Marina E. Plissiti | University of Ioannina/Greece (original) (raw)

Papers by Marina E. Plissiti

The British Journal of Radiology, 2005

The aim of this work was to evaluate a new semi-automated intravascular ultrasound (IVUS) border ... more The aim of this work was to evaluate a new semi-automated intravascular ultrasound (IVUS) border detection method. The method was used to identify the lumen and the external elastic membrane or the borders of stents in 80 IVUS images, randomly selected from 10 consecutive human coronary arteries. These semi-automated results were compared with observations of two experts. Several indices in each case were obtained in order fully to evaluate the method. The time required for identification of the borders was also recorded. The interobserver variability of the method ranged from 1.21% to 5.61%, the correlation coefficient from 0.98 to 0.99, the slope was close to unity (0.94-1.03), the y intercept close to zero and the Williams index value was close to unity (range 0.67-0.91). The time (mean+/-SD) required for the method to identify the borders of the different vessel layers for the whole IVUS sequence was 5.2+/-0.2 min. The results demonstrate that the method is reliable and capable of identifying rapidly and accurately the different vessel layers depicted in IVUS images.

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this work, we solve the problem of motion representation in videos, according to local transfo... more In this work, we solve the problem of motion representation in videos, according to local transformations applied to specific keypoints extracted from static the images. First, we compute the coordinates of the keypoints of the body or face through a pre-trained model, and then we introduce a convolutional neural network to estimate a dense motion field through optical flow. Next, we train a generative adversarial network that exploits the previous information to generate new images that resemble as much as possible the target frames. To reduce trembling and extract smooth movements, our model incorporates a low-pass spatio-temporal Gaussian filter. Results indicate that our method provides high performance and the movement of objects is accurate and robust.

2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015

The automated interpretation of Pap smear images is a challenging issue with several aspects. The... more The automated interpretation of Pap smear images is a challenging issue with several aspects. The accurate segmentation of the structuring elements of each cell is a crucial procedure which entails in the correct identification of pathological situations. However, the extended cell overlapping in Pap smear slides complicates the automated analysis of these cytological images. In this work, we propose an efficient algorithm for the separation of the cytoplasm area of overlapping cells. The proposed method is based on the fact that in isolated cells the pixels of the cytoplasm exhibit similar features and the cytoplasm area is homogeneous. Thus, the observation of intensity changes in extended subareas of the cytoplasm indicates the existence of overlapping cells. In the first step of the proposed method, the image is tesselated into perceptually meaningful individual regions using a superpixel algorithm. In a second step, these areas are merged into regions exhibiting the same characteristics, resulting in the identification of each cytoplasm area and the corresponding nuclei. The area of overlap is then detected using an algorithm that specifies faint changes in the intensity of the cytoplasm of each cell. The method has been evaluated on cytological images of conventional Pap smears, and the results are very promising.

2011 17th International Conference on Digital Signal Processing (DSP), 2011

In this work, we present an efficient framework for the training of active shape models (ASM), re... more In this work, we present an efficient framework for the training of active shape models (ASM), representing smooth shapes, which is based on the representation of a shape by the vibrations of a spring-mass system. A deformable model whose behavior is driven by physical principles is used on a training set of shapes. The boundary of the regions of interest of the elements of the training set is detected with the convergence of the physics-based deformable model and attributes of the shapes of interest are expressed in terms of modal analysis. Based on the estimated modal distribution, we develop a framework, similar in spirit to ASM, to detect and describe an unknown new shape. The main difference with respect to standard ASM is that the modal amplitudes of the learnt model are used instead of the 2D landmark points and the cost function to be minimized is accordingly modified. The proposed method is evaluated using cytological images of conventional Pap smears, which contain 44 nuclei of squamous epithelial cells.

Technologies, 2022

Visible light communication (VLC) is an upcoming wireless communication technology. In a VLC syst... more Visible light communication (VLC) is an upcoming wireless communication technology. In a VLC system, signal integrity under low illumination intensity and high transmission frequencies are of great importance. Towards this direction, the performance of the analog front end (AFE) sub-system either at the side of the transmitter or the receiver is crucial. However, little research on the AFE of the receiver is reported in the open literature. Aiming to enhance signal integrity, three pre-amplification topologies for the VLC receiver AFE are presented and compared in this paper. All three use bipolar transistors (BJT): the first consists of a single BJT, the second of a double BJT in cascade connection, and the third of a double BJT in Darlington-like connection. In order to validate the performance characteristics of the three topologies, simulation results are provided with respect to the light illumination intensity, the data transmission frequency and the power consumption. Accordi...

Sensors

The rapid spread of the COVID-19 pandemic, in early 2020, has radically changed the lives of peop... more The rapid spread of the COVID-19 pandemic, in early 2020, has radically changed the lives of people. In our daily routine, the use of a face (surgical) mask is necessary, especially in public places, to prevent the spread of this disease. Furthermore, in crowded indoor areas, the automated recognition of people wearing a mask is a requisite for the assurance of public health. In this direction, image processing techniques, in combination with deep learning, provide effective ways to deal with this problem. However, it is a common phenomenon that well-established datasets containing images of people wearing masks are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present a publicly available annotated image database containing images of people with and without a mask on their faces, in different environments and situations. Moreover, we tested the performance of deep learning detectors in images and videos on this dataset. The ...

2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)

2018 25th IEEE International Conference on Image Processing (ICIP)

Classification of cervical cells in Pap smear images is a challenging task due to the limitations... more Classification of cervical cells in Pap smear images is a challenging task due to the limitations these images exhibit and the complexity of the morphological changes in the structural parts of the cells. This procedure is very important as it provides fundamental information for the detection of cancerous or precancerous lesions. For this reason several algorithms have been proposed in order to classify normal and abnormal cells in such images. However, it is a common phenomenon that each research group usually creates its own dataset of images, as well-established datasets are not publicly available. To overcome this obstacle and to assist the research progress in this field, we present an annotated image database of Pap smear images, in which the cells are categorized in five different classes, depending on their cytomorphological features. The area of the cytoplasm and the nucleus in each image is manually defined by experts and salient features of intensity, texture and shape are calculated for each region of interest. Several experiments have been performed for the classification of these images and they include feature and image based classification schemes. In this direction, methods based on support vector machines and deep neural networks are tested and the performance of each classifier is presented in order to constitute a reference point for the evaluation of future classification techniques.

Ieee Transactions on Information Technology in Biomedicine a Publication of the Ieee Engineering in Medicine and Biology Society, Oct 1, 2010

In this paper, we present a fully automated method for cell nuclei detection in Pap smear images.... more In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application of a distance-dependent rule on the resulted centroids; and the application of classification algorithms. In our method, we have examined the performance of an unsupervised (fuzzy C-means) and a supervised (support vector machines) classification technique. In both classification techniques, the effect of the refinement step improves the performance of the clustering algorithm. The proposed method was evaluated using 38 cytological images of conventional Pap smears containing 5617 recognized squamous epithelial cells. The results are very promising, even in the case of images with high degree of cell overlapping.

Medical Imaging 2014: Image Processing, 2014

In this work, we propose a novel and robust method for the accurate separation of elliptical over... more In this work, we propose a novel and robust method for the accurate separation of elliptical overlapped nuclei in microscopic images. The method is based on both the information provided by the global boundary of the nuclei cluster and the detection of concavity points along this boundary. The number of the nuclei and the area of each nucleus included in the cluster are estimated automatically by exploiting the different parts of the cluster boundary demarcated by the concavity points. More specifically, based on the set of concavity points detected in the image of the clustered nuclei, all the possible configurations of candidate ellipses that fit to them are estimated by least squares fitting. For each configuration, an index measuring the fitting residual is computed and the configuration providing the minimum error is selected. The method may successfully separate multiple (more than two) clustered nuclei as the fitting residual is a robust indicator of the number of overlapping elliptical structures even if many erroneous concavity points are present due to noise. Moreover, the algorithm has been evaluated on cytological images of conventional Pap smears and compares favorably with state of the art methods both in terms of accuracy and execution time.

International Journal of E-Health and Medical Communications, 2015

The automated diagnosis of cervical cancer in Pap smear images is a difficult though extremely im... more The automated diagnosis of cervical cancer in Pap smear images is a difficult though extremely important procedure. In order to obtain reliable diagnostic information, the nuclei and their characteristics must be correctly identified and evaluated. However, the presence of inflammatory and overlapping cells in these images complicates the detection process. In this work, a segmentation algorithm is developed to extract the inflammatory cells and enable accurate nuclei detection. The proposed algorithm is based on the combination of gray level thresholding and the definition of a distance rule, which entails in the identification of inflammatory cells. The results indicate that our method significantly simplifies the nuclei detection process, as it reduces the number of inflammatory cells that may interfere.

In this paper an automated method for cell nucleus segmentation in PAP smear images is presented.... more In this paper an automated method for cell nucleus segmentation in PAP smear images is presented. The method combines the global knowledge about the cells and nuclei appearance and the local characteristics of the area of the nuclei, in order to achieve an accurate nucleus boundary. Filters and morphological operators in all three channels of a color image result in the determination of the locations of nuclei in the image, even in cases where cell overlapping occurs. The nucleus boundary is determined by a deformable model. The results are very promising, even when images with high degree of overlapping are used.

International Congress Series, 2003

We have developed an efficient method for 3D reconstruction of coronary arteries. Our approach is... more We have developed an efficient method for 3D reconstruction of coronary arteries. Our approach is based on the fusion of intravascular ultrasound (IVUS) and biplane angiography. The method includes an efficient algorithm for the automatic identification of the regions of interest in IVUS images and a novel methodology for the extraction of the catheter path from biplane angiographies. The estimation of IVUS frames relative twist and the computation of first IVUS frame absolute orientation is also investigated. To assess the performance of the method, a validation procedure is introduced. Several metrics are obtained to verify the reliability of our method in the description of coronary artery morphology.

In this work, we investigate the classification of cervical cells by exploiting only the nucleus ... more In this work, we investigate the classification of cervical cells by exploiting only the nucleus features and not taking into account the features extracted from the cytoplasm area. This procedure is motivated by the fact that the nuclei areas can be extracted automatically from Pap smear images, in contrast to the cytoplasm segmentation which is not a solved problem yet. Furthermore, we consider the representation of these features in low dimensional spaces using non linear dimensionality reduction techniques, which can ...

Lecture Notes in Computational Vision and Biomechanics, 2012

Cervical smear screening is the most popular method used for the detection of cervical cancer in ... more Cervical smear screening is the most popular method used for the detection of cervical cancer in its early stages. The most eminent screening test is the Pap smear, which is based on the staining of cervical cells, using the technique that was first introduced by George Papanicolaou (Science 1942). With this screening technique, precancerous conditions and abnormal changes in cells that may develop into cancer are recognized. The widespread use of this test in developed countries has significantly reduced the incidence and ...

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012

IEEE Transactions on Image Processing, 2012

A method for the segmentation of overlapping nuclei is presented, which combines local characteri... more A method for the segmentation of overlapping nuclei is presented, which combines local characteristics of the nuclei boundary and a priori knowledge about the expected shape of the nuclei. A deformable model whose behavior is driven by physical principles is trained on images containing a single nuclei, and attributes of the shapes of the nuclei are expressed in terms of modal analysis. Based on the estimated modal distribution and driven by the image characteristics, we develop a framework to detect and describe the unknown nuclei boundaries in images containing two overlapping nuclei. The problem of the estimation of an accurate nucleus boundary in the overlapping areas is successfully addressed with the use of appropriate weight parameters that control the contribution of the image force in the total energy of the deformable model. The proposed method was evaluated using 152 images of conventional Pap smears, each containing two overlapping nuclei. Comparisons with other segmentation methods indicate that our method produces more accurate nuclei boundaries which are closer to the ground truth.

Abstract In this work we present a fully automated method for the accurate detection of cell nucl... more Abstract In this work we present a fully automated method for the accurate detection of cell nuclei boundaries in conventional Pap smear images, based on the watershed transform. For the extraction of nuclei and cytoplasm markers, which are used as starting points for the flooding process, a morphological reconstruction step is initially performed in each image. The watershed transform is then applied in the color morphological gradient image, which shows the boundaries of the more pronounced nuclei.