Biomedical subjects

Abraham Pouliakis

Publications and source records attributed to Abraham Pouliakis.

5 recordsLinked to original sources

PubMed2006

Discriminating benign from malignant thyroid lesions using artificial intelligence and statistical selection of morphometric features.

The objective of this study was to perform a comparative investigation of the capability of various classifiers in discriminating benign from malignant thyroid lesions. Using May Grunvald-Giemsa-stained smears taken by fine needle aspiration (FNA) and a custom image analysis system, 25 nuclear features describing the size, shape and texture of the nuclei were measured in each case. A statistical pre-processing of features revealed that only 4 of the 25 features are important when discriminating benign from malignant thyroid lesions, which were transformed and fed to four classifiers for subsequent analysis. The cases were divided into one set used for the training of classifiers, a second set used as the test set, and the remaining cases with no clear classification formed an ambiguous test set. Classification was performed at the nuclear and patient level. The technique described in this study produced encouraging results and promises to be a helpful tool in the daily cytological laboratory routine.

Anthropometry↗

PubMed2006

The potential of feature selection by statistical techniques and the use of statistical classifiers in the discrimination of benign from malignant gastric lesions.

The objective of this study was the investigation of the potential value of morphometry, feature selection and statistical classifiers techniques, such as neural networks, for the classification of benign from malignant gastric nuclei and cases. One hundred and twenty gastric smears, routinely processed and stained by Papanicolaou technique, were analyzed by a customized image analysis system. Data from half of the cases were selected to form the training set, while the remaining data formed the test set. A feature selection technique was applied in order to identify the most important nuclear features, which were used in a second stage by statistical classifiers to classify a nucleus as benign or malignant. Using the classifier results for the nuclear classification, a method to classify each individual patient was developed. The performance of the proposed method was validated through the test set. The technique described in this report produces significant results at the nuclear and patient level and promises to be a powerful assistance tool for everyday cytological laboratory routine.

Anthropometry↗

PubMed2005

Potential of radial basis function neural networks in discriminating benign from malignant lesions of the lower urinary tract.

OBJECTIVE: To investigate the potential value of morphometry and neural network tools for discriminating benign from malignant nuclei and lesions of the lower urinary tract. STUDY DESIGN: The study group consisted of 33 cases of lithiasis, 41 cases of inflammation, 66 cases of benign hyperplasia of the prostate, 4 cases of carcinoma in situ, 48 cases of grade 1 transitional cell carcinoma of the bladder (TCCB) and 123 cases of grade 2 and 3 TCCB. Images of routinely processed voided urine smears stained by the Giemsa technique were analyzed by a custom image analysis system. Analysis of the images gave a data set of features from 31,158 nuclei. A radial basis function (RBF)-type neural network was employed to discriminate benign from malignant nuclei, based on the extracted morphometric and textural features. Subsequently a second RBF classifier was employed to discriminate benign from malignant cases. The nuclei from 156 randomly selected cases (50% of total cases) was used as a training set, and the nuclei from the remaining 159 cases made up the test set. Similarly, in an attempt to discriminate at the patient level, the same 156 cases were used to train an RBF classifier; the remaining 159 cases were used for the test set. The cases used for training and testing the 2 classifiers (nuclear and patient level) were the same for the 2 kinds of classifiers. RESULTS: Application of the RBF classifier permitted the correct classification of 93.64% of benign nuclei and 85.61% of malignant, giving an overall accuracy of 84.45%. At the patient level the RBF classifier permitted an overall accuracy of 94.97%. These results were on the test sets. CONCLUSION: The role of nuclear morphologic features in the cytologic diagnosis of lower urinary tract alterations was confirmed by the results of this study. The observed overlap in feature space indicates that the nuclear characteristics do not form strictly separate clusters; that fact explains the difficulty morphologists have with reproducible identification of nuclei from the lower urinary tract. Application of RBF offers good classification at the nuclear and patient level and promises to become a powerful tool for everyday practice in the cytologic laboratory.

Algorithms↗

PubMed2004

Application of discriminant analysis and quantitative cytologic examination to gastric lesions.

OBJECTIVE: To investigate of the potential value of morphometry and discriminant analysis for the classification of benign and malignant gastric cells and lesions. STUDY DESIGN: The data set consisted of 13,300 cells from 120 cases composed of 30 cases of cancer, 26 cases of gastritis and 64 cases of ulcer according to the final histologic diagnosis. The cytologic diagnosis was divided into 5 categories (gastritis, ulcer, inflammatory dysplasia, cancer and true dysplasia). Classification was attempted at 2 levels: the cell level to classify individual cells and the case level to classify individual cases. For the cellular classification the measured cells from 50% of available cases were selected as a training set to construct a model. The cells from the remaining cases were used as a test set to validate the model. Similarly for case classification, the same 50% of cases that were used for cell classification were used as a training set and the remaining cases as a test set. Images of routinely processed gastric smears stained by the Papanicolaou technique were analyzed by a customized image analysis system. RESULTS: Application of discriminant analysis on the test set gave correct classification of 98.4% of benign cells and 67.1% of malignant cells. On case classification, 100% accuracy was achieved for benign and malignant cases, both for the training and test sets. CONCLUSION: The application of discriminant analysis described in this paper could produce significant classification results at the cellular and individual case level.

Cell Size↗

PubMed2002

Potential of the learning vector quantizer in the cell classification of endometrial lesions in postmenopausal women.

OBJECTIVE: To investigate the potential of artificial neural networks for cell identification in endometrial lesions from postmenopausal women. STUDY DESIGN: The study was performed on cytologic material obtained by the Gynoscann endometrial cell samplerfrom 12 cases of atrophic endometrium, 48 cases of hyperplasia without cytologic atypia (18 cases of simple hyperplasia and 30 cases of complex hyperplasia), 12 cases of hyperplasia with cytologic atypia (complex atypical hyperplasia) and 48 cases of adenocarcinoma (30 cases of well-differentiated, 12 cases of moderately differentiated and 6 cases of poorly differentiated carcinoma). From each case approximately 100 cells were examined using a custom image analysis system. A learning vector quantizer (LVQ) identified the collected data. RESULTS: Investigation of cells from Endometrial Alterations with LVQ proved that according to the nuclear characteristics, as expressed by morphometric and textural measures, the endometrial cells from postmenopausal women may be identified as belonging to one of thefollowing three groups: atrophy, hyperplasia without cytologic atypia (simple and complex hyperplasia) and malignant neoplastic lesions (atypical complex and adenocarcinoma). CONCLUSION: The role of nuclear morphologic features in the cytologic diagnosis of endometrial alterations was confirmed. The overlap in thefeature space observed indicates that cell characteristics do not form strictly separate clusters. Thatfact explains the difficulty that morphologists have with the reproducible identification of cells from endometrial lesions in postmenopausal women. Application of LVQ offers a good classification at the cell level and promises to be a powerful toolfor classification on the individual patient level andfor the clarification of the natural history of endometrial pathology.

Adenocarcinoma↗