Browsing by Subject "Image processing"

Now showing 1 - 20 of 74

Open Access
2-D adaptive prediction based Gaussianity tests in microcalcification detection
(SPIE, 1998-01) Gürcan, M. Nafi; Yardımcı, Yasemin; Çetin, A. Enis
With increasing use of Picture Archiving and Communication Systems (PACS), Computer-aided Diagnosis (CAD) methods will be more widely utilized. In this paper, we develop a CAD method for the detection of microcalcification clusters in mammograms, which are an early sign of breast cancer. The method we propose makes use of two-dimensional (2-D) adaptive filtering and a Gaussianity test recently developed by Ojeda et al. for causal invertible time series. The first step of this test is adaptive linear prediction. It is assumed that the prediction error sequence has a Gaussian distribution as the mammogram images do not contain sharp edges. Since microcalcifications appear as isolated bright spots, the prediction error sequence contains large outliers around microcalcification locations. The second step of the algorithm is the computation of a test statistic from the prediction error values to determine whether the samples are from a Gaussian distribution. The Gaussianity test is applied over small, overlapping square regions. The regions, in which the Gaussianity test fails, are marked as suspicious regions. Experimental results obtained from a mammogram database are presented.
Open Access
2-D triangular mesh-based mosaicking for object tracking in the presence of occlusion
(SPIE, 1997) Toklu, C.; Tekalp, A. M.; Erdem, A. Tanju
In this paper, we describe a method for temporal tracking of video objects in video clips. We employ a 2D triangular mesh to represent each video object, which allows us to describe the motion of the object by the displacements of the node points of the mesh, and to describe any intensity variations by the contrast and brightness parameters estimated for each node point. Using the temporal history of the node point locations, we continue tracking the nodes of the 2D mesh even when they become invisible because of self-occlusion or occlusion by another object. Uncovered parts of the object in the subsequent frames of the sequence are detected by means of an active contour which contains a novel shape preserving energy term. The proposed shape preserving energy term is found to be successful in tracking the boundary of an object in video sequences with complex backgrounds. By adding new nodes or updating the 2D triangular mesh we incrementally append the uncovered parts of the object detected during the tracking process to the one of the objects to generate a static mosaic of the object. Also, by texture mapping the covered pixels into the current frame of the video clip we can generate a dynamic mosaic of the object. The proposed mosaicing technique is more general than those reported in the literature because it allows for local motion and out-of-plane rotations of the object that results in self-occlusions. Experimental results demonstrate the successful tracking of the objects with deformable boundaries in the presence of occlusion.
Open Access
Accelerated phase-cycled SSFP imaging with compressed sensing
(Institute of Electrical and Electronics Engineers Inc., 2015) Çukur, T.
Balanced steady-state free precession (SSFP) imaging suffers from irrecoverable signal losses, known as banding artifacts, in regions of large B0 field inhomogeneity. A common solution is to acquire multiple phase-cycled images each with a different frequency sensitivity, such that the location of banding artifacts are shifted in space. These images are then combined to alleviate signal loss across the entire field-of-view. Although high levels of artifact suppression are viable using a large number of images, this is a time costly process that limits clinical utility. Here, we propose to accelerate individual acquisitions such that the overall scan time is equal to that of a single SSFP acquisition. Aliasing artifacts and noise are minimized by using a variable-density random sampling pattern in k-space, and by generating disjoint sampling patterns for separate acquisitions. A sparsity-enforcing method is then used for image reconstruction. Demonstrations on realistic brain phantom images, and in vivo brain and knee images are provided. In all cases, the proposed technique enables robust SSFP imaging in the presence of field inhomogeneities without prolonging scan times. © 2014 IEEE.
Open Access
Adaptive grids: an image-based approach to generate navigation meshes
(S P I E - International Society for Optical Engineering, 2013-02-01) Akaydın, A.; Güdükbay, U.
We propose adaptive grids, an image-based approach for constructing navigation meshes, which are used for path planning. A cellular navigation mesh, called an adaptive grid, is constructed from a top-view range image of a three-dimensional urban model. A navigation graph can then be extracted from this adaptive grid for path planning. We compare our approach with two popular navigation mesh-generation approaches and obtain promising results in terms of path accuracy and memory cost.
Open Access
Analog CMOS implementation of cellular neural networks
(IEEE, 1993) Baktır, I. A.; Tan, M. A.
The analog CMOS circuit realization of cellular neural networks with transconductance elements is presented. This realization can be easily adapted to various types of applications in image processing just by choosing the appropriate transconductance parameters according to the predetermined coefficients. The effectiveness of the designed circuits for connected component detection is shown by HSPICE simulations. For “fixed function” cellular neural network circuits the number of transistors are reduced further by using multi-input transconductance elements.
Open Access
Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19
(2023-10) Terzi, Merve Begüm
Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.
Open Access
Autofocused spotlight SAR image reconstruction of off-grid sparse scenes
(Institute of Electrical and Electronics Engineers Inc., 2017) Camlıca, S.; Gurbuz, A. C.; Arıkan, Orhan
Synthetic aperture radar (SAR) has significant role in remote sensing. Phase errors due to uncompensated platform motion, measurement model mismatch, and measurement noise can cause degradations in SAR image reconstruction. For efficient processing of the measurements, image plane is discretized and autofocusing algorithms on this discrete grid are employed. However, in addition to the platform motion errors, the reflectors, which are not exactly on the reconstruction grid, also degrade the image quality. This is called the off-grid target problem. In this paper, a sparsity-based technique is developed for autofocused spotlight SAR image reconstruction that can correct phase errors due to uncompensated platform motion and provide robust images in the presence of off-grid targets. The proposed orthogonal matching pursuit-based reconstruction technique uses gradient descent parameter updates with built in autofocus. The technique can reconstruct high-quality images by using sub Nyquist rate of sampling on the reflected signals at the receiver. The results obtained using both simulated and real SAR system data show that the proposed technique provides higher quality reconstructions over alternative techniques in terms of commonly used performance metrics.
Open Access
An automated system for design-rule-based visual inspection of printed circuit boards
(IEEE, 1991) Oğuz, Seyfullah Halit; Onural, Levent
The design and the implementation of an automated, design-rule-based, visual printed circuit board (PCB) inspection system are presented. The system employs mathematical-morphology-based image processing algorithms. This system detects PCB defects related to the conducting structures on PCBs by checking a set of geometric design rules. For this purpose, an image segmentation algorithm and a defect detection algorithm are designed. The defect detection algorithm is capable of verifying the minimum conductor spacing, minimum conductor trace width, and the minimum land width requirements on digital binary PCB images. Also, an existing defect detection algorithm is modified for its implementation in the system.
Open Access
Automatic detection of geospatial objects using multiple hierarchical segmentations
(Institute of Electrical and Electronics Engineers, 2008-07) Akçay, H. G.; Aksoy, S.
The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. © 2008 IEEE.
Open Access
Automatic image captioning
(2004) Pan J.-Y.; Yang H.-J.; Duygulu, Pınar; Faloutsos, C.
In this paper, we examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.
Open Access
Block wavelet transforms for image coding
(IEEE, 1993) Çetin, A. Enis; Gerek, Ö. N.; Ulukuş, Ş.
In this paper, a new class of block transforms is presented. These transforms are constructed from subband decomposition filter banks corresponding to regular wavelets. New transforms are compared to the discrete cosine transform (DCT). Image coding schemes that employ the block wavelet transform (BWT) are developed. BWT's can be implemented by fast (O(N log N)) algorithms.
Open Access
BRAPH: A graph theory software for the analysis of brain connectivity
(Public Library of Science, 2017) Mijalkov, M.; Kakaei, E.; Pereira, J. B.; Westman, E.; Volpe, G.
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. © 2017 Mijalkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Open Access
Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. Enis
Cancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.
Open Access
Cepstrum based feature extraction method for fungus detection
(SPIE, 2011) Yorulmaz, Onur; Pearson, T.C.; Çetin, A. Enis
In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Open Access
A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images
(John Wiley & Sons, Inc., 2014) Arslan, S.; Ozyurek, E.; Gunduz Demir, C.
Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. © 2014 International Society for Advancement of Cytometry.
Open Access
Compressive sensing based flame detection in infrared videos
(IEEE, 2013) Günay, Osman; Çetin, A. Enis
In this paper, a Compressive Sensing based feature extraction algorithm is proposed for flame detection using infrared cameras. First, bright and moving regions in videos are detected. Then the videos are divided into spatio-temporal blocks and spatial and temporal feature vectors are exctracted from these blocks. Compressive Sensing is used to exctract spatial feature vectors. Compressed measurements are obtained by multiplying the pixels in the block with the sensing matrix. A new method is also developed to generate the sensing matrix. A random vector generated according to standard Gaussian distribution is passed through a wavelet transform and the resulting matrix is used as the sensing matrix. Temporal features are obtained from the vector that is formed from the difference of mean intensity values of the frames in two neighboring blocks. Spatial feature vectors are classified using Adaboost. Temporal feature vectors are classified using hidden Markov models. To reduce the computational cost only moving and bright regions are classified and classification is performed at specified intervals instead of every frame. © 2013 IEEE.
Open Access
Computer vision based forest fire detection
(IEEE, 2008) Töreyin, B. Uğur; Çetin, A. Enis
Lookout posts are commonly installed in the forests all around Turkey and the world. Most of these posts have electricity. Surveillance cameras can be placed on to these surveillance towers to detect possible forest fires. Currently, average fire detection time is 5 minutes in manned lookout towers. The aim ofthe proposed computer vision based method is to reduce the average fire detection rate. The detection method is based on the wavelet based analysis of the background images at various update rates.
Open Access
Computer vision based text and equation editor for LATEX
(IEEE, 2004-06) Öksüz, Özcan; Güdükbay, Uğur; Çetin, Enis
In this paper, we present a computer vision based text and equation editor for LATEX. The user writes text and equations on paper and a camera attached to a computer records actions of the user. In particular, positions of the pen-tip in consecutive image frames are detected. Next, directional and positional information about characters are calculated using these positions. Then, this information is used for on-line character classification. After characters and symbols are found, corresponding LATEX code is generated.
Open Access
Continuous processing of images through user sketched functional blocks
(Wiley-Blackwell, 1988) Kaya, A.; Ozguc, B.
Our aim is to implement an image processing system with previously defined algorithms through the use of functional blocks connected together to form a diagram that we call a schematic. A functional block means the representation of a routine or function by a visual object in our intent. The arc connecting two blocks is the path and direction through which an image is transferred. In the development of the system we relied on the usage of new developments in software providing for the construction of better interfaces. Most important of these are the object oriented style of programming, iconic interfacing, multiple window and multi-tasking operating systems.
Open Access
Current constrained voltage scaled reconstruction (CCVSR) algorithm for MR-EIT and its performance with different probing current patterns
(Institute of Physics Publishing, 2003) Birgül, Ö.; Eyüboğlu, B. M.; İder, Y. Z.
Conventional injected-current electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) techniques can be combined to reconstruct high resolution true conductivity images. The magnetic flux density distribution generated by the internal current density distribution is extracted from MR phase images. This information is used to form a fine detailed conductivity image using an Ohm's law based update equation. The reconstructed conductivity image is assumed to differ from the true image by a scale factor. EIT surface potential measurements are then used to scale the reconstructed image in order to find the true conductivity values. This process is iterated until a stopping criterion is met. Several simulations are carried out for opposite and cosine current injection patterns to select the best current injection pattern for a 2D thorax model. The contrast resolution and accuracy of the proposed algorithm are also studied. In all simulation studies, realistic noise models for voltage and magnetic flux density measurements are used. It is shown that, in contrast to the conventional EIT techniques, the proposed method has the capability of reconstructing conductivity images with uniform and high spatial resolution. The spatial resolution is limited by the larger element size of the finite element mesh and twice the magnetic resonance image pixel size.