Browsing by Author "Arica, Sami"

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On Visualization and Quantification of Lesion Margin in CT Liver Images
(2020) Arica, Sami; Altuntas, Tugce Sena; Erbay, Gurcan; 0000-0002-1706-8680; AAK-5370-2021
Cancer is the one of the leading causes of death worldwide, and cancer incidence increases every year. The analysis of lesion margin is quite important to diagnose malignant and benign masses and to detect the presence and the stage of tumor invasion in case of cancer. Accordingly, the aim of the study is to visualize and quantify margin of lesions on radiological images by means of a digital computer. In this study, computed tomography (CT) images of liver have been employed for analysis because the liver has crucial tasks in our body and liver cancerrelated deaths is ranked as the forth among the cancer-related deaths. The proposed method consisted of four main steps: image cropping and smoothing, specification of target lesion, the boundary detection of target lesion, and visualization and quantification of margin. First, the images were converted to gray scale. The blank regions surrounding the liver in the CT images were removed before specification of target lesion, and further were smoothed with a bilateral filter. Next, the target region was specified roughly by drawing it manually. The boundary of lesion was more precisely determined with the active contour method employing the sketched borderline as the initial curve. Next, the properties of the target region: the centroid, major axis length, and the orientation values were computed. The intensities along a line passing through the center of the tumor were obtained for eighteen different rotation angles. A pulse model was fit to each of the intensity signal corresponding to a rotation. Then, the intensity change, margin sharpness and width were acquired from the pulse approximation associated to each rotation angle. The level difference provided the intensity change, the slope of edges gave the margin sharpness, and distance between the start and end points of the pulse edge represented margin width. Besides, the inner (core) and outer diameter with respect to angle were also displayed.
A Plain Segmentation Algorithm Utilizing Region Growing Technique for Automatic Partitioning of Computed Tomography Liver Images
(2018) Arica, Sami; Avsar, Tugce Sena; Erbay, Gurcan; 0000-0002-1706-8680; AAK-5370-2021
Medical image segmentation is quite significant, especially for diagnosis and treatment of diseases. In this study, similar and different tissues in computed tomography (CT) images of liver are decomposed by utilizing region growing method. The images are preprocessed before segmentation. First, gray scale CT images are smoothed with a median filter, and a coarse segmentation is done with four level uniform quantization. A pixel from each connected component of the quantized image is selected as a seed point and is employed by region growing algorithm to specify corresponding segment. The number of segments depends on the number of connected components. Experimental results show that this basic method has successfully segmented the liver.
Predicting Tumor Recurrence in Patients with Cervical Carcinoma Treated with Definitive Chemoradiotherapy: Value Of Quantitative Histogram Analysis On Diffusion-Weighted MR Images
(2017) Erbay, Gurcan; Onal, Cem; Karadeli, Elif; Guler, Ozan C.; Arica, Sami; Koc, Zafer; https://orcid.org/0000-0002-1706-8680; https://orcid.org/0000-0002-0352-8818; https://orcid.org/0000-0001-6908-3412; https://orcid.org/0000-0003-0987-1980; 27445314; AAK-5370-2021; HOC-5611-2023; AAK-5399-2021; AAC-5654-2020; S-8384-2016
Background: Further research is required for evaluating the use of ADC histogram analysis in more advanced stages of cervical cancer treated with definitive chemoradiotherapy (CRT). Purpose: To investigate the utility of apparent diffusion coefficient (ADC) histogram derived from diffusion-weighted magnetic resonance images in cervical cancer patients treated with definitive CRT. Material and Methods: The clinical and radiological data of 50 patients with histologically proven cervical squamous cell carcinoma treated with definitive CRT were retrospectively analyzed. The impact of clinicopathological factors and ADC histogram parameters on prognostic factors and treatment outcomes was assessed. Results: The mean and median ADC values for the cohort were 1.043 +/- 0.135 x 10(-3) mm(2)/s and 1.018 x 10(-3) mm(2)/s (range, 0.787-1.443 x 10(-3) mm(2)/s). The mean ADC was significantly lower for patients with advanced stage (>= IIB) or lymph node metastasis compared with patients with stage < IIB or no lymph node metastasis. The mean ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90), and 95th percentile ADC (ADC95) were significantly lower in patients with tumor recurrence compared with patients without recurrence. In multivariate analysis, tumor size, ADC75 and ADC95 were independent prognostic factors for both overall survival and disease-free survival. Conclusion: ADC histogram parameters could be markers for disease recurrence and for predicting survival outcomes. ADC75, ADC90, and ADC95 of the primary tumor were significant predictors of disease recurrence in cervical cancer patients treated with definitive CRT.