Browsing by Author "Kayaalti, Omer"

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Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi
(IEEE, 2015) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, Eser; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 02.05. Elektrik & Elektronik Mühendisliği
Image fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.
Citation - WoS: 16
Citation - Scopus: 17
Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC
(Springer, 2018) Karacavus, Seyhan; Yilmaz, Bulent; Tasdemir, Arzu; Kayaalti, Omer; Kaya, Eser; Icer, Semra; Ayyildiz, Oguzhan; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 02.05. Elektrik & Elektronik Mühendisliği
We investigated the association between the textural features obtained from F-18-FDG images, metabolic parameters (SUVmax(,) SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.
Citation - Scopus: 1
Comparison of Lung Tumor Segmentation Methods on PET Images
(IEEE, 2015) Eset, Kubra; Icer, Semra; Karacavus, Seyhan; Yilmaz, Bulent; Kayaalti, Omer; Ayyildiz, Oguzhan; Kaya, Eser; 01. Abdullah Gül University; 02. Mühendislik Fakültesi; 02.05. Elektrik & Elektronik Mühendisliği
Lung cancer is the most common cause of cancer-related deaths that occur all over the world. Recently, various image processing approaches have been used on PET images in order to characterize the uniformity, density, coarseness, roughness, and regularity (i.e., texture properties) of the intratumoral F-18-fluorodeoxyglucose (FDG) uptake. The first and important step of this kind of analysis is to differentiate tumor region from other structures and background, which is called segmentation. In this study, k-means, active contour (snake), and Otsu's tresholding methods were applied on PET images obtained from 36 patients and the performances were compared by the nuclear medicine expert in our team. The results show that Otsu tresholding approach is more selective.
Computer-Aided Classification of Breast Cancer Histopathological Images
(IEEE345 E 47TH ST, NEW YORK, NY 10017 USA, 2017) Aksebzeci, Bekir Hakan; Kayaalti, Omer; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü; 01. Abdullah Gül University
Nowadays, one of the most common types of cancer is breast cancer. The early and accurate diagnosis of breast cancer has great importance in the treatment of the disease. In the diagnosis of breast cancer, histopathological analysis of cell and tissue specimens taken by biopsy is considered as the gold standard. Histopathological analysis is a tedious process that is highly dependent on the knowledge and experience of the pathologists. In this study; it is aimed to develop a computer-aided system that can reduce the workload of pathologists and help them in their diagnosis. An image set containing benign and malignant tumor images of breast cancer has been studied. To perform texture analysis on tumor images; first order statistics, Gabor and gray-level co-occurrence matrix (GLCM) feature extraction methods have been applied. Then, various classifiers were applied to the obtained feature matrices and their performances were compared. The highest classification accuracy was achieved 82.06% by Random Forests classifier with feature combination of Gabor and GLCM methods. The results presented here show that computer-assisted diagnosis of breast cancer is a promising field.
A Decision Support System for the Prediction of Mortality in Patients With Acute Kidney Injury Admitted in Intensive Care Unit
(Univ South Bohemia, 2020) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 01. Abdullah Gül University
Intensive care unit (ICU) is a very special unit of a hospital, where healthcare professionals provide treatment and, later, close followup to the patients. It is crucial to estimate mortality in ICU patients from many viewpoints. The purpose of this study is to classify the status of patients with acute kidney injury (AKI) in ICU as early mortality, late mortality, and survival by the application of Classification and Regression Trees (CART) algorithm to the patients' attributes such as blood urea nitrogen, creatinine, serum and urine neutrophil gelatinase-associated lipocalin (NGAL), alkaline phosphatase, lactate dehydrogenase (LDH), gamma-glutamyl transferase, laboratory electrolytes, blood gas, mean arterial pressure, central venous pressure and demographic details of patients. This study was conducted 50 patients with AKI who were followed up in the ICU. The study also aims to determine the significance of relationship between the attributes used in the prediction of mortality in CART and patients' status by employing the Kruskal-Wallis H test. The classification accuracy, sensitivity, and specificity of CART for the tested attributes for the prediction of early mortality, late mortality, and survival of patients were 90.00%, 83.33%, and 91.67%, respectively. The values of both urine NGAL and LDH on day 7 showed a considerable difference according to the patients' status after being examined by the Kruskal-Wallis H test.
Citation - WoS: 28
Citation - Scopus: 31
Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing
(Elsevier, 2014) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa Hakan; 01. Abdullah Gül University
Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.
Prognostic Significance of the Texture Features Determined Using Three Dimensional 18F-FDG PET Images: New Potential Biomarkers
(Soc Nuclear Medicine inc, 2016) Karacavus, Seyhan; Yilmaz, Bulent; Kayaalti, Omer; Tasdemir, Arzu; Kaya, Eser; Icer, Semra; Asyali, Musa; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yilmaz, Bulent; Ayyildiz, Oguzhan; 01. Abdullah Gül University
Citation - WoS: 4
Relationship Between Neutrophil Gelatinase-Associated Lipocalin and Mortality in Acute Kidney Injury
(Galenos Yayincilik, 2018) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 01. Abdullah Gül University
Objective: Almost half of intensive care patients are affected by acute kidney injury (AKI). The purpose of this study is to determine parameters that can be used for predicting of early (within 28 days) and late (within 90 days) mortality in patients who are followed-up with AKI in intensive care units. Materials and Methods: In this study, a dataset that contains 50 patients with AKI in intensive care units was used. This dataset contains blood urea nitrogen, creatinine, plasma and urinary neutrophil gelatinase-associated hpocalin (NGAL), lactate dehydrogenase, alkaline phosphatase and gammaglutamyl transpeptidase values of patients who were admitted to intensive care for various reasons and who developed AKI on the days 1, 3 and 7. In addition to these values, laboratory results such as serum electrolytes on day 1, blood gas; vital signs such as mean arterial pressure, central venous pressure; and demographic data were also recorded. Data mining techniques were applied to determine correlation between all of these data and mortality. Results: The threshold level of urinary NGAL on day 7 was determined to be 69 ng/mL, and strong correlation was found between this threshold level and early mortality. Similarly, the threshold level of plasma NGAL on day 7 was determined to be 150 ng/mL, and this was highly correlated with early mortality. Besides, strong correlation was also found between the difference in the urinary NGAL levels on day 1 and 7, and early mortality. Conclusion: In this study, plasma and urinary NGAL levels were found to be closely related to early mortality in patients who were followed-up with AKI in intensive care units. On the other hand, any parameter associated with late mortality was not found.