Papers by Dimitris Iakovidis
Measurement Science and Technology, 2021
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide ran... more Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
Applied Intelligence, 2007
This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accu... more This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.
Software for Enhanced Video Capsule Endoscopy: State of the Art and Challenges for Essential Progress
Nature Reviews Gastroenterology & Hepatology, Feb 2015
Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bow... more Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear—parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2010
This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vect... more This paper presents a novel method for unsupervised DNA microarray gridding based on Support Vector Machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2010
The screening of the small intestine has become painless and easy with wireless capsule endoscopy... more The screening of the small intestine has become painless and easy with wireless capsule endoscopy (WCE) that is a revolutionary, relatively non-invasive imaging technique performed by a wireless swallowable endoscopic capsule transmitting thousands of video frames per examination. The average time required for the visual inspection of a full 8-h WCE video ranges from 45 to 120 min, depending on the experience of the examiner. In this paper, we propose a novel approach to WCE reading time reduction by unsupervised mining of video frames. The proposed methodology is based on a data reduction algorithm which is applied according to a novel scheme for the extraction of representative video frames from a full length WCE video. It can be used either as a video summarization or as a video bookmarking tool, providing the comparative advantage of being general, unbounded by the finiteness of a training set. The number of frames extracted is controlled by a parameter that can be tuned automatically. Comprehensive experiments on real WCE videos indicate that a significant reduction in the reading times is feasible. In the case of the WCE videos used this reduction reached 85% without any loss of abnormalities.
ND: a thyroid nodule detection system for analysis of ultrasound images and videos
Journal of medical systems, 2012
In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid N... more In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.
IEEE transactions on bio-medical engineering, 2012
We present a novel framework for automatic extraction of the progress of an infection from time-s... more We present a novel framework for automatic extraction of the progress of an infection from time-series medical images, with application to pneumonia monitoring. In each image of a series, the lungs, which are the body components of interest in our study, are detected and delineated by a modified active shape model-based algorithm that is constrained by binary approximation masks. This algorithm offers resistance in the presence of infection manifestations that may distort the typical appearance of the body components of interest. The relative extent of the infection manifestations is assessed by supervised classification of samples acquired from the respective image regions. The samples are represented by multiple dissimilarity features fused according to a novel entropy-based weighted voting scheme offering nonparametric operation and robustness to outliers. The output of the proposed framework is a time series of structured data quantifying the relative extent of infection manifestations at the body components of interest over time. The results obtained indicate an improved performance over relevant state-of-the-art methods. The overall accuracy quantified by the area under receiver operating characteristic reaches 90.0 ± 2.1%. The effectiveness of the proposed framework to pneumonia monitoring, the generality, and the adaptivity of its methods open perspectives for application to other medical imaging domains.
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 2011
Medical decision making can be regarded as a process, combining both analytical cognition and int... more Medical decision making can be regarded as a process, combining both analytical cognition and intuition. It involves reasoning within complex causal models of multiple concepts, usually described by uncertain, imprecise, and/or incomplete information. Aiming to model medical decision making, we propose a novel approach based on cognitive maps and intuitionistic fuzzy logic. The new model, called intuitionistic fuzzy cognitive map (iFCM), extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain. Furthermore, a modification in the formulation of the new model makes it even less sensitive than the original model to missing input data. To validate its effectiveness, an iFCM with 34 concepts representing fuzzy, linguistically expressed patient-specific data, symptoms, and multimodal measurements was constructed for pneumonia severity assessment. The results obtained reveal its comparative advantage over the respective FCM model by providing decisions that match better with the ones made by the experts. The generality of the proposed approach suggests its suitability for a variety of medical decision-making tasks.
This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) i... more This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable background regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model.
In this paper we present a new feature extraction methodology for color texture recognition. It i... more In this paper we present a new feature extraction methodology for color texture recognition. It is based on the covariance of 2 nd -order statistical features in the wavelet domain of the color channels of the images and it is named as Color Wavelet Covariance (CWC). The experimentation showed that the CWC features could be used effectively for texture representation even when illumination varies. The use of the linear K-L (Karhunen-Loeve) transformation of the RGB color space for the extraction of the CWC features resulted in a performance that was comparable to the one achieved with more complex non-linear color transformations. The recognition accuracy tested with texture mosaics reached an average of 86%. Using images acquired under varying illumination the performance of the CWC features on the K-L space reached an average of 88%.
Portable chest radiography is a valuable tool for screening patients hospitalized in intensive ca... more Portable chest radiography is a valuable tool for screening patients hospitalized in intensive care, providing visual cues for diagnosis and physiological measurements. However, its practicality comes at the cost of quality, which is mainly affected by misaligned body positioning, thus increasing x-ray misinterpretation rates. This paper presents a novel methodology for the detection of the lung field boundaries in portable chest radiographs of patients with bacterial pulmonary infections. Such infections are radiographically manifested as foci of consolidations which can lead to vague or invisible lung field boundaries, difficult to distinguish even by experienced physicians. Conventional and state-of-the-art approaches address mainly stationary radiographs, whereas only a few of them cope with pulmonary infections. The proposed methodology is based on an active shape model incorporating shape prior information about the lung fields. The model is initialized by a novel technique utilizing a set of salient points detected on the peripheral anatomic structures of the lungs. A selective thresholding algorithm based on a spinal cord sampling process supports both the initialization and the evolution of the model for the detection of the lung field boundaries. The experiments show that the proposed methodology outperforms state-of-the-art approaches.
FCMs are appropriate to explicitly encode the knowledge and experience accumulated on the operati... more FCMs are appropriate to explicitly encode the knowledge and experience accumulated on the operation of a complex system. Once constructed for a particular domain, an FCM allows a qualitative simulation of the system. In this paper, we investigate a first approach to introduce intuitionistic fuzzy logic into the construction process of FCMs for improved medical decision making. The theory of intuitionistic fuzzy sets provides a sound mathematical model suitable for modeling the imprecision that is inherent in real world problems. It is employed to the step where the fuzzy if-then rules are used for the determination of cause-effect relationships assigning linguistic weights among the concepts. The novel intuitionistic FCM proposed in this paper are implemented by introducing a factor of hesitancy into the weights of a standard FCM. This factor provides an additional cue on the cause-effect relationships among concepts. The results from its experimental evaluation on a medical decision making problem that is critical to patient safety, indicate its effectiveness and open perspectives for its general applicability.
Pulmonary radiographs are essential tools to the evaluation and diagnosis of suspected infections... more Pulmonary radiographs are essential tools to the evaluation and diagnosis of suspected infections of the lower respiratory system. Interpretation of a radiograph in the clinical context is a valuable diagnostic adjunct to the selection and the management of a specific clinical protocol for therapy. The key element in the proper diagnosis of a bacterial pulmonary infection is the analysis of the radiographic data accumulated over time. A dynamic consultation system that captures the progress of a disease over time can prove a valuable means to patients' monitoring and follow-up. The aim of this work is to provide an initial framework which can be used to describe the progress of a bacterial pulmonary infection based on the spatial variation of its radiographic manifestation in temporal image sequences. This is realized by the unsupervised discrimination of inflammatory areas from normal lung parenchyma in chest radiographs and their quantitative evaluation over time. Inflammatory areas, which are visually discriminated by their relative opacity within the lung fields, are identified by using an hierarchical cluster merging scheme based on successive non-negative matrix factorizations (NMF) of radiographic patterns of intensity and texture. The experimentation results validate the effectiveness of the proposed methodology along with its advantage over standard supervised methodologies where the need for feature normalization between the diverse images is prevalent.
Pulmonary infiltrates are common radiological findings indicating the filling of airspaces with f... more Pulmonary infiltrates are common radiological findings indicating the filling of airspaces with fluid, inflammatory exudates, or cells. They are most common in cases of pneumonia, acute respiratory syndrome, atelectasis, pulmonary oedema and haemorrhage, whereas their extent is usually correlated with the extent or the severity of the underlying disease. In this paper we propose a novel pattern recognition framework for the measurement of the extent of pulmonary infiltrates in routine chest radiographs. The proposed framework follows a hierarchical approach to the assessment of image content. It includes the following: (a) sampling of the lung fields; (b) extraction of patient-specific grey-level histogram signatures from each sample; (c) classification of the extracted signatures into classes representing normal lung parenchyma and pulmonary infiltrates; (d) the samples for which the probability of belonging to one of the two classes does not reach an acceptable level are rejected and classified according to their textural content; (e) merging of the classification results of the two classification stages. The proposed framework has been evaluated on real radiographic images with pulmonary infiltrates caused by bacterial infections. The results show that accurate measurements of the infiltration areas can be obtained with respect to each lung field area. The average measurement error rate on the considered dataset reached 9.7% ± 1.0%.
This paper investigates a novel computational approach to thyroid tissue characterization in ultr... more This paper investigates a novel computational approach to thyroid tissue characterization in ultrasound images. It is based on the hypothesis that tissues in thyroid ultrasound images may be differentiated by directionality patterns. These patterns may not be always distinguishable by the human eye because of the dominant image noise. The encoding of the directional patterns in the thyroid ultrasound images is realized by means of Radon Transform features. A representative set of ultrasound images, acquired from 66 patients was constructed to perform experiments that test the validity of the initial hypothesis. Supervised classification experiments showed that the proposed approach is capable of discriminating normal and nodular thyroid tissues, whereas nodular tissues can be further characterized as of high or low malignancy risk.
The use of active contours for texture segmentation seems rather attractive in the recent researc... more The use of active contours for texture segmentation seems rather attractive in the recent research, indicating that such methodologies may provide more accurate results. In this paper, a novel model for texture segmentation is presented, combining advantages of the active contour approach with texture information acquired by the Local Binary Pattern (LBP) distribution. The proposed LBP scheme has been formulated in order to capture regional information extracted from distributions of LBP values, characterizing a neighborhood around each pixel, instead of using a single LBP value to characterize each pixel. The log-likelihood statistic is employed as a similarity measure between the LBP distributions, resulting to more detailed and accurate segmentation of texture images.
The use of active contours for texture segmentation seems rather attractive in the recent researc... more The use of active contours for texture segmentation seems rather attractive in the recent research, indicating that such methodologies may provide more accurate results. In this paper, a novel model for texture segmentation is presented, combining advantages of the active contour approach with texture information acquired by the Local Binary Pattern (LBP) distribution. The proposed LBP scheme has been formulated in order to capture regional information extracted from distributions of LBP values, characterizing a neighborhood around each pixel, instead of using a single LBP value to characterize each pixel. The log-likelihood statistic is employed as a similarity measure between the LBP distributions, resulting to more detailed and accurate segmentation of texture images.
A few approaches have been presented in the literature towards the discrimination of texture in m... more A few approaches have been presented in the literature towards the discrimination of texture in medical images. Medical experts proposed that the more valuable information for discriminating among normal and suspicious cancer regions in endoscopic images is the texture of the examined tissue. Texture can be encoded by a number of mathematical descriptors. Three well-known textural descriptors, as well as a new wavelet-based one are used in this paper for an accurate study and evaluation of the methodologies encountered. Experiments conducted include tests with various images from the Brodatz album, as well as interpretation of tissue regions in endoscopic image. In all cases the recognition task is supported by multilayer perceptron type neural network architectures
A search for tt resonances in lepton+jets events with highly boosted top quarks collected in pp c... more A search for tt resonances in lepton+jets events with highly boosted top quarks collected in pp collisions at √ s = 7 TeV with the ATLAS detector
Capsule endoscopy is a non-invasive imaging technique commonly used for screening of the entire s... more Capsule endoscopy is a non-invasive imaging technique commonly used for screening of the entire small intestine. It is performed by a wireless swallowable endoscopic capsule capable of transmitting thousands of video frames per examination. The visual inspection of the vast amount of images acquired during such an examination is a subjective and highly time consuming task even for experienced gastroenterologists. In this paper we propose a novel approach to the reduction of the number of the video frames to be inspected so as to enable faster inspection of the endoscopic video. It is based on symmetric non-negative matrix factorisation initialised by the fuzzy c-means algorithm and it is supported by non-negative Lagrangian relaxation to extract a subset of video frames containing the most representative scenes from a whole endoscopic examination. The experimental evaluation of the proposed approach was tested on annotated endoscopic videos with frames displaying ulcers, bleedings and normal tissues from various sites in the small intestine. The results demonstrate that the video summary produced consists of representative frames from all the abnormal findings and the normal tissues of the input video.
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Papers by Dimitris Iakovidis