammogram Micro calcification toAid tumour detection using Artificial NeuralNetwork Based Classifier

In mammograms, a cluster of micro calcifications shows an early sign of breast cancer. An accurat... more In mammograms, a cluster of micro calcifications shows an early sign of breast cancer. An accurate and efficient classification scheme is needed for detection of tumour in mammograms. A Computer-Aided-Detection (CAD) system for automatic detection of tumour of micro calcification clusters on mammograms is discussed in this paper. The approach is very helpful for radiologist to diagnose the tumour and performs faster than typical screening programs. The images are collected from the Mammographic Image Analysis Society (MIAS) databases for implementation. The feature is extracted by Local Binary Pattern and classified using ANN. The proposed scheme provides high accuracy in classification of micro calcifications.

Speech Enhancement Using Harmonic-Model with Multichannel Wiener Filter

Enhancing the Compression Efficiency of Medical Image Sequences using SSP and BCH

The demand to preserve raw image data for further processing has been increased with the hasty gr... more The demand to preserve raw image data for further processing has been increased with the hasty growth of digital technology. In medical industry the images are generally in the form of sequences which are much correlated. These images are very important and hence lossless compression Technique is required to reduce the number of bits to store these image sequences and take less time to transmit over the network The proposed compression method combines Super-Spatial Structure Prediction with inter-frame coding that includes Motion Estimation and Motion Compensation to achieve higher compression ratio. Motion Estimation and Motion Compensation is made with the fast block-matching process Inverse Diamond Search method. To enhance the compression ratio we propose a new scheme Bose, Chaudhuri and Hocquenghem (BCH). Results are compared in terms of compression ratio and Bits per pixel to the prior arts. Experimental results of our proposed algorithm for medical image sequences achieve 30%...

Hybrid SVM classification technique to detect mental stress in human beings using ECG signals

2013 International Conference on Advanced Computing and Communication Systems

In recent years, stress has become ingrained part of our life, being stressed by our financial wo... more In recent years, stress has become ingrained part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. In this work, Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used. The features extracted from HRV are given as input, to the two stage classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. In the first stage of the classifier, Self Organizing Map is used to classify the stress into two classes as `stress level 1'(no stress & low stress) and `stress level 2' (medium stress & high stress). In the second stage Support Vector Machine is used with RBF kernel to subdivide the `stress level 1' into two classes `No Stress' and `Low Stress'. The stress level 2 is subdivided into twoclasses `Medium Stress' and `High Stress'. The performance of this hybrid structure is better and the efficiency of classification is 91%.

Analysis of Secure Medical Image Communication with Digital Signature and Reversible Watermarking

TELKOMNIKA Indonesian Journal of Electrical Engineering

Protection of Medical image contents becomes the important issue in computer network security. Di... more Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of P...

An efficient approach for the detection of link failures in WBAN system for health care applications

International Journal of Communication Systems

Coalition formation and Task Allocation of multiple autonomous robots

2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), 2015

Multi robot systems are of increased demand in industrial environments for easier task execution ... more Multi robot systems are of increased demand in industrial environments for easier task execution in short durations. The challenge in deploying a multi robot system for task execution lies in coordinating the robots to perform a task collaboratively within the stipulated time. This paper deals with the following challenge in the multi robot system, first is the search for the most optimal solution for coalition formation, which acquires ample information about the task and the robot agent used, followed by allocating tasks to the robots in the coalition which is commonly referred to as Multi-Robot Task Allocation (MRTA) problem. The optimal solution for robot coalition lies in determining the best number of robots required to complete the task within the deadline that is achieved using dynamic ANT coalition technique which works online. The novelty of the proposed methodology lies in using the most effective local search process of the memetic algorithm rather than using the whole of the memetic algorithm which includes the global search and local search process.

Region of interest extraction using combined segmentation in Multispectral Palm Image

2013 Fifth International Conference on Advanced Computing, Dec 1, 2013

Image Acquisition is the method of capturing the image. Entire image is not needed for Enhancemen... more Image Acquisition is the method of capturing the image. Entire image is not needed for Enhancement Restoration and Object Recoginition. Image segemention is to get the information which is needed for Authentication System. Processing the segemented image will be cost effective and time effective. Each person can be differentiated based on the physiological and behavioural characteristics. Example for physiological characteristics are palm, fingerprint, iris etc. Palmprint recognition system consists of capturing image, segmentation, feature extraction, matching and result. For any image the noise should be removed and only the region which is needed should be extracted. In Palm Acquisition, the whole palm is captured but it is not required for recognition or authentication. Only certain features such as palmline and texture are extracted from whole palm, which contains the necessary information. In this paper we proposed a methodology for extracting Region of Interest which consists of Otsu thresholding scheme[18], Morphological operations, Canny edge detection, marking reference point and palm image alignment. Experimental results show that the approach is efficient both in computational cost and segmentation quality of Multispectral palm Images. The performance of Region of Interest (ROI) segementation is tested with CASIA MultiSpectral Palmprint Image Database V1.0. ROI extraction for peg free palm image depends on the hand position, there is no constant time. In our methodology ROI is extracted in 2.45 seconds.

High density impulse noise removal using BDND filtering algorithm

2014 International Conference on Communication and Signal Processing, 2014

Switching median filters outperform standard median filters in the removal of impulse noise. This... more Switching median filters outperform standard median filters in the removal of impulse noise. This is because, it considers only the noisy pixels and performs filtering operation on that pixels without considering noise-free pixels. The Boundary Discriminative Noise Detection (BDND) filter is proven to operate effectively under different impulse noise models. It initially classifies pixels into three groups as (a) low intensity impulse noise (b) high intensity impulse noise (c) uncorrupted pixels. Then noise detection and filtering steps are performed. Pixel misclassification is the main drawback of BDND filtering algorithm. So we modify the filtering step of this algorithm and named it as modified boundary discriminative noise detection (MBDND). The two modifications incorporated are as follows: (1) Expansion of filtering window. (2) Incorporating spatial and intensity information. By introducing these modifications into the algorithm, it is found that there is increase in the performance and the quality of image has improved. Results are compared with other median filters like Center Weighted Median Filter (CWMF), Progressive Switching Median Filter (PSMF), Adaptive Threshold Median Filter (ATMF) and it is found that MBDND performs well even at high noise density (90%).

Combining trace transform and SVD for classification of micro-calcifications in digital mammograms

Breast cancer has been most frequent form of common cancer in women. It is also the leading cause... more Breast cancer has been most frequent form of common cancer in women. It is also the leading cause of mortality in women each year. Breast cancer is much less common in younger women and is most often diagnosed when women are over 60. Breast cancer is the second-most common and leading cause of cancer death among women. It has turn into a major health issue in the world over the past 50 years, and its occurrence has increased in recent years. One of the leading methods for diagnosing breast cancer is screening mammography. The appearance of micro-calcification in mammograms is an early sign of breast cancer. To overcome the issue automated micro-calcification detection techniques play a vital role in cancer diagnosis and treatment. This paper aims to develop an automatic system to classify the digital mammogram images into Benign or Malignant images. We have proposed Support vector machine (SVM) based classifier for to detect the microcalcification at each location in the mammogram images. The proposed method has been implemented in three stages (a) preprocessing (b) feature extraction (c) SVM classification. The proposed method has been evaluated using Mammogram Image Analysis Society (MIAS) database. Experimental results show that, when compared to several other methods SVM shows 94.94% micro calcification detection in mammograms.

Performance Analysis of Secure Medical Image Communication with Digital Signature and Reversible Watermarking

EURASIP Journal on Image and Video Processing

Protection of Medical image contents becomes the important issue in computer network security. Di... more Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of P...

Enhancing Efficiency of Spiht Using HCC for Medical Image Sequences

Medical image sequences are produced in enormous amount which requires a large storage capacity. ... more Medical image sequences are produced in enormous amount which requires a large storage capacity. These image sequences are of great importance, hence it is necessary to develop a system that produces high degree of compression while preserving the medical information. Lossless compression Technique is required to reduce the number of bits to store these image sequences and take less time to transmit over the network. This paper proposes a method that combines SPIHT with HCC to enhance the compression ratio. Inter-frame coding is used to exploit the correlation among image sequences. Initially SPIHT is applied with fast block matching process, Diamond Search. To enhance the efficiency of compression ratio a new scheme Head Code Compression is introduced. Results are compared in terms of Compression Ratio and Peak Signal-to-Noise Ratio with other state of art coding. Experimental results of our proposed methodology achieves 25% more reduction than the other state-of-the-art algorithms.

Distance Based Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

Applied Mechanics and Materials, 2014

In wireless sensor networks Energy-efficient routing is an important issue due to the limited bat... more In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection

Human Action Recognition Using Local Space Time Features and Adaboost SVM

Human action recognition has a wide range of promising applications like video surveillance, inte... more Human action recognition has a wide range of promising applications like video surveillance, intelligent interface, and video retrieval. The objective of this project is to recognize and annotate different human activities present in digital videos taken from both constrained and unconstrained environment. This work extended the use of the techniques existing in object recognition in 2D images to video sequences by determining the Spatio-temporal Interest points using the extension of Harris operator in the time dimension. Features descriptors are computed on the cuboids around these interest points and further they are clustered and bag of features is built .SVM is used to classify the different classes of action present in the video. The recognition rate is further improved by using Adaboost SVM wherein number of weak classifier is weighted to form a strong classifier. The result shows that the proposed method using adaboost SVM classifier, the mean accuracy rate of recognition of KTH dataset is 89.13%.

Efficient Image Sequence Compression for Capsule Endoscopy Using Iwt

In multimedia environment many videos have been introduced which occupies more memory space, espe... more In multimedia environment many videos have been introduced which occupies more memory space, especially in the medical field. In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is approached. The compressor consists of a low-cost YCbCr color space converter and variable-length predictive with a combination and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The results shows increase in PSNR value of 54db the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression.

A New Hybridized Speech Enhancement Technique for Stationary and Non-Stationary Noisy Environments

by Kanaga Suba Raja S and G R Suresh

Journal of Computational and Theoretical Nanoscience

Automatic Diagnosis of Breast Cancer using Thermographic Color Analysis and SVM Classifier

Advances in Intelligent Systems and Computing

Development of four stress levels in group stroop colour word test using HRV analysis

Background: Stress has become an integrated part of our life. Stress was modelled as four stages ... more Background: Stress has become an integrated part of our life. Stress was modelled as four stages by Hans Selye’s in his General Adaptation Syndrome (GAS) model. Objective: The main objective of this work is to design an efficient laboratory Group Stroop Colour Word Test (GSCWT) to induce distinct four levels of stress that is, no stress, low, medium and high stress, which mimics a real time working memory (WM) load for the subject and thus cause stress for the subject. Subjects and Method: Healthy and normal 50 subject’s Heart Rate Variability (HRV) is considered for analysis. GSCWT is developed using software Matlab 2013 and is validated by recording ECG both at rest and while performing the different levels of stroop task. The time domain, frequency domain parameters of HRV is determined using kubios software and the influence of GSCWT between the different levels of stress is analysed using statistical t-test to compare the features between each state and one-way Anova test is us...

Embedding data in audio signals using HSA-EMD algorithm

2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), 2017

Piracy on the internet has been a long plaguing problem and it has made digital watermarking sche... more Piracy on the internet has been a long plaguing problem and it has made digital watermarking schemes to play an important role in handling such issues. In this paper, an adaptive watermarking algorithm HSA-EMD is used. Each audio signal is divided into frames and decomposed into intrinsic mode function (IMF). Each IMF has its own number of extrema and minima. The watermark is embedded into the extrema of last IMF. Hence the location of watermark cannot be detected. The proposed scheme is highly robust against various attacks. It gives improved performance than the existing schemes. Thus watermarked IMF is less sensitive to common signal processing attacks.

Decision Support System for Diabetes using Tongue Images

The tongue is a major part of the human body to taste, speak and swallow the food. Tongue portion... more The tongue is a major part of the human body to taste, speak and swallow the food. Tongue portion is directly connected with our internal organ. If any problem in that it reflects the effect through the tongue. Tongue center portion is connected with stomach, pancreas. Side portions are connected with liver. Tongue tip is connected with the heart, etc. In this study, an efficient Decision Support System is used to diagnosis the diabetes based on Characterization of tongue images by analyzing tongue colour distribution and texture for diabetic patient and healthy person. Comparative analysis of tongue image of DM and Healthy Person by using Kernel Ensemble Classification (KEC) method. Also Show the lab values of Diabetes.

Study of Mammogram Micro calcification toAid tumour detection using Artificial NeuralNetwork Based Classifier

In mammograms, a cluster of micro calcifications shows an early sign of breast cancer. An accurat... more In mammograms, a cluster of micro calcifications shows an early sign of breast cancer. An accurate and efficient classification scheme is needed for detection of tumour in mammograms. A Computer-Aided-Detection (CAD) system for automatic detection of tumour of micro calcification clusters on mammograms is discussed in this paper. The approach is very helpful for radiologist to diagnose the tumour and performs faster than typical screening programs. The images are collected from the Mammographic Image Analysis Society (MIAS) databases for implementation. The feature is extracted by Local Binary Pattern and classified using ANN. The proposed scheme provides high accuracy in classification of micro calcifications.

Speech Enhancement Using Harmonic-Model with Multichannel Wiener Filter

Enhancing the Compression Efficiency of Medical Image Sequences using SSP and BCH

The demand to preserve raw image data for further processing has been increased with the hasty gr... more The demand to preserve raw image data for further processing has been increased with the hasty growth of digital technology. In medical industry the images are generally in the form of sequences which are much correlated. These images are very important and hence lossless compression Technique is required to reduce the number of bits to store these image sequences and take less time to transmit over the network The proposed compression method combines Super-Spatial Structure Prediction with inter-frame coding that includes Motion Estimation and Motion Compensation to achieve higher compression ratio. Motion Estimation and Motion Compensation is made with the fast block-matching process Inverse Diamond Search method. To enhance the compression ratio we propose a new scheme Bose, Chaudhuri and Hocquenghem (BCH). Results are compared in terms of compression ratio and Bits per pixel to the prior arts. Experimental results of our proposed algorithm for medical image sequences achieve 30%...

Hybrid SVM classification technique to detect mental stress in human beings using ECG signals

2013 International Conference on Advanced Computing and Communication Systems

In recent years, stress has become ingrained part of our life, being stressed by our financial wo... more In recent years, stress has become ingrained part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. In this work, Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used. The features extracted from HRV are given as input, to the two stage classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. In the first stage of the classifier, Self Organizing Map is used to classify the stress into two classes as `stress level 1'(no stress & low stress) and `stress level 2' (medium stress & high stress). In the second stage Support Vector Machine is used with RBF kernel to subdivide the `stress level 1' into two classes `No Stress' and `Low Stress'. The stress level 2 is subdivided into twoclasses `Medium Stress' and `High Stress'. The performance of this hybrid structure is better and the efficiency of classification is 91%.

Analysis of Secure Medical Image Communication with Digital Signature and Reversible Watermarking

TELKOMNIKA Indonesian Journal of Electrical Engineering

Protection of Medical image contents becomes the important issue in computer network security. Di... more Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of P...

An efficient approach for the detection of link failures in WBAN system for health care applications

International Journal of Communication Systems

Coalition formation and Task Allocation of multiple autonomous robots

2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN), 2015

Multi robot systems are of increased demand in industrial environments for easier task execution ... more Multi robot systems are of increased demand in industrial environments for easier task execution in short durations. The challenge in deploying a multi robot system for task execution lies in coordinating the robots to perform a task collaboratively within the stipulated time. This paper deals with the following challenge in the multi robot system, first is the search for the most optimal solution for coalition formation, which acquires ample information about the task and the robot agent used, followed by allocating tasks to the robots in the coalition which is commonly referred to as Multi-Robot Task Allocation (MRTA) problem. The optimal solution for robot coalition lies in determining the best number of robots required to complete the task within the deadline that is achieved using dynamic ANT coalition technique which works online. The novelty of the proposed methodology lies in using the most effective local search process of the memetic algorithm rather than using the whole of the memetic algorithm which includes the global search and local search process.

Region of interest extraction using combined segmentation in Multispectral Palm Image

2013 Fifth International Conference on Advanced Computing, Dec 1, 2013

Image Acquisition is the method of capturing the image. Entire image is not needed for Enhancemen... more Image Acquisition is the method of capturing the image. Entire image is not needed for Enhancement Restoration and Object Recoginition. Image segemention is to get the information which is needed for Authentication System. Processing the segemented image will be cost effective and time effective. Each person can be differentiated based on the physiological and behavioural characteristics. Example for physiological characteristics are palm, fingerprint, iris etc. Palmprint recognition system consists of capturing image, segmentation, feature extraction, matching and result. For any image the noise should be removed and only the region which is needed should be extracted. In Palm Acquisition, the whole palm is captured but it is not required for recognition or authentication. Only certain features such as palmline and texture are extracted from whole palm, which contains the necessary information. In this paper we proposed a methodology for extracting Region of Interest which consists of Otsu thresholding scheme[18], Morphological operations, Canny edge detection, marking reference point and palm image alignment. Experimental results show that the approach is efficient both in computational cost and segmentation quality of Multispectral palm Images. The performance of Region of Interest (ROI) segementation is tested with CASIA MultiSpectral Palmprint Image Database V1.0. ROI extraction for peg free palm image depends on the hand position, there is no constant time. In our methodology ROI is extracted in 2.45 seconds.

High density impulse noise removal using BDND filtering algorithm

2014 International Conference on Communication and Signal Processing, 2014

Switching median filters outperform standard median filters in the removal of impulse noise. This... more Switching median filters outperform standard median filters in the removal of impulse noise. This is because, it considers only the noisy pixels and performs filtering operation on that pixels without considering noise-free pixels. The Boundary Discriminative Noise Detection (BDND) filter is proven to operate effectively under different impulse noise models. It initially classifies pixels into three groups as (a) low intensity impulse noise (b) high intensity impulse noise (c) uncorrupted pixels. Then noise detection and filtering steps are performed. Pixel misclassification is the main drawback of BDND filtering algorithm. So we modify the filtering step of this algorithm and named it as modified boundary discriminative noise detection (MBDND). The two modifications incorporated are as follows: (1) Expansion of filtering window. (2) Incorporating spatial and intensity information. By introducing these modifications into the algorithm, it is found that there is increase in the performance and the quality of image has improved. Results are compared with other median filters like Center Weighted Median Filter (CWMF), Progressive Switching Median Filter (PSMF), Adaptive Threshold Median Filter (ATMF) and it is found that MBDND performs well even at high noise density (90%).

Combining trace transform and SVD for classification of micro-calcifications in digital mammograms

Breast cancer has been most frequent form of common cancer in women. It is also the leading cause... more Breast cancer has been most frequent form of common cancer in women. It is also the leading cause of mortality in women each year. Breast cancer is much less common in younger women and is most often diagnosed when women are over 60. Breast cancer is the second-most common and leading cause of cancer death among women. It has turn into a major health issue in the world over the past 50 years, and its occurrence has increased in recent years. One of the leading methods for diagnosing breast cancer is screening mammography. The appearance of micro-calcification in mammograms is an early sign of breast cancer. To overcome the issue automated micro-calcification detection techniques play a vital role in cancer diagnosis and treatment. This paper aims to develop an automatic system to classify the digital mammogram images into Benign or Malignant images. We have proposed Support vector machine (SVM) based classifier for to detect the microcalcification at each location in the mammogram images. The proposed method has been implemented in three stages (a) preprocessing (b) feature extraction (c) SVM classification. The proposed method has been evaluated using Mammogram Image Analysis Society (MIAS) database. Experimental results show that, when compared to several other methods SVM shows 94.94% micro calcification detection in mammograms.

Performance Analysis of Secure Medical Image Communication with Digital Signature and Reversible Watermarking

EURASIP Journal on Image and Video Processing

Protection of Medical image contents becomes the important issue in computer network security. Di... more Protection of Medical image contents becomes the important issue in computer network security. Digital Watermarking has becomes a promising technique for medical content authentication, it allows to embed relevant information with the image, which provides confidentiality, integrity and authentication by embedding Digital Signature (DS) with the Medical image. In this paper we focus on need for reversible watermarking, Medical Image Compression and security related problems in medical images, it comparing the performances of various lossless watermarking techniques for various medical image modalities like MRI (Magnetic Resonance Imaging), US (Ultrasonic), CT (Computed Tomography), Endoscopic and Angiographic images. Region of Interest (ROI) supporting lossless watermarking systems only considered for discussions. Performance of all lossless watermarking with Digital Signature is analyzed by means of four parameters Capacity Rate, PSNR (Peak Signal to Noise ratio), NPCR (Number of P...

Enhancing Efficiency of Spiht Using HCC for Medical Image Sequences

Medical image sequences are produced in enormous amount which requires a large storage capacity. ... more Medical image sequences are produced in enormous amount which requires a large storage capacity. These image sequences are of great importance, hence it is necessary to develop a system that produces high degree of compression while preserving the medical information. Lossless compression Technique is required to reduce the number of bits to store these image sequences and take less time to transmit over the network. This paper proposes a method that combines SPIHT with HCC to enhance the compression ratio. Inter-frame coding is used to exploit the correlation among image sequences. Initially SPIHT is applied with fast block matching process, Diamond Search. To enhance the efficiency of compression ratio a new scheme Head Code Compression is introduced. Results are compared in terms of Compression Ratio and Peak Signal-to-Noise Ratio with other state of art coding. Experimental results of our proposed methodology achieves 25% more reduction than the other state-of-the-art algorithms.

Distance Based Clustering Algorithm for Energy Efficiency in Wireless Sensor Networks

Applied Mechanics and Materials, 2014

In wireless sensor networks Energy-efficient routing is an important issue due to the limited bat... more In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection

Human Action Recognition Using Local Space Time Features and Adaboost SVM

Human action recognition has a wide range of promising applications like video surveillance, inte... more Human action recognition has a wide range of promising applications like video surveillance, intelligent interface, and video retrieval. The objective of this project is to recognize and annotate different human activities present in digital videos taken from both constrained and unconstrained environment. This work extended the use of the techniques existing in object recognition in 2D images to video sequences by determining the Spatio-temporal Interest points using the extension of Harris operator in the time dimension. Features descriptors are computed on the cuboids around these interest points and further they are clustered and bag of features is built .SVM is used to classify the different classes of action present in the video. The recognition rate is further improved by using Adaboost SVM wherein number of weak classifier is weighted to form a strong classifier. The result shows that the proposed method using adaboost SVM classifier, the mean accuracy rate of recognition of KTH dataset is 89.13%.

Efficient Image Sequence Compression for Capsule Endoscopy Using Iwt

In multimedia environment many videos have been introduced which occupies more memory space, espe... more In multimedia environment many videos have been introduced which occupies more memory space, especially in the medical field. In this paper, a new low complexity and lossless image compression system for capsule endoscopy (CE) is approached. The compressor consists of a low-cost YCbCr color space converter and variable-length predictive with a combination and unary encoding. All these components have been heavily optimized for low-power and low-cost and lossless in nature. As a result, the entire compression system does not incur any loss of image information. Unlike transform based algorithms, the compressor can be interfaced with commercial image sensors which send pixel data in raster-scan fashion that eliminates the need of having large buffer memory. The results shows increase in PSNR value of 54db the proposed algorithm offers a solution to wireless capsule endoscopy with lossless and yet acceptable level of compression.

A New Hybridized Speech Enhancement Technique for Stationary and Non-Stationary Noisy Environments

by Kanaga Suba Raja S and G R Suresh

Journal of Computational and Theoretical Nanoscience

Automatic Diagnosis of Breast Cancer using Thermographic Color Analysis and SVM Classifier

Advances in Intelligent Systems and Computing