M. Gletsos

Bio: M. Gletsos is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Feature extraction & Contextual image classification. The author has an hindex of 2, co-authored 3 publications receiving 276 citations.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

Abstract: In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease". The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

Classification of hepatic lesions from CT images using texture features and neural networks

Abstract: In this paper a computer-aided diagnostic system for the classification of hepatic lesions from Computed Tomography (CT) images is presented. Regions of Interest (ROI's) taken from non-enhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas (a total of 147 samples), have been used as input to the system. The system consists of two levels: the feature extraction and the classification levels. The feature extraction level calculates the average grey scale and 48 texture characteristics, which are derived from the spatial grey-level co-occurrence matrices, obtained from the ROI's. The classifier level consists of three sequentially placed feed-forward Neural Networks (NN's), which are activated sequentially. The first NN classifies into normal or pathological liver regions. The pathological liver regions are classified by the second NN into cysts or "other disease". The third NN classifies "other disease" into hemangiomas and hepatocellular carcinomas. In order to enhance the performance of the classifier and improve the execution time, the dimensionality of the initial feature vector has been reduced using the sequential forward floating selection method for each individual NN input vector. A total classification rate of 98% has been achieved.

Innovative Action for Forest Fire Prevention in Kythira Island, Greece, through Mobilization and Cooperation of the Population: Methodology and Challenges

Abstract: The island of Kythira in Greece suffered a major forest fire in 2017 that burned 8.91% of its total area and revealed many challenges regarding fire management. Following that, the Hellenic Society for the Protection of Nature joined forces with the Institute of Mediterranean and Forest Ecosystems in a project aiming to improve fire prevention there through mobilization and cooperation of the population. This paper describes the methodology and the results. The latter include an in-depth analysis of fire statistics for the island, development of a forest fuels map, and prevention planning for selected settlements based on fire modeling and on an assessment of the vulnerability of 610 structures, carried out with the contribution of groups of volunteers. Emphasis was placed on informing locals, including students, through talks and workshops, on how to prevent forest fires and prepare their homes and themselves for such an event, and on mobilizing them to carry out fuel management and forest rehabilitation work. In the final section of the paper, the challenges that the two partners faced and the project achievements and shortcomings are presented and discussed, leading to conclusions that can be useful for similar efforts in other places in Greece and elsewhere.

Computer supported collaborative environment for virtual simulation of radiation treatment planning

Abstract: The collaborative environment architecture is based on both off-line and on-line communication of data under a secure framework and can be directly integrated into the infrastructure of a radiotherapy department. The on-line collaboration is based on the simultaneous execution of all actions at both collaborating sites, and prerequisites the off-line communication of the data set on which the collaboration will be performed. A technical pilot study has been carried out on clinical sites, monitoring the performance of the implementation, which revealed high interactivity during an on-line collaboration session.

Computer and Robot Vision

Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

Abstract: This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Automated medical image segmentation techniques

Abstract: Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

Synthetic data augmentation using GAN for improved liver lesion classification

Abstract: In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.