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C S Pattichis

Publications and source records attributed to C S Pattichis.

14 recordsLinked to original sources
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↗