Browsing by Author "Kayaalti, Omer"

Now showing 1 - 6 of 6

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; 0000-0002-8473-9720; 0000-0003-2954-1217; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yilmaz, Bulent; Ayyildiz, Oguzhan
Akciğer kanseri, tüm dünyada kansere bağlı gerçekleşen ölümlerin en sık nedenidir. Son zamanlarda, tümör içi 18Fflorodeoksiglukoz (FDG)’un tutulumunun düzgünlük, pürüzlülük ve düzenliliğini (yani tekstür özelliklerini) tanımlamak için PET görüntüleri üzerinde çeşitli görüntü işleme yaklaşımları kullanılmaktadır. Bunun ilk ve önemli aşaması tümörlü bölgenin diğer bölgelerden başarıyla ayrıştırılması, yani segmentasyonudur. Bu çalışmada, 36 hastadan alınan tek veya çok kesit görüntüler üzerinde kortalamalar, aktif kontur (yılan), Otsu eşikleme yaklaşımlarını kullanarak elde edilmiş alan ve hacimlerin ekibimizdeki nükleer tıp uzmanı tarafından değerlendirmesiyle karşılaştırması yapılmıştır. Sonuç olarak, Otsu eşikleme algoritmasının daha seçici davrandığı gözlenmiştir
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
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, FAC HEALTH & SOCIAL STUD, JIROVCOVA, CESKA BUDEJOVICE, 370 04, CZECH REPUBLIC, 2020) Kayaalti, Selda; Kayaalti, Omer; Aksebzeci, Bekir Hakan; 0000-0001-6711-2363; 0000-0002-1630-1241; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü
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.
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; Kara, Sadik; Asyali, Musa Hakan; 0000-0001-7476-8141; 0000-0003-2954-1217; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yilmaz, Bulent; Aksebzeci, Bekir Hakan
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.
Prognostic significance of the texture features determined using three dimensional 18F-FDG PET images: new potential biomarkers
(SOC NUCLEAR MEDICINE INC1850 SAMUEL MORSE DR, RESTON, VA 20190-5316, 2016) Karacavus, Seyhan; Yilmaz, Bulent; Kayaalti, Omer; Tasdemir, Arzu; Kaya, Eser; Icer, Semra; Ayyildiz, Oguzhan; Eset, Kubra; Vardareli, Erkan; Asyali, Musa; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yilmaz, Bulent; Ayyildiz, Oguzhan
Prognostic significance of the texture features determined using three dimensional 18F-FDG PET images: new potential biomarkers
Registration and Fusion of Lung Tumor PET/CT Images
(IEEE, 2015) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, Eser; 0000-0003-2954-1217; 0000-0002-8473-9720; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Ayyildiz, Oguzhan; Yilmaz, Bulent
Görüntü birleştirme medikal alanda tamamlayıcı yönüyle ve teşhis ve tedavi planlama gibi uygulamalarda kullanılmasıyla dikkat çekmektedir. Bu çalışmada 8 adet küçük hücre dışı akciğer kanserli hastanın positron emisyon tomografi (PET) ve bilgisayarlı tomografi (BT) görüntüleri önce çakıştırılmış sonra dalgacık ve temel bileşen analizi metotlarıyla birleştirilmiştir. Karşılıklı bilgi ve eğitimli gözle kıyaslama sonucunda dalgacık daha başarılı bulunmuştur.