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- MakaleAçık Erişim2024Scopus SJRQ33D reconstruction of foot metatarsal bones of women using CT images
Reis, Hatice Çatal
Bone morphology is a fundamental factor in human anatomy. However, foot and ankle bones have yet to be adequately evaluated in 3-dimensional. It is essential to present the biometric data of anatomical structures. This study formed 3D models of the metatarsal bones of the feet of young women using image processing techniques to examine biometric measurements and determine morphology on these 3D models. This study investigated bone lengths in the metatarsal bones of women feet in Türkiye. A total of ten young female subjects were included as the test group to measure the lengths of their foot metatarsal bones using CT (Computed Tomography) scans, and 20 feet (left/right) were examined. The parameters that were used for the analyses were detector collimation of 64x0.5 mm, section thickness of 0.5 mm, current of 100 mA, tube voltage of 120 kVp, and pixel spacing of 512x512 pixels with a monochrome resolution providing 16-bit gray levels. CT images were processed, and a 3D metatarsal reconstruction was gathered. Then, the biometric measurements were calculated on this 3D model. For the lengths of the volunteers' right/left foot metatarsal bones, statistically significant differences were calculated using a one-sample t-test. For the female metatarsal bones of the left and right feet, statistically significant differences in length were calculated on 3D models. The mean results of the metatarsal length measurements were MT1(metatarsal): 59.52±1.42 mm, MT2: 70.45±1.82 mm, MT3: 66.25±1.82 mm, MT4: 65.12±1.81 mm and MT5: 63.63±1.81 mm. The level of statistical significance was accepted as p <0.05 for the one-sample t-test conducted for each metatarsal bone. The lengths of the right foot metatarsal bones were different from those of the left foot metatarsal bones in the sample. However, this difference was approximately one-tenth of a millimeter. The shortest bone was MT1, and the longest bone was MT2. These measurements are consistent with the anatomical information in the literature. The 3D models from the CT images and the biometric measurements of the metatarsal bones were found to be reliable and accurate.
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İncele - MakaleAçık Erişim2023Scopus SJRQ3An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images
Reis, Hatice Catal; Bayram, Bulent
Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D models via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The parameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). According to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% average F-measure. Radiologist reports were used for the sake of comparison. The proposed technique for detecting bone tumors of the foot via CT can help radiologists with its increased precision, sensitivity, specificity, and F-measure. This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities.
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İncele - MakaleAçık Erişim2023Detection of COVID-19 infection from CT images using the medical photogrammetry technique
Çatal Reis, Hatice; Türk, Veysel; Kaya, Serhat
Medical data such as computed tomography (CT), magnetic resonance imaging (MRI), and Ultrasound images are used in medical photogrammetry. CT images have been used frequently in recent years for the diagnosis of COVID-19 disease, which has contagious and fatal symptoms. CT is an effective method for early detection of lung anomalies due to COVID-19 infection. Machine learning (ML) techniques can be used to detect and diagnose medical diseases. In particular, classification methods are applied for disease diagnosis and diagnosis. This study proposes traditional machine learning algorithms Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes, and an ensemble learning model to detect COVID-19 anomalies using CT images. According to the experimental findings, the proposed ensemble learning model produced an accuracy of 96.71%. This study can help identify the fastest and most accurate algorithm that predicts CT images with Covid-19 during the epidemic process. In addition, machine learning-based approaches can support healthcare professionals and radiologists in the diagnostic phase.
- MakaleAçık Erişim2018Detection of foot bone anomaly using medical photogrammetry
Reis, Catal H.
Photogrammetry has been used for medical diagnostic and treatment. Mostly used medical photogrammetric techniques are Ultrasound, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images. CT and MRI are the most effective method for the early detection of foot and ankle anomaly. Researchers have been developing various methods to detect anomaly. Many image segmentation techniques are available in the literature. Computer Aided Diagnosing (CAD) system has been proposed in this study for detection of foot bone anomaly by the analysis of CT images. In this study, a segmentation based on edge detection method is proposed for the classification of anomaly in foot CT images. Edge detection algorithms are the most commonly used techniques in image processing for edge detection. Canny edge detector is evaluated in this study. In this study, .dicom medical image standard format and ten male patient's foot CT images (245 images and 50 test data) are used. The used parameters are detector collimation of 64 mm, scanning thickness of 1-5 mm, and pixel sizes of 512x512 in radiometric resolution of 16 bits' gray levels. The proposed method consists of five major steps: (i) calculating the horizontal & vertical gradient, (ii) determining gradient magnitude and gradient direction, (iii) applying non-maximal suppression, (iv) computing high and low thresholds, (v) hysteresis thresholding are applied to the multi-detector computed tomography to detect the bone anomaly. In this study, automatic edge-based digital image processing techniques are applied to detect of foot bone anomaly. The proposed canny segmentation method enables users segment anomaly in MDCT of foot very quickly and efficiently. The results demonstrate that the proposed segmentation method is effective for segmenting anomaly. The proposed method obtains satisfactory performances in terms of accuracy and F-measure the area under Receiver Operating Characteristic curve (ROC curve (AUC)). The proposed segmentation method achieves an accuracy of 0.86 and F-measure of 0.92, respectively. The purpose of our study is to detect the anomaly of the foot and it was the simplest and less time consuming process.
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