Konstantinos Koutroumbas

Publications (3)3.67 Total impact

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

  Beatrix Cochand-Priollet, Konstantinos Koutroumbas, Tatiana Mona Megalopoulou, Abraham Pouliakis, Gregory Sivolapenko, Petros Karakitsos
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  ABSTRACT: 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.
  Oncology Reports 02/2006; 15 Spec no.:1023-6. · 1.84 Impact Factor
• Article: The potential of feature selection by statistical techniques and the use of statistical classifiers in the discrimination of benign from malignant gastric lesions.

  Tatiana Mona Megalopoulou, Konstantinos Koutroumbas, Abraham Pouliakis, Gregory Sivolapenko, Petros Karakitsos
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  ABSTRACT: 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.
  Oncology Reports 02/2006; 15 Spec no.:1033-6. · 1.84 Impact Factor
• Conference Proceeding: Discrimination of Benign from Malignant Breast Lesions Using Statistical Classifiers.

  Konstantinos Koutroumbas, Abraham Pouliakis, Tatiana Mona Megalopoulou, John Georgoulakis, Anna-Eva Giachnaki, Petros Karakitsos
  Advances in Artificial Intelligence, 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006, Proceedings; 01/2006