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C S Pattichis

Publications and source records attributed to C S Pattichis.

At least 19 recordsLinked to original sources
PubMed2007

Snakes based segmentation of the common carotid artery intima media.

Ultrasound measurements of the human carotid artery walls are conventionally obtained by manually tracing interfaces between tissue layers. In this study we present a snakes segmentation technique for detecting the intima-media layer of the far wall of the common carotid artery (CCA) in longitudinal ultrasound images, by applying snakes, after normalization, speckle reduction, and normalization and speckle reduction. The proposed technique utilizes an improved snake initialization method, and an improved validation of the segmentation method. We have tested and clinically validated the segmentation technique on 100 longitudinal ultrasound images of the carotid artery based on manual measurements by two vascular experts, and a set of different evaluation criteria based on statistical measures and univariate statistical analysis. The results showed that there was no significant difference between all the snakes segmentation measurements and the manual measurements. For the normalized despeckled images, better snakes segmentation results with an intra-observer error of 0.08, a coefficient of variation of 12.5%, best Bland-Altman plot with smaller differences between experts (0.01, 0.09 for Expert1 and Expert 2, respectively), and a Hausdorff distance of 5.2, were obtained. Therefore, the pre-processing of ultrasound images of the carotid artery with normalization and speckle reduction, followed by the snakes segmentation algorithm can be used successfully in the measurement of IMT complementing the manual measurements. The present results are an expansion of data published earlier as an extended abstract in IFMBE Proceedings (Loizou et al. IEEE Int X Mediterr Conf Medicon Med Biol Eng POS-03 499:1-4, 2004).

Animals↗
PubMed2007

Selected eHealth applications in Cyprus from the training perspective.

OBJECTIVES: In this paper a review of selected eHealth applications in Cyprus is presented linked with their success or failure based on their training activities. METHODS: The eHealth systems presented and their training activities include an update of the health information system (HIS) in the public hospitals, a medical system for emergency telemedicine (AMBULANCE and EMERGENCY-112 projects), a home monitoring system for cancer patients (DITIS), a satellite-based network in healthcare applications (EMISPHER and HEALTHWARE projects), and the training activities of the Cyprus Society of Medical Informatics. Different methodologies for training were used ranging from classical approaches like train the trainers, using demo cases followed by personal training, group training, and workshops, to more recent methodologies based on eLearning sessions including teleconsultations. RESULTS: The training was carried out successfully in all cases. However, not all eHealth systems were put into practice successfully, mainly for reasons not related to training. CONCLUSIONS: It is anticipated that this paper will promote the importance of these applications and their training activities as well as help in the spin off of others thus enabling the offering of a better service to the citizen.

Computer Communication Networks↗
PubMed2006

Texture analysis of ultrasonic images of symptomatic carotid plaques can identify those plaques associated with ipsilateral embolic brain infarction.

OBJECTIVES: The aim of our study was to determine the association between objective, computerised texture analysis of carotid plaque ultrasonic images and embolic CT-brain infarction in patients presenting with hemispheric neurological symptoms. DESIGN: Cross-sectional study in patients with 50%-99% (ECST) carotid stenosis. PATIENTS AND METHODS: Carotid plaque ultrasonic images (n=54, 26 with TIAs and 28 with stroke) obtained during carotid ultrasound were normalised and standardised for resolution and subsequently assessed visually for the presence of discrete echogenic or juxtaluminal echolucent components and overall echogenicity (plaque type). Using computer software, 51 histogram/textural features of the plaque outlines were calculated. Factor analysis was subsequently applied to eliminate redundant variables. Small cortical, large cortical and discrete subcortical infarcts on CT-brain scan were considered as being embolic. RESULTS: Twenty-five cases (46%) had embolic infarcts. On logistic regression, grey-scale median (GSM), a measure of echolucency, spatial grey level dependence matrices (SGLDM) correlation and SGLDM information measure of correlation-1, measures of homogeneity were significant (p<0.05), but not grey level runlength statistics (RUNL) Run Percentage (RP), stenosis severity, type of symptoms or echolucent juxtaluminal components. Using ROC curves methodology, SGLDM information measure of correlation-1 improved the value of GSM in distinguishing embolic from non-embolic CT-brain infarction. CONCLUSION: Computerised texture analysis of ultrasonic images of symptomatic carotid plaques can identify those that are associated with brain infarction, improving the results achieved by GSM alone. This methodology could be applied to prospective natural history studies of symptomatic patients not operated on or randomised trials of patients undergoing carotid angioplasty and stenting in order to identify high-risk subgroups for cerebral infarction.

Algorithms↗
PubMed2006

Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering.

Image quality is important when evaluating ultrasound images of the carotid for the assessment of the degree of atherosclerotic disease, or when transferring images through a telemedicine channel, and/or in other image processing tasks. The objective of this study was to investigate the usefulness of image quality evaluation based on image quality metrics and visual perception, in ultrasound imaging of the carotid artery after normalization and speckle reduction filtering. Image quality was evaluated based on statistical and texture features, image quality evaluation metrics, and visual perception evaluation made by two experts. These were computed on 80 longitudinal ultrasound images of the carotid bifurcation recorded from two different ultrasound scanners, the HDI ATL-3000 and the HDI ATL-5000 scanner, before (NF) and after (DS) speckle reduction filtering, after normalization (N), and after normalization and speckle reduction filtering (NDS). The results of this study showed that: (1) the normalized speckle reduction, NDS, images were rated visually better on both scanners; (2) the NDS images showed better statistical and texture analysis results on both scanners; (3) better image quality evaluation results were obtained between the original (NF) and normalized (N) images, i.e. NF-N, for both scanners, followed by the NF-DS images for the ATL HDI-5000 scanner and the NF-DS on the HDI ATL-3000 scanner; (4) the ATL HDI-5000 scanner images have considerable higher entropy than the ATL HDI-3000 scanner and thus more information content. However, based on the visual evaluation by the two experts, both scanners were rated similarly. The above findings are also in agreement with the visual perception evaluation, carried out by the two vascular experts. The results of this study showed that ultrasound image normalization and speckle reduction filtering are important preprocessing steps favoring image quality, and should be further investigated.

Carotid Arteries↗
PubMed2003

Texture-based classification of atherosclerotic carotid plaques.

There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation.

Algorithms↗
PubMed2002

Healthcare telematic applications in Cyprus.

OBJECTIVES: a) To present a review of ongoing health telematic applications in Cyprus. b) To promote the use of these health telematic applications in the Cyprus region. c) To help in the spin off of other health telematic applications thus enabling the offering of a better health service to the citizens. METHODS AND RESULTS: The health telematics applications include a medical system for emergency telemedicine (AMBULANCE and EMERGENCY-112 projects), a system for the evaluation of the risk of stroke by telemedicine (EROS), a diagnostic telepathology network in gynaecological cancer (TELEGYN), a collaborative virtual medical team for home healthcare of cancer patients (DITIS), and a health telematics training network (HEALTHNET). The paper refers to the set-up and characteristics of these applications and tries to relate them with the health policies that should be applied in Cyprus. CONCLUSIONS: It is anticipated that this paper will promote the importance of health telematics applications for Cyprus and increase the awareness on the possibilities that these applications offer for health policies in all levels of health related human resources.

Ambulances↗
PubMed2000

AM-FM texture segmentation in electron microscopic muscle imaging.

This paper describes the application of an amplitude modulation-frequency modulation (AM-FM) image representation in segmenting electron micrographs of skeletal muscle for the recognition of: 1) normal sarcomere ultrastructural pattern and 2) abnormal regions that occur in sarcomeres in various myopathies. A total of 26 electron micrographs from different myopathies were used for this study. It is shown that the AM-FM image representation can identify normal repetitive structures and sarcomeres, with a good degree of accuracy. This system can also detect abnormalities in sarcomeres which alter the normal regular pattern, as seen in muscle pathology, with a recognition accuracy of 75%-84% as compared to a human expert.

Humans↗
PubMed2000

Content-based retrieval of breast cancer biopsy slides.

The Biopsy Analysis Support System (BASS), previously used for image analysis of immunohistochemically stained sections of breast carcinoma, has been extended to include indexing and content-based retrieval of biopsy slide images from a database of 57 captured cases. Images from histopathological biopsy slides are described and these are accessed in terms of the properties of either individual nuclei or groups of cell nuclei present in the slide. Visual similarity of cases is specified in terms of a diagnostic index, commonly known as the H-score, which incorporates the heterogeneity of nuclear staining intensity, as well as the percentage of nuclei staining at specific intensities. The system provides a platform that can be exploited in telepathology and teleconsultation, but further research is needed to explore its full potential and accuracy in a diagnostic clinical environment.

Breast Neoplasms↗
PubMed1999

A morphological analysis of the macro motor unit potential.

The technique of macro EMG is used to investigate the motor unit architecture in a number of pathological conditions. Amplitude and area are the most commonly used criteria, but these parameters alone are not sufficient to assess the complexity of the macro MUP morphology. In an attempt to examine the morphology of the macro MUP in more detail, additional measures were investigated including, (i) average power, (ii) duration, and (iii) number of phases. Macro MUP duration was defined as the time parameter that contains a particular fraction (90%) of the total power of the potential. The above mentioned parameters were evaluated for normal subjects and for patients suffering with motor neuron disease (MND), spinal muscular atrophy (SMA), and Becker's muscular dystrophy (BMD). It is shown that high amplitude and average power macro MUPs give shorter macro MUP duration than macro MUPs with normal amplitude. In contrast, in low amplitude macro MUPs there is a tendency towards a higher duration measure, as compared with the duration of the normal amplitude macro MUPs. Also, t-test results for the duration measure gave a significant difference between the NOR-MND, and no significant difference between the NOR-BMD and NOR-SMA groups at P<0. 05. Significant difference between the NOR and the three disease groups investigated was obtained for the parameters log amplitude, log area, and log average power. The number of phases was not significantly different between the NOR and the rest of the groups. In conclusion, the average power and duration parameters can possibly be used as additional discriminators to detect abnormalities of the macro motor unit potential in both needle and surface EMG but further investigation is necessary.

Biopsy↗
PubMed1999

Unsupervided pattern recognition for the classification of EMG signals.

The shapes and firing rates of motor unit action potentials (MUAP's) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at low to moderate force levels, it is required: i) to identify the MUAP's composing the EMG signal, ii) to classify MUAP's with similar shape, and iii) to decompose the superimposed MUAP waveforms into their constituent MUAP's. For the classification of MUAP's two different pattern recognition techniques are presented: i) an artificial neural network (ANN) technique based on unsupervised learning, using a modified version of the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and ii) a statistical pattern recognition technique based on the Euclidean distance. A total of 1213 MUAP's obtained from 12 normal subjects, 13 subjects suffering from myopathy, and 15 subjects suffering from motor neuron disease were analyzed. The success rate for the ANN technique was 97.6% and for the statistical technique 95.3%. For the decomposition of the superimposed waveforms, a technique using crosscorrelation for MUAP's alignment, and a combination of Euclidean distance and area measures in order to classify the decomposed waveforms is presented. The success rate for the decomposition procedure was 90%.

Action Potentials↗
PubMed1999

Time-scale analysis of motor unit action potentials.

Quantitative analysis in clinical electromyography (EMG) is very desirable because it allows a more standardized, sensitive and specific evaluation of the neurophysiological findings, especially for the assessment of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, the usefulness of the wavelet transform (WT), that provides a linear time-scale representation is investigated, for describing motor unit action potential (MUAP) morphology. The motivation behind the use of the WT is that it provides localized statistical measures (the scalogram) for nonstationary signal analysis. The following four WT's were investigated in analyzing a total of 800 MUAP's recorded from 12 normal subjects, 15 subjects suffering with motor neuron disease, and 13 from myopathy: Daubechies with four and 20 coefficients, Chui (CH), and Battle-Lemarie (BL). The results are summarized as follows: 1) most of the energy of the MUAP signal is distributed among a small number of well-localized (in time) WT coefficients in the region of the main spike, 2) for MUAP signals, we look to the low-frequency coefficients for capturing the average waveshape of the MUAP signal over long durations, and we look to the high-frequency coefficients for locating MUAP spike changes, 3) the Daubechies 4 wavelet, is effective in tracking the transient components of the MUAP signal, 4) the linear spline CH (semiorthogonal) wavelet provides the best MUAP signal approximation by capturing most of the energy in the lowest resolution approximation coefficients, and 5) neural network DY (DY) of Daubechies 4 and BL WT coefficients was in the region of 66%, whereas DY for the empirically determined time domain feature set was 78%. In conclusion, wavelet analysis provides a new way in describing MUAP morphology in the time-frequency plane. This method allows for the fast extraction of localized frequency components, which when combined with time domain analysis into a modular neural network decision support system enhances further the DY to 82.5% aiding the neurophysiologist in the early and accurate diagnosis of neuromuscular disorders.

Action Potentials↗
PubMed1997

Learning systems in biosignal analysis.

In biosignal analysis, the utility of artificial neural networks (ANN) in classifying electromyographic (EMG) data trained with the momentum back propagation algorithm has recently been demonstrated. In the current study, the self-organizing feature map algorithm, the genetics-based machine learning (GBML) paradigm, and the K-means nearest neighbour clustering algorithm are applied on the same set of data. The aim of this exercise is to show how these three paradigms can be used in practice, given that their diagnostic performance is problem- and parameter-dependent. A total of 720 macro EMG recordings were carried out from four groups, from seven normal, nine motor neuron disease, 14 Becker's muscular dystrophy, and six spinal muscular atrophy subjects, respectively. Twenty-three of the subjects were used for training and 13 for evaluating the various models. For each subject, the mean and the standard deviation of the parameters (i) amplitude, (ii) area, (iii) average power and (iv) duration were extracted. The feature vector was structured in two different ways for input to the models: an eight-input feature vector that consisted of both the mean and the standard deviation of the four parameters measured, and a four-input feature vector that included only the mean of the parameters. Also, due to the heterogenous nature of the spinal muscular atrophy group, three class models that excluded this group were investigated. In general, self-organizing feature map and GBML models resulted in comparable diagnostic performance of the order of 80-90% correct classifications (CCs) score for the evaluation set, whereas the K-means nearest neighbour algorithm models gave lower percentage CCs. Furthermore, for all three learning paradigms: better diagnostic performance was obtained for the three class models compared with the four class models; similar diagnostic performance was obtained for both the eight- and four-input feature vectors. Finally, it is claimed that the proposed methodology followed in this work can be applied for the development of diagnostic systems in the analysis of biosignals.

Algorithms↗
PubMed1997

Computer-aided detection of breast cancer nuclei.

A computer-aided detection system for tissue cell nuclei in histological sections is introduced and validated as part of the Biopsy Analysis Support System (BASS). Cell nuclei are selectively stained with monoclonal antibodies, such as the anti-estrogen receptor antibodies, which are widely applied as part of assessing patient prognosis in breast cancer. The detection system uses a receptive field filter to enhance negatively and positively stained cell nuclei and a squashing function to label each pixel value as belonging to the background or a nucleus. In this study, the detection system assessed all biopsies in an automated fashion. Detection and classification of individual nuclei as well as biopsy grading performance was shown to be promising as compared to that of two experts. Sensitivity and positive predictive value were measured to be 83% and 67.4%, respectively. One major advantage of BASS stems from the fact that the system simulates the assessment procedures routinely employed by human experts; thus it can be used as an additional independent expert. Moreover, the system allows the efficient accumulation of data from large numbers of nuclei in a short time span. Therefore, the potential for accurate quantitative assessments is increased and a platform for more standardized evaluations is provided.

Algorithms↗
PubMed1996

Computer-aided classification of breast cancer nuclei.

Breast cancer is the most common malignancy affecting the female population in industrialized countries. Prognostic factors, such as steroid receptors visualized in biopsy slides, provide critical information to oncologists regarding the hormonal status of the individual tumors. These factors influence the choice of treatment and help in predicting patient survival and probability of recurrence. The objective of this paper is to introduce a new computer-aided system for the classification of breast cancer nuclei based on neural networks. Currently, medical experts assess steroid receptors in breast cancer biopsy slides mostly manually using four- or five-level grading schemes. These schemes are based on the assessment of two parameters: number of nuclei positive and their staining intensity. Available computerized systems define their own grading schemes based on automated measurements of low-level features, such as optical density, texture, area, and others. However, the findings produced by these systems may not be readily comprehensible by the majority of medical experts who have been accustomed to manual assessment schemes. Moreover, findings from one system cannot be directly compared to findings obtained from other computerized systems. To date, no standardized assessment scheme exists for computerized systems, while interobserver and intraobserver variabilities limit the utility of the routinely used manual assessment schemes. In this paper a new system for computer-aided biopsy analysis is introduced. Here, we focus on the system's nuclear classification module. The input to this module consists of a set of six local and global features: optical density, two chromaticity indices, a variance based texture measure, global nuclei density mean, and variance. The output of the nuclei classification module consists of a membership label in a zero to four grading scheme for each detected nucleus. The classification module is based on a feedforward neural network trained in a supervised fashion to classify the nuclear feature vectors. The sample data comprises 3015 nuclei from 28 images that were classified by a human expert. A Sammon plot visualization of the six dimensional input feature space shows that the classification problem is quite difficult. The neural network used in the classification module achieved 72% accuracy. Our result indicate that by using a nuclear classification module such as the one introduced in this paper it is possible to translate low-level system measurements into a vocabulary that is familiar to medical experts. Thus, a contribution is made to the standardization of grading schemes in addition to improving the accuracy in grading breast cancer nuclei.

Biopsy↗
PubMedDate not supplied

Autoregressive and cepstral analyses of motor unit action potentials.

Quantitative electromyographic signal analysis in the time domain for motor unit action potential (MUAP) classification and disease identification has been well documented over recent years. Considerable work has also been carried out in the frequency domain using classical power spectrum analysis techniques. Although MUAP autoregressive (AR) spectral analysis has been suggested as a diagnostic tool by a number of studies, it has not been thoroughly investigated yet. This work investigates the application of AR modeling and cepstral analysis for the diagnostic assessment of MUAPs recorded from normal (NOR) subjects and subjects suffering with motor neuron disease (MND) and myopathy (MYO). The following feature sets were extracted from the MUAP signal: (i) time domain measures, (ii) AR spectral measures, (iii) AR coefficients, and (iv) cepstral coefficients. Discriminative analysis of the individual features was carried out using the univariate and multiple covariance analysis methods. Both methods showed that: (i) the duration measure is the best discriminative feature among the time domain parameters, and (ii) the median frequency is the best discriminative feature among the AR spectral measures. Furthermore, the classification performance of the above four feature sets was investigated for three classes (NOR, MND and MYO) using artificial neural networks. Results showed that the highest diagnostic yield was obtained with the time domain measures followed by the cepstral coefficients, the AR spectral parameters, and the AR coefficients. In conclusion, MUAP autoregressive and cepstral analyses combined with time domain analysis provide useful information in the assessment of myopathology.

Action Potentials↗