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Öğe Adrenal Tumor Classification on T1 and T2-weighted Abdominal MR Images(Institute of Electrical and Electronics Engineers Inc., 2019) Barstugan M.; Ceylan R.; Asoglu S.; Cebeci H.; Koplay M.Adrenal tumors occur on adrenal glands and can be malignant. Adrenal glands consist of cortex and medulla. If cortex or medulla produce hormones extremely, the hormonal unbalance situation arises. This situation causes adrenal tumor occurrence on adrenal glands. In this study, adrenal tumors on T1 and T2-weighted MR images were classified by the SVM algorithm. Before the classification stage, different feature extraction algorithms and filtering methods were used for preprocessing. The classification results that were obtained by four different methods were evaluated on five different evaluation metrics as sensitivity, specificity, accuracy, precision, and F-score. The best classification performance was obtained with Method 2 on T1-weighted MR (Magnetic Resonance) images where the sensitivity, specificity, accuracy, precision, and F-score metrics were obtained as 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. © 2019 IEEE.Öğe Automatic liver segmentation in abdomen CT images using SLIC and adaboost algorithms(Association for Computing Machinery, 2018) Barstugan M.; Ceylan R.; Sivri M.; Erdogan H.This study is an implementation of liver segmentation on abdomen CT images. The liver organ was segmented by using SLIC super-pixel and AdaBoost algorithms. Firstly, the images were clustered by SLIC super-pixel algorithm. Then, the liver was segmented by AdaBoost classifier. The segmentation process was done automatically. The automatic segmentation is based on the classification of overlapping patches of the image. The results of automatic segmentation and manual segmentation were compared and the efficiency of the method was observed. The best Dice rate was obtained as 92.13% and the best Jaccard rate was obtained as 85.8% on 16 abdomen CT images. © 2018 Association for Computing Machinery.