Mammographic Mass Detection Using a Mass Template

Serhat Ozekes,Onur Osman,Onur Osman 대한영상의학회 2005 Korean Journal of Radiology Vol.6 No.4

Objective: The purpose of this study was to develop a new method for automated mass detection in digital mammographic images using templates. Materials and Methods: Masses were detected using a two steps process. First, the pixels in the mammogram images were scanned in 8 directions, and regions of interest (ROI) were identified using various thresholds. Then, a mass template was used to categorize the ROI as true masses or non-masses based on their morphologies. Each pixel of a ROI was scanned with a mass template to determine whether there was a shape (part of a ROI) similar to the mass in the template. The similarity was controlled using two thresholds. If a shape was detected, then the coordinates of the shape were recorded as part of a true mass. To test the system's efficiency, we applied this process to 52 mammogram images from the Mammographic Image Analysis Society (MIAS) database. Results: Three hundred and thirty-two ROI were identified using the ROI specification methods. These ROI were classified using three templates whose diameters were 10, 20 and 30 pixels. The results of this experiment showed that using the templates with these diameters achieved sensitivities of 93%, 90% and 81% with 1.3, 0.7 and 0.33 false positives per image respectively. Conclusion: These results indicate that the detection performance of this template based algorithm is satisfactory, and may improve the performance of computer- aided analysis of mammographic images and early diagnosis of mammographic masses.

Nodule Detection in a Lung Region that’s Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

Serhat Ozekes,Onur Osman,Osman N. Ucan 대한영상의학회 2008 Korean Journal of Radiology Vol.9 No.1

Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computeraided detection of lung nodules. Objective: The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods: Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results: The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion: Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computeraided detection of lung nodules.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

Turker Tekin Erguzel,Serhat Ozekes,Selahattin Gultekin,Nevzat Tarhan,Gokben Hizli Sayar,Ali Bayram 대한신경정신의학회 2015 PSYCHIATRY INVESTIGATION Vol.12 No.1

ObjectiveaaThe combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). MethodsaaThe artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. ResultsaaThe ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. ConclusionaaPotential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

Ant Colony Optimization Based Feature Selection Method for QEEG Data Classification

Turker Tekin Erguzel,Serhat Ozekes,Selahattin Gultekin,Nevzat Tarhan 대한신경정신의학회 2014 PSYCHIATRY INVESTIGATION Vol.11 No.3

Objective-Many applications such as biomedical signals require selecting a subset of the input features in order to represent the whole set of features. A feature selection algorithm has recently been proposed as a new approach for feature subset selection. Methods-Feature selection process using ant colony optimization (ACO) for 6 channel pre-treatment electroencephalogram (EEG) data from theta and delta frequency bands is combined with back propagation neural network (BPNN) classification method for 147 major depressive disorder (MDD) subjects. Results-BPNN classified R subjects with 91.83% overall accuracy and 95.55% subjects detection sensitivity. Area under ROC curve (AUC) value after feature selection increased from 0.8531 to 0.911. The features selected by the optimization algorithm were Fp1, Fp2, F7, F8, F3 for theta frequency band and eliminated 7 features from 12 to 5 feature subset. Conclusion-ACO feature selection algorithm improves the classification accuracy of BPNN. Using other feature selection algorithms or classifiers to compare the performance for each approach is important to underline the validity and versatility of the designed combination.

Arrhythmogenic Noncompaction Cardiomyopathy: Is There an Echocardiographic Phenotypic Overlap of Two Distinct Cardiomyopathies?

Dursun Aras,Ozcan Ozeke,Serkan Cay,Firat Ozcan,Kazım Baser,Umuttan Dogan,Murat Unlu,Burcu Demirkan,Omac Tufekcioglu,Serkan Topaloglu 한국심초음파학회 2015 Journal of Cardiovascular Imaging (J Cardiovasc Im Vol.23 No.3

The clinical diagnosis of right ventricular (RV) cardiomyopathies is often challenging. It is difficult to differentiate the isolatedleft ventricular (LV) noncompaction cardiomyopathy (NC) from biventricular NC or from coexisting arrhythmogenic ventricularcardiomyopathy (AC). There are currently few established morphologic criteria for the diagnosis other than RV dilation andpresence of excessive regional trabeculation. The gross and microscopic changes suggest pathological similarities between, orcoexistence of, RV-NC and AC. Therefore, the term arrhythmogenic right ventricular cardiomyopathy is somewhat misleading asisolated LV or biventricular involvement may be present and thus a broader term such as AC should be preferred. We describe anunusual case of AC associated with a NC in a 27-year-old man who had a history of permanent pacemaker 7 years ago due tosecond-degree atrioventricular block.

Energetic Electron and Proton Interactions with Pc5 Ultra Low Frequency (ULF) Waves during the Great Geomagnetic Storm of 15-16 July 2000

Lee, Eunah,Mann, Ian R.,Ozeke, Louis G. The Korean Space Science Society 2022 Journal of astronomy and space sciences Vol.39 No.4

The dynamics of the outer zone radiation belt has received a lot of attention mainly due to the correlation between the occurrence of enhancing relativistic electron flux and spacecraft operation anomalies or even failures (e.g., Baker et al. 1994). Relativistic electron events are often observed during great storms associated with ultra low frequency (ULF) waves. For example, a large buildup of relativistic electrons was observed during the great storm of March 24, 1991 (e.g., Li et al. 1993; Hudson et al. 1995; Mann et al. 2013). However, the dominant processes which accelerate magnetospheric radiation belt electrons to MeV energies are not well understood. In this paper, we present observations of Pc5 ULF waves in the recovery phase of the Bastille day storm of July 16, 2000 and electron and proton flux simultaneously oscillating with the same frequencies as the waves. The mechanism for the observed electron and proton flux modulations is examined using ground-based and satellite observations. During this storm time, multiple packets of discrete frequency Pc5 ULF waves appeared associated with energetic particle flux oscillations. We model the drift paths of electrons and protons to determine if the particles drift through the ULF wave to understand why some particle fluxes are modulated by the ULF waves and others are not. We also analyze the flux oscillations of electrons and protons as a function of energy to determine if the particle modulations are caused by a ULF wave drift resonance or advection of a particle density gradient. We suggest that the energetic electron and proton modulations by Pc5 ULF waves provide further evidence in support of the important role that ULF waves play in outer radiation belt dyanamics during storm times.

Energetic Electron and Proton Interactions with Pc5 Ultra Low Frequency (ULF) Waves during the Great Geomagnetic Storm of 15–16 July 2000

이은아,Ian R. Mann,Louis G. Ozeke 한국우주과학회 2022 Journal of Astronomy and Space Sciences Vol.39 No.4

The dynamics of the outer zone radiation belt has received a lot of attention mainly due to the correlation between the occurrence of enhancing relativistic electron flux and spacecraft operation anomalies or even failures (e.g., Baker et al. 1994). Relativistic electron events are often observed during great storms associated with ultra low frequency (ULF) waves. For example, a large buildup of relativistic electrons was observed during the great storm of March 24, 1991 (e.g., Li et al. 1993; Hudson et al. 1995; Mann et al. 2013). However, the dominant processes which accelerate magnetospheric radiation belt electrons to MeV energies are not well understood. In this paper, we present observations of Pc5 ULF waves in the recovery phase of the Bastille day storm of July 16, 2000 and electron and proton flux simultaneously oscillating with the same frequencies as the waves. The mechanism for the observed electron and proton flux modulations is examined using groundbased and satellite observations. During this storm time, multiple packets of discrete frequency Pc5 ULF waves appeared associated with energetic particle flux oscillations. We model the drift paths of electrons and protons to determine if the particles drift through the ULF wave to understand why some particle fluxes are modulated by the ULF waves and others are not. We also analyze the flux oscillations of electrons and protons as a function of energy to determine if the particle modulations are caused by a ULF wave drift resonance or advection of a particle density gradient. We suggest that the energetic electron and proton modulations by Pc5 ULF waves provide further evidence in support of the important role that ULF waves play in outer radiation belt dyanamics during storm times.