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Please follow the links to view the publication.Epidural needle length measurement by video processing
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0446
This paper presents a novel image processing algorithm to measure the length and depth of an epidural needle during insertion. A wireless camera is used which transmits video during insertion to a host computer. The computer contains the image processing algorithm to detect the visible needle in the image and measures the length. The measurement is done by HSV background removal, colour comparison and using RGB histograms to locate 10mm markings on the Tuohy needle shaft. The visible length is then subtracted from the known length of the needle to calculate the depth of the needle tip. The camera can be placed in the operating theatre up to one meter away from the needle insertion site. The purpose of measuring needle depth in real time is to precisely place the needle in the epidural space. (6 pages)Mass detection in digital mammograms using gabor filter bank
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0465
Digital Mammograms are currently the most effective imaging modality for early detection of breast cancer but the number of false negatives and false positives is high. Mass is one type of breast lesion and the detection of masses is highly challenged problem. Almost all methods that have been proposed so far suffer from high number of false positives and false negatives. In this paper, a method for detecting true masses is presented, especially, for the reduction of false positives and false negatives. The key idea of the proposal is the use of Gabor filter banks for extracting the most representative and discriminative local spatial textural properties of masses that are present in mammograms at different orientations and scales. The system is evaluated on 512 (256 normal+256 true mass) regions of interests (ROIs) extracted from digital mammograms of DDSM database. We performed experiments with Gabor filter banks having different numbers of orientations and scales to find the best parameter setting. Using a powerful feature selection technique and support vector machines (SVM) with 10-fold cross validation, we report to achieve Az = 0.995±0.011, the area under ROC. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods, which are based on texture description, and the difference is statistically significant. (6 pages)Semi automated segmentation of chromosomes in metaphase cells
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0463
Image segmentation plays a crucial role in many medical imaging applications by automating or facilitating the delineation of anatomical structures and other regions of interest. Since the birth of the automated karyotyping systems by the aid of computers, building a fully automated chromosome analysis system has been an ultimate goal. Along with many other challenges, accurate segmentation of the chromosomes has been a major challenge especially due to the non rigid nature of the chromosomes. The earlier reported approaches for the segmentation have limited success as they are sensitive to scale variation, experimented only on gray images, unable to segment the clusters and the highly bent chromosomes. This work, describes an effective approach of segmentation of chromosomes in Metaphase images using Random Walker Algorithm [RWA] which is yet unexplored and not reported in the literature. The efforts are also done to compare the results with traditional methods so as to prove the efficiency of the implemented RWA algorithm. The algorithm is tested on publically available database and has shown encouraging and acceptable results. (6 pages)A new mutual information based similarity measure for medical image registration
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0424
Medical image registration (IR) is the systematic process of aligning spate images, often involving different modalities with common reference framework, so complementary information can be combined and compared. This paper presents a new similarity measure which uses Expectation Maximization for Principal Component Analysis allied with mutual information (EMPCA-MI) for medical IR. The new measure has been analysed on multimodal, three band magnetic resonance images (MRI) T1, T2 and PD weighted, in the presence of both intensity non-uniformities (INU) and noise. Both quantitative and qualitative experimental results clearly demonstrate both improved robustness and lower computational complexity of the new EMPCA-MI paradigm compared with existing MI-based similarity measures, for various MRI test datasets. (6 pages)Using sensor enabled augmented reality for healthcare
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0587
The problem of deteriorating health condition of students is addressed using an image-sensing system under the principles of augmented reality using devices and computer simulation programs. In this system the augmented reality system allows the young users to continue with their learning as they develop their physical fitness at the same time. Some of our extensive measurement results, including those for Cardiorespiratory Endurance have been reported as an effective indicator of our new system. (5 pages)Validating the neutrosophic approach of MRI denoising based on structural similarity
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0419
This paper focuses on validating the proposed Neutrosophic Set (NS) approach of Magnetic Resonance Image (MRI) denoising based on structural similarity such as Structural Similarity Index (SSIM) and Quality Index based on Local Variance (QILV). The Neutrosophic Set approach of median filter is used to reduce the Rician noise in MR image. The experiments have conducted on real MR image with Rician noise added. The visual and the diagnostic quality of the denoised image is well preserved. The performance of this filter is compared with median filter and non local mean filter (NLM). (6 pages)Segmentation of sputum cell image for early lung cancer detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0433
Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. To that end, computer-aided diagnosis system using images of sputum stained smears has been an attractive approach due to its practicality, low cost, and invasiveness. In this context, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with an other competitive technique via a series of experimentation conducted with a data set of 88 images. (6 pages)MRI mammogram image classification using ID3 algorithm
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0464
Breast cancer is one of the most common forms of cancer in women. In order to reduce the death rate , early detection of cancerous regions in mammogram images is needed. The existing system is not so accurate and it is time consuming. The Proposed system is mainly used for automatic segmentation of the mammogram images and classify them as benign,malignant or normal based on the decision tree ID3 algorithm. A hybrid method of data mining technique is used to predict the texture features which play a vital role in classification. The sensitivity, the specificity, positive prediction value and negative prediction value of the proposed algorithm accounts to 93.45% , 99.95%,94% and 98.5% which rates very high when compared to the existing algorithms. The size and the stages of the tumor is detected using the ellipsoid volume formula which is calculated over the segmented region. (5 pages)A thresholding approach for detection of sputum cell for lung cancer early diagnosis
http://dl-live.theiet.org/content/conferences/10.1049/cp.2012.0442
In this paper, we address the problem of detection and extraction of sputum cells that help in lung cancer early diagnosis. Our approach is based on thresholding classifier looking at the distribution of sputum pixels and non sputum pixels in RGB space for extracting the sputum cell from the raw sputum image. In this method the problem is viewed as a segmentation problem focusing on extraction of such sputum cells from the images whereby we want to partition the image into sputum cell regions including the nuclei, cytoplasm and the background that includes all the rest. These cells will be analyzed to check whether they are cancerous or not. In this study, we used a database of 100 sputum color images to test the thresholding classifier by comparing it with the ground truth data of extracted sputum cells and it has shown a better extraction result than previous work. Moreover, we computed a histogram for different color spaces (RGB, YCbCr, HSV, L*a*b* and XYZ) to find the best color space with low false detection rate. We used some performance criteria such as precision, specificity and accuracy to evaluate the improved thresholding classifier. (6 pages)Robust PSD features for ion-channel signals
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0154
Ion-channel sensors which mimic naturally occurring pore-forming proteins can be used to detect small metal ions and organic molecules. A chamber with a lipid bilayer hosting ion-channels produced by protein insertion constitutes such a sensor. Each analyte produces a characteristic signal pattern during its migration from one section of the chamber to another through the ion-channels. A four chamber ion-channel sensor array is built for accurate analyte detection. The power distribution information in the transform domain has been successfully used as discriminatory features for each chamber signal. However, these features are not robust to noise and hence result in a reduced classification performance. In this paper, we pose the stabilization of PSD features extracted from noisy segments as a matrix completion problem. Matrix completion with a low rank assumption provides the stabilized features. We demonstrate using a synthetic experiment that the proposed setup achieves improved classification performance in comparison to using the features directly. Furthermore, performing analyte detection in real ion-channel data, using the proposed robust features, provides reduction in false alarm rates. (5 pages)Towards assisted living via probabilistic vital-sign monitoring in the home
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0022
This paper describes the development of a reliable multi-sensor data fusion system for monitoring patient vital-signs in the home. Initial investigatory work has taken place using ambulatory hospital patients, in the Oxford Cancer Hospital. Our monitoring approach is based on a probabilistic model of normality learned from a data-set of vital signs acquired from a representative group of high-risk patients. Alerts are provided to carers whenever patient vital signs are deemed "abnormal" with respect to the model of normality. We show examples of how this approach correctly detects physiological deterioration in the target patient group, and describe future work in further validation of the technology in home monitoring applications. (6 pages)CT-based robust statistical shape modeling for forensic craniofacial reconstruction
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0126
Estimating the facial outlook from an unidentified skull is a challenging task in forensic investigations. This paper presents the definition and implementation of a craniofacial model for computerized craniofacial reconstruction (CFR). The craniofacial model consists of a craniofacial template that is warped towards an unidentified target skull. The allowed transformations for this warping are statistically defined using a PCA-based transformation model, resulting in a linear combination of major modes of deformations. This work builds on previous work [1] in which a statistical model was constructed based on facial shape (represented as a dense set of points) variations and sparse soft tissue depths at 52 craniofacial landmarks. The main contribution of this work is the extension of the soft tissue depth measurements to a dense set of points derived from a database of head CT-images of 156 patients. Despite the limited amount of training data compared to the number of degrees of freedom, the reconstruction tests show good results for a larger part of the test data. Root mean squared error (RMSE) values between reconstruction results and ground truth data smaller than 4 mm over the total head and neck region are observed. (6 pages)The potential of Internet of Things (IOT) for assisted living applications
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0040
Presents a collection of slides covering the following topics: m-IOT; healthcare IT; medicine; mobile healthcare; Long Term Evolution; 4G health; m-health; Internet of things; RFID; protocol; WSN; AAL systems; diabetes management system; cellular phone; assisted living; wireless medical sensors; ulPv6 and 6LoWPAN. (40 pages)Trends and issues in community telecare in the United Kingdom
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0031
With an increasing elderly population putting a strain on the cost of care, and the social, health and cost benefits of using community social care and telecare services, telecare is experiencing growth. This paper discusses trends in demand for telecare and issues with service provision and mainstreaming services and evaluates access, service reliability, the market, standards, interoperability and technology developments. (5 pages)Legal, ethical and socio-economic aspects of community telecare
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0023
Legal, ethical and socio-economic factors in community telecare differ from those pertaining to telemedicine and are examined with reference to older persons' care. Issues discussed include equipment liability, service malpractice, technical and service standards, consent (including the Mental Capacity Act), research, trials, human factors, dependence, privacy, security, accessibility, quality, affordability, social inequalities and community factors. (6 pages)Internet of M-health Things 'm-IOT'
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0036
Internet of Things (IoT) is a new and evolving concept that provides connectivity to the Internet via sensing devices to achieve intelligent identification and management in a heterogeneous connectivity environment. From the assisted living perspective, this emerging concept will enable new communication connectivity routes between elderly disabled patient and care services through innovative networking architectures in AAL environments. M-health is defined as "mobile computing, medical sensor, and communications technologies for health care" [1]. This evolutionary concept provides both mobility and 'always connected' functionalities for different healthcare applications. In this paper we introduce a new amalgamated concept of Internet of m-health Things (m-IoT). m-IoT is a new concept that matches the functionalities of m-health and IoT for a new and innovative future (4G health) applications. In principle m-IoT introduce a new healthcare connectivity paradigm that interconnects IP-based communication technologies such as 6LoWPAN with emerging 4G networks for future Internet based healthcare services. In this paper we will present a general m-IoT architecture based on 6LoWPAN technology for measurement of body temperature as an example for healthcare application. (3 pages)A review of the state of the art in artifact removal technologies as used in an assisted living domain
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0033
There has been significant growth in the area of ubiquitous, pervasive, distributed healthcare technologies due to the increasing burden on the healthcare system and the impending demographic shift towards an aging population. The move from a hospital-centric healthcare system towards in-home health assessment is aimed to alleviate the burden on healthcare professionals, the health care system and caregivers. Advances in signal acquisition, data storage and communication channels provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and reduce the utility of the affected part of the signal. The degrading effect of the artifacts significantly increases when data collection is moved from the clinic into the home. Advances in signal processing have brought about significant improvement in artifact removal over the last number of years. This paper reviews the most common physiological and location-indicative signals recorded in the home and documents the artifacts which occur most often. A discussion of some of the most common artifact removal techniques is then provided. An evaluation of the advantages and disadvantages of each is given with reference to the assisted living environment. (6 pages)Movable patient health monitoring using GPS
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0070
The Heartbeat Malfunction detector is a device which monitors the heartbeat and body temperature of a movable patient via a heartbeat Sensor, temperature Sensor and compares it against a predetermined value set and if these values cross a particular limit it would automatically alert the doctor of the patient via a sms from a Bluetooth enabled mobile, using Bluetooth Wireless Technology. A heartbeat sensor is directly connected to a microcontroller, which measures the Beat per Minute (BPM). This heart beat sensor is designed to give digital output of heart beat when a finger is placed inside it. With each heart pulse the detector signal vanes. This variation is converted to electrical pulse. This signal is amplified and triggered through an amplifier which outputs +5V logic level signal. The digital pulses are fed to the external interrupt of microcontroller 8051. By using a software counter in the code, we can count the pulses. The microcontroller (8051) is here used to develop a heart beat monitoring system. By placing your finger in between a LED and photo resistance, we can detect the pulses of heart. A temperature sensor is used to check the body temperature of patient.Design and qualitative evaluation of tactile devices for stroke rehabilitation
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0025
Rehabilitation environments combining virtual reality with everyday motor tasks can promote recovery from neurological illness, such as stroke. Tactile devices, providing physical stimulation to the skin, may improve motor retraining. While many tactile devices have been reported, there is a distinct paucity of studies evaluating how they are perceived. This multidisciplinary research has investigated three tactile devices (vibration motors, a motor-driven 'squeezer', and shape memory alloys) for providing a realistic sensation of static interaction with virtual objects. These devices have been iteratively redesigned and qualitatively evaluated with healthy human participants. This paper presents the devices, their evaluation, and iterative redesign. (6 pages)Medical image security using LSB and chaotic logistic map
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0086
In this modem era, many researchers have been concentrating more on using the field of chaos and its applications for their research. Particularly, medical image encryption and decryption using chaotic signals are proposed frequently for medical image cryptography and steganography. In this article, the patient medical details in text form and medical image of the organ in pictorial form are encrypted and decrypted using two different set of algorithms. One of the advantages of this method is its security, which is provided by the chaotic signal.A recurrent quantum neural network model enhances the EEG signal for an improved brain-computer interface
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0028
The brain-computer interface (BCI) technology is a means of communication that allows individuals with severe movement disability to communicate with external assistive devices using the electroencephalogram (EEG) or other brain signals. The human mind and mental processes are inherently quantum in nature. It is therefore logical to investigate the possibility of designing new approaches to Brain-computer interface (BCI) with the amalgamation of quantum and classical approaches. This paper presents an intelligent information processing paradigm to enhance the raw electroencephalogram (EEG) data. A Recurrent Quantum Neural Network (RQNN) model using a non linear Schrodinger Wave Equation (SWE) is proposed here to filter the Motor Imagery (MI) based EEG signal of the BCI user. It is shown that if the potential field of the SWE is excited by the raw EEG data using a self-organized learning scheme, then the probability density function (pdf) associated with the EEG signal is transferred to the probability amplitude function which is the response of the SWE. In this scheme, the EEG data is encoded in terms of a particle like wave packet which helps to recover the EEG signal by denoising the raw data. Thus the filtered EEG signal is a wave packet which glides along and moves like a particle. This denoised EEG signal is then fed as an input to the feature extractor to obtain the Hjorth features. These features are then used to train a Linear Discriminant Analysis (LDA) classifier. It is shown that the accuracy of the classifier output over the training and the evaluation datasets using the filtered EEG is enhanced compared to that using the raw EEG signal for six of the nine subjects with a fixed set of parameters for all the subjects. (6 pages)DE-noising of EEG signals using Bayes shrink based on Coiflet transform
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0048
The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. However, the presence of artifacts like Electro-oculogram (EOG), Electrocardiogram (ECG), Electromyogram (EMG) and power-line noise in the EEG signal is a major problem in the study of brain potentials. Hence, these superfluous signals are needed to be removed. There are various methods for removal of artifacts. This paper discusses a wavelet-based approach for correcting the artifacts generated by eye blinks, eyeball movements and facial muscle movements in EEG using threshold called Bayes Shrink based on Coiflet Transform.Making Mobile Health work - an alternative look at Mobile Health business models
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0037
Presents a collection of slides covering the following topics: mobile health business models; health staff; mHealth; disease curing; health service; hospitals; drug dealers; vitamin pills; supermarket; tablets; local healthcare system; and medical profession. (28 pages)Development of an ambient intelligent environment to facilitate the modelling of 'well-being'
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0035
The monitoring of individuals conditions in a purely medical sense, through the acquisition and interpretation of physiological data by a medical practitioner on a regular, personal basis, will not be a viable proposition in the future due to the projected numbers of individuals requiring such activity. The programme of work being developed in the University of Glamorgan defines a networked, wireless ambient data acquisition environment that serves as real world Ambient Intelligent Environment (AIE) test bed. Its objective is the classification and integration of the data into a knowledge based intelligent system to provide the mechanism of ubiquitous computing. This paper presents a framework in which those ideas can be applied and tested in a distributed architecture that will facilitate invasive observation and even a level of supervision through pervasive and intelligent supervisor interventions. Initial work that has been undertaken has attempted to infer the emotional state of an individual within a monitored ambient intelligent environment and these early findings are presented as an indicator of the potential of the architecture being developed. The knowledge abstraction mechanism and classification techniques focus on the use of fuzzy logic methodology where a non-intrusive intelligent learning and adaptation agent that could be embedded in Ambient Intelligent Environments will be discussed. This proposed agent learns and models the user behaviour in order to control the environment on their behalf with respect to his emotional state. In order to realise one of the main requirements of ambient intelligent systems, the agent was developed to act in an adaptive way where it will manage and control the environment on behalf of the user with respect to his emotional state as an attempt to understand the word "Well- being". It will also allow the rules to be adapted and extended online, assisting a life-long learning technique as the environmental conditions changes and the user behaviour adjust with it. (6 pages)The Technology Strategy Board's Assisted Living Innovation Platform
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0038
Presents a collection of slides covering the following topics: technology strategy board; assisted living innovation platform; ageing population; and knowledge transfer. (43 pages)Brain Music System: standardized brain music therapy
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0029
The paper discusses a standardized therapeutic treatment using the Brain Music System, a system that uses Sonified Neurofeedback accurately and cost effectively to convert brainwaves into musical sound using Digital Signal Processing algorithms. A standard course of sonified neurofeedback therapy (for example 15 sessions), tailored specifically to individual patients, is a realistic possibility due to the inexpensive and portable nature of the system, and could be used both inside or even outside of a traditional clinical setting for subjects suffering from a wide array of mental and neurological conditions. In a pilot study to test the algorithms and output of the Brain Music System, the distribution of the Alpha, Beta and Theta waves in normal subjects corresponds closely to that in published studies using standard high-end equipment (confined to expensive clinical setups). These results allows the Brain Music System to align its protocol to practice standards, and to better associate standard algorithmic tasks to each of the three mentioned brainwave types. (4 pages)Clustering performance analysis of FCM algorithm on iterative relaxed median filtered medical images
http://dl-live.theiet.org/content/conferences/10.1049/ic.2011.0069
Noise removal is a major concern in image processing particularly in medical imaging. In this paper, a novel noise removal technique called Iterative relaxed median filter (IRMF) has been proposed and the effect of noise removal, by means of median filtering, on Fuzzy C-Means Clustering (FCM) has been analysed. Noise removal is carried out by various median filtering methods such as standard median filter (SMF), adaptive median filter (AMF), hybrid median filter (HMF) & relaxed median filter (RMF) and the performance of these methods is compared with the proposed method.An algorithm proposed for semi-supervised learning in cancer detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0487
Semi-supervised learning, a relatively new area in machine learning, represents a blend of supervised and unsupervised learning, and has the potential of reducing the need of expensive labelled data whenever only a small set of labelled examples is available. In this paper an algorithm for Semi Supervised learning for detecting Cancer is proposed. We use the few labelled data to train the SVM classifier with Gist-SVM. We enlarge the number of training examples with SVM-Naive Bayes classifiers. We used WBC dataset from UCI Machine learning depository for our proposed methodology.Sensing, processing and application of EMG signals for HAL (Hybrid Assistive Limb)
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0463
Hybrid Assistive Limb (HAL) is an assistive technology device for supporting physically disabled persons by understanding the percentage of their disability. This work aims to design and develop a HAL based on Electromyogram (EMG) signals. The EMG signal is a biomedical signal that measures electrical currents generated by muscles. These signals can be used for clinical/biomedical applications if advanced methods for detection, decomposition, processing, and classification are available. The pattern of the EMG signal produced may differ depending on the activity of the muscle movement. Four types of biceps muscle activities are identified using the signal pattern generated from raw surface EMG data. Threshold detection method and pattern recognition method were carried out and it is found that pattern recognition method is more generalized method for classification as threshold method is user dependent. The overall classification rate of about (80-83) % obtained using LDA and a classification rate of more than 90% obtained using ANN. Control commands for a stepper motor used for driving artificial limb are developed from the classified EMG signal and stepper motor control is achieved through computer parallel port.A robust segmentation algorithm for branch structure and its implementation
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0298
Medical image segmentation is a serious challenge in medical image processing. The medical images have low contrast, the variability of the organizational characteristics, the ambiguity of the border of the tissues and the complexity of microstructures (e.g. blood vessels, nerves). These features restrict the segmentation of the branch structure in the medical images. This paper presents a robust medical segmentation algorithm that combines the active contour model and region growth segmentation method. General locations, given by the region growth segmentation method, act as the initial position of snake model for segmentation. In this way, we can get the branch structure in the abdominal CT, such as the abdominal aorta, the celiac trunk and mesenteric artery. The experimental result shows that the revised algorithm achieves a better practical effect through surface rendering from VTK. It can help the doctor diagnose the illness wisely and objectively. (5 pages)Local transparency technique for heart model from VTK
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0863
In the surgical operation, the catheter can be used to collect the point clouds data and do the ablation operation. Then we can get the three-dimensional heart reconstruction model from point clouds using the surface reconstruction algorithm in visualization system. Usually, the heart model is displayed opaquely in the visualization window and light can not go through the object. So doctors could hardly see the location of the catheter inside the model in real-time system. In addition, sometimes doctors have to observe the internal structure and the interested region in the model for clinical diagnosis and analysis. Therefore, it is necessary for us to do the transparency to meet the doctor requirement. Transparency can be generally divided into overall transparency and local transparency. Sometimes it is difficult for doctor to recognize the relative position between the catheter and the model surface from the overall transparency. Allowing for this reason, in this paper, we not only introduce the overall transparency, but also propose two ways to deal with the interest region transparency. All the methods are implemented on VTK platform.A comparative study of information extraction tools used for biological database
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0492
The Internet presents a huge amount of biological data. It is difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems and tool that transform the Web pages into program-friendly structures such as a relational database. This paper made a study on information extraction tools. Which can be used for Biological databases. The tools have been classified based on four categories such as tools for Manually constructed Information Extraction, Supervised Wrapper Induction System, Semi supervised Information Extraction Systems, Unsupervised Information extraction Systems. Finally we made a comparative study on the Information Extraction tools used for Biological database based on the technique used such as scan Pass, Extraction Rules Type, Features used, Learning Algorithm and Tokenization Schemes.Research on model correction based on scattered point cloud data surface reconstruction
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0855
Surface reconstruction based on point cloud data has been extensively studied. In this paper, the original data has a lot of noise and without normal vector information, so we use Poisson surface reconstruction algorithm to create 3D heart model, which has good robustness for noisy and irregular point cloud data. Then correct the shape by removing or adding points from the model surface to perfect the heart model, and reconstruct the new model by Power Crust algorithm based on the correctional surface point cloud data with normal vector, which is quick, accurate and efficient and very suitable for fast model correction. The results not only accurately display the spatial relationship of the heart, but also can be discretionary scaling and rotation in 3D space. The visual 3D graphical structures of the reconstructed heart model can display diseases (cardiopathy & arrhythmia) and enable catheter navigation in real time, which can help doctors to detect and diagnose diseases effectively, and improve the accuracy and security of medical diagnosis.A study of performance of longest common subsequence identification with sequence identity of biosequences
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0446
Searching for clue to the result with biosequences is an important area of research for computational scientists in bioinformatics. The sequences are longer and demand more and more computational power in order that the result yields benefits to the society. More often the computational results are used in obtaining quick clue to the expected results of lengthy laboratory process. The identity and similarity between sequences provide the basic clue and guidance as to how to progress with work. This paper analyses SRLCS algorithm with the tools like CLUSTAL-W, and MUSCLE in identifying Longest Common Subsequence (LCS) with reference to identity between the bio sequences.A combined MI-AVR approach for informative gene selection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0489
Accurate classification of diseases from microarray gene expression profile is a challenging task because of its high dimensional low sample data. Most of the gene selection methods employ the criterion function on the entire microarray samples only once which cannot exactly represent the relevance among genes. This paper proposes a hybrid gene selection algorithm that selects genes in two stages, initially with all samples using Mutual Information followed by only unclassified samples using Augmented Variance Ratio. Feed Forward Neural Network trained by Back Propagation algorithm is used to classify the samples. The performance of the proposed approach is tested using six gene expression datasets. Simulation results show that the proposed method selects the genes which are highly informative and produces good classification accuracy than other methods reported in the literature.Some considerations on the design of passive optical networks
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.1361
Wavelength-agile hybrid WDM-TDM passive optical networks (PON) architecture, dual-mode ONT which enables user roaming between EPON and GPON, integration of PON, PON health monitoring (PHM) and home health monitoring (HHM), all these issues relate to design and applications of next generation PON are discussed. (2 pages)A de-noising method for heart sound signal using Otsu's threshold selection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0848
In this research, Otsu's method methods is quoted and applied to de-noise heart sounds so that unwanted noises can be separated from a combined set of noises extracted through an electronic stethoscope. The noise was analyzed by applying a random but suitable threshold section method, the unwanted noise was reduced and the useful sound was reconstructed. The initial result shows that the method applied is effective and the reconstructed signal appears to be providing a better quality of heart sound.Research of medical image reconstruction system based-on MAC OS
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0315
Unlike other 3D image visualization system based on Windows, this paper takes full use of the image processing advantages on MAC OS, with the library of development of the platform VTK, to achieve the medical reconstruction system. In this paper, we will not only discuss the environment building of VTK on MAC OS, but also achieve some functions of medical image system. The stability and feasibility of the system are being verified by the simulation results. (4 pages)Unsupervised textural segmentation of SonoElastographic breast images
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0462
Breast Cancer, the most common malignancy in women and the second leading cause of death for women all over the world. By earlier detection of cancer, the better treatment can be provided. SonoElastography, a new medical imaging technique reduces un necessary biopsies compared to mammography and conventional ultrasonography. The diagnosis and treatment of the cancer rely on segmentation of SonoElastographic images. Texture features are widely used in classification problems, i. e. mainly for diagnostic purposes where the Region Of Interest (ROI) is delineated manual ly. It has not yet been considered for SonoElastographic segmenta tion. SonoElastographic images of 15 patients taken using Siemens Acuson Antares are considered for experimentation. The images contain both benign and malignant tumors. From the experimental procedure it is proposed that the combination of texture features, Local Binary Pattern (LBP), Contrast and Variance are best suited for segmentation of SonoElastographic breast images. The images are first enhanced using sticks filter to remove noise, to improve contrast, and emphasize tumor boundary. Then extract the features to segment the breast image. The resultant images undergo some post-processing steps to remove the spurious spots. The segmented image is thinned to mark the tumor boundary. The results are then quantified with the help of an expert radiologist. The proposed work can be used for further diagnostic process, to decide if the segmented tumor is benign or malignant.Intelligent fussy system based dermoscopic image segmentation for melanoma detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0461
Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. This paper explains the task of segmenting skin lesions in Dermoscopy images based on intelligent Fuzzy clustering techniques for the early diagnosis of Malignant Melanoma. The various Fuzzy clustering techniques used are Fuzzy C Means Algorithm (FCM), Possibilistic C Means Algorithm and Hierarchical C Means Algorithm. The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE), False Negative Error (FNE) Coefficient of similarity, spatial overlap and their performance is evaluated.Medical application on Internet of Things
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0751
Internet technology provides various services via network. With the diversification of terminals and development of internet technology, internet technology has come into the stage of Next Generation Network(NGN) technology. Comparing with the current Internet technology that provides services in the imaginary space, the technology on Internet of Things (IOT) is based on real word. It links things together via sensors and wireless communication technology to collect a variety of information on the condition of people and their surrounding space in the real world. The combination of Internet technology and Technology on IOT integrates physical world and imaginary space on a shared platform to eliminate the constraints of imaginary space and provide intricate, diverse, and advanced services focusing on people, which have not been achieved [1]. The future direction for the integration of Internet technology and technology on IOT, the technology of body sensor network and information services are suggested. This paper analyze the possibility and related issues of providing advanced services for human health management in the real world and research direction of medical technology on IOT.Health guard system with emergency call based on smartphone
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0926
This paper summarises the development of an innovative mobile application on healthcare Health Guard system using the smartphone. The system has incorporated a number of important features including location-based service, automatic falling detection and cancellation system and is build on cloud computing. The system detects the stumbling or the crash encountered by the mobile user. The automatic falling detection system will be triggered and judge whether the user in a perilous position. Meanwhile, the pre- defined contact persons will be alerted by a SMS sent from our system. The useful information including the location of the user and the route which can enable to reach the mobile user in danger will be sent through the cloud to the website. The contact persons can correspondingly see the necessity of reporting to the police. A number of tests have demonstrated the applicability of the system.Epicardial contour segmentation by using level set with prior shape and region
http://dl-live.theiet.org/content/conferences/10.1049/cp.2011.0993
A segmentation model is proposed for the epicardial contour of the cardiac MRI image based on level set with prior shape and region. Considering the shape correlation of the left ventricle (LV) in the cardiac image sequences, the segmentation result of the current frame is referred as the prior shape of the next frame and the contraction and diastolic change area of LV is exploited as the prior region. The initial evolving curve is automatically located by calculating the posterior probability of the pixel belonging to the chamber of LV. The prior shape and region prevent the evolving curve from leaking off the outer contour and pull it to the accurate edges. Promising experimental results are obtained on the real cardiac sequences.On the study of a ubiquitous healthcare network with security and QoS
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0551
As a consequence of rapid technological development, the normally isolated medical sensor platform is gradually being integrated into networks. However, most ubiquitous or mobile health systems are lacking a system that combines both network security and QoS (Quality of Service). In this paper investigates the possible security and QoS methods of a ubiquitous healthcare (U-healthcare) network platform in OSI (Open System Interconnections)-layers. This network platform is based on the Wireless Overlay Networks (WON) Bluetooth, 802.11 and 802.16 over IPv6 Network, and provides a secure and stable U-healthcare platform by including the application of healthcare sensors with RFID, U-healthcare PDA VoIPv6, falling detection, and patient orientation.Research on image filtering method to combine mathematics morphology with adaptive median filter
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.1152
As a imaging technology of no-traditional lamp-house irradiation ,ultrasound medical imaging with its many advantages, such as no harm to the human body, real-time, cheap and easy to use, is widely used in clinic. But the ultrasonic imaging speckle noise make it difficulties to distinguish between normal tissue and pathological tissue. According to the character of noise in the medical ultrasonic image , an new method of the medical ultrasonic imaging filter based on mathematics morphology and adaptive filtering is proposed after analysis of speckle noise and general filter, and an experiment is made to validate. The experimental method is as follows : Firstly the Rayleigh noise is imposed on the original image , and then the median filter and the adaptive median filter are used on the contaminated image. Secondly the morphological filter is used to improve image quality and enhance the contrast , after the adaptive median filter is used on the image, to retain more necessary details. Finally the three noise filtering methods are compared from the images denoise and evaluation . And the results indicate that the new method is superior to other ones.An accurate and adaptive pedometer integrated in mobile health application
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.1032
An interesting and valuable application in sensor network technology is Personalised Rehabilitation. In particular, through the combination of a smart phone and wearable sensors, patients can follow and have feedback on their exercise programmes, while enhancing the medical monitoring. In this paper, we design a system to measure the patient's activity by estimating his walking habits. The system, ready to be integrated in a mobile health application, consists of an inertial sensor with a tri-axial orthogonal accelerometer attached to patient's foot, while the sensor is connected to a smart phone for data processing. Additionally, we propose an algorithm for step detection and gait state estimation. The challenge is to provide reliable and accurate detection when pacers are in different gait states. Also, the algorithm will reduce error drifts at start moment when sensor signals are very unstable. Extensive experiment results will demonstrate that the algorithm provides good performance in terms of accuracy, adaptability and memory use.Efficient EEG compression using JPEG2000 with coefficient thresholding
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0488
This paper outlines a scheme for compressing EEG signals based on the JPEG2000 image compression algorithm. Such a scheme could be used to compress signals in an ambulatory system, where low-power operation is important to conserve battery life; therefore, a high compression ratio is desirable to reduce the amount of data that needs to be transmitted. The JPEG2000 specification makes use of the wavelet transform, which can be efficiently implemented in embedded systems. In this research, the JPEG2000 standard was broken down to its core components and adapted for use on EEG signals with additional compression steps added. Variations on the compression architecture were tested to maximize compression ratio (CR) while minimizing reconstructed percentage root-mean-squared difference (PRD) and power requirements. Tests indicate that the algorithm performs well in efficiently compressing EEG data, without significant loss in signal fidelity.Capacitive instrumentation amplifier for low-power bio potential signal detection
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0487
This paper presents the design of a low-power capacitively-coupled CMOS instrumentation amplifier (INA) for long term bio potential measurement and recording applications. The increased demand for low cost, portable and wearable medical monitoring equipment for EEG (electroencephalogram) and ECG (electrocardiogram) is in turn giving rise to a need for very low power, but high precision, analogue front ends. EEG and ECG signals are differential signals characterized by very low amplitude (up to 100μV for EEG and up to 5mV for ECG), relatively low bandwidth (0.5Hz-150/300Hz) and a quite noisy environment. We present an instrumentation amplifier that achieves the specifications recommended by the application while lowering as much as possible the power consumption through appropriate design choices. The front end has been designed and simulated using a CMOS 0.35μm AMS process.An e-service design on stroke-precaution for elderly
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.0584
The urgent needs of medical assistant for elderly increase rapidly. This paper presents an e-service design on stroke-precaution for elderly, followed by its implementation and evaluation. The e-service design focuses on the elderly problems including the degeneracy of memory and vision as well as the recession of attention. These physiological problems become the barriers for elderly to learn knowledge effectively. In order to help elderly to obtain knowledge for stroke-precaution, this study provides a health information system and its interface design that specifically addresses the physiological issues for elderly. Owing to increase the accessibility of the stroke-precaution health e-service, we utilize multimedia streaming technology and video clips to present stroke-precaution knowledge for elderly. Unlike traditional e-learning system provide massive healthy information, this study provide a personalized health service design which only provide related healthcare information according to user's health condition. The user interface as well as the recommendation mechanism is designed for elderly. Analytical result indicates our service design could help elderly to leap over the physiological problems they encountered. With stroke-precaution healthy service for elderly, life quality could be improved for elderly and their families.Intravenous infusion monitoring system based on WSN
http://dl-live.theiet.org/content/conferences/10.1049/cp.2010.1024
Reviewing various treatment plans, the progress and velocity of intravenous infusion must be strictly controlled. Undeniable, present artificial monitoring methods not only increase the burden of patients, relatives and medical staff, but are vulnerable to lead oversight as well. This paper presents the design and implementation of a novel wireless sensor network for intravenous infusion monitoring based on slot-coupled infrared emitting diode as sensors, chip ATMEGE128L as MCU and chip CC2420 as ZigBee-based RF communication. The system has following characteristics: non-touch droplet monitor, easy to reuse, multiple protections on system accuracy and reliability, easy to integrate with existing hospital management system due to flexible design of the host computer software, low cost, and easy to launch large-scale applications.