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Item Metadata only การเขียนโปรแกรมและการประมวลผลภาพเบื้องต้นด้วยภาษาไพธอนสำหรับนักรังสีเทคนิค(2560) ยุทธพล วิเชียรอินทร์; มหาวิทยาลัยมหิดล. คณะเทคนิคการแพทย์. ภาควิชารังสีเทคนิคItem Metadata only An evaluation of physical performance of conventional x-ray machine using computed radiography system(Mahidol University. Mahidol University Library and Knowledge Center, 2008) Tipvimol MeechaiItem Metadata only Computer-aided classification of alzheimer's disease based on support vector machine with combination of cerebral image features in MRI(Mahidol University. Mahidol University Library and Knowledge Center, 2016) Chonnikan Jongkreangkrai; Yudthaphon Vichianin; Chiraporn Tocharoenchai; Kakanand SrungboonmeeSeveral studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from magnetic resonance (MR) brain images, such as the volume and shape of hippocampus and cerebral cortical thicknesses. In this study, we were interested in combining cerebral image features, including hippocampus and amygdala volumes, and entorhinal cortex thickness to improve the performance of the AD differentiation. Thus, the aim of this study was to investigate the useful cerebral image features obtained from MRI for computer-aided classification of AD patients using a support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and those of 100 normal subjects obtained from Alzheimer's Disease Neuroimaging Initiative were studied. FreeSurfer (software for analysis of brain imaging data) was used to measure hippocampus and amygdala volumes, and entorhinal cortex thicknesses in left and right brain hemispheres. Relative volumes of hippocampus and amygdala were then calculated using total intracranial volume (TIV) to correct the variation in individual head size. SVM was employed for classification of AD patients using five different combinations of cerebral image features (H: left and right hippocampus relative volumes, A: left and right amygdala relative volumes, E: left and right entorhinal cortex thicknesses, HA: left and right hippocampus and amygdala relative volumes, and ALL: all features). Receiver operating characteristic (ROC) analysis and area under the curve (AUC) were used to evaluate the method. AUC values of 5 cerebral feature combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA), and 0.8906 (ALL), respectively. Although using all features (ALL) provided the highest AUC, there were no statistically significant differences among them except for the A feature. Our results showed that all combinations of cerebral image features derived from T1- weighted MR brain images may be feasible for computer-aided classification of AD patients by using SVM.Item Metadata only A design and assessment of computer assisted instruction (CAI) on computed tomography(Mahidol University. Mahidol University Library and Knowledge Center, 2005) Weena Swatdiswanee; Manus Mongkolsuk; Napapong Pongnapang, the second with Sound without Answer and the third with Sound with Answer by evaluating learning achievement. The sample group were 70 first and second year Radiological Technology Students, Mahidol University. They were randomly separated into three groups...There is a lack of media for teaching Computed Tomography. Computer Assisted Instruction in (CAI) in Computed Tomography may solve this problem. The purpose of this research was to compare three different types of CAI, the first with No SoundItem Metadata only Imaging of complications following treatment with assisted reproductive technology: keep on your radar at each step(2022-01-01) Srisajjakul S.; Mahidol UniversitySince the advent of assisted reproductive technology (ART), the utilization of ART procedures has become increasingly popular among women seeking to establish pregnancy. Radiologists are therefore likely to encounter the various complications of ART... of an additional imaging modality, such as computed tomography or magnetic resonance imaging, as a problem-solving tool. This article briefly discusses the steps involved in assisted reproduction. Its aim is to help radiologists become familiarizedPublication Metadata only Patient’s medical image mobile controller(2017-10-19) Patcharathicha Burananithi; Chayanit Ditsom; Thipnantana Triphom; Pattanasak Mongkolwat; Mahidol University© 2017 IEEE. Medical imaging modalities generate medical images of a patient’s body to assist medical doctors to provide accurate medical diagnoses to the patient. Typical imaging interpretations are done by a board certified physician, called... to another location to seek for a consultation from a radiology about images in question. It is a very time consuming process and inefficient use of resources. This project offers an app called “REMOTIMAGE” that offers an easy access to view medical images