Multi-regional Adaptive Image Compression (AIC) for hip fractures in pelvis radiography

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content

Uyku Analizi ve Uyku Sinyallerinin İşlenmesi

[No abstract available

Emerging Approaches in Analysis and Evaluation in Sports Science

It is not an easy task to analyze individual’s performances, especially in team sports. There are numerous variables to be measured and considered to accurate analysis. Training, warm-up, motivation methods, strategies, procedures then can be arranged accordingly. Studies in sports sciences do not generally converge on a single global (optimum) solution, where instead various findings contribute for perpetual advances. In this review, research studies, recent approaches, and limitations are discussed. Moreover, future studies, trending approaches, and new performance analysis techniques are also discussed

Analysis of water-equivalent materials used during irradiation in the clinic with XCOM and BEAMnrc

The devices used in the departments of Radiology, Nuclear Medicine, and Radiation Oncology should check for precise dose at some periods. The purpose of study is to compare the materials used for dosimetric control using Monte Carlo (MC) simulation. For MC simulation, BEAMnrc and DOSXYZnrc were used. For photoelectric absorption and total absorption, XCOM was used. Five phantom materials were selected. These materials were PMMA, polystyrene, blood liquid, soft tissue, and water. The PDD's have calculated for each material by DOSXYZnrc. When Percent Depth Dose (PDD's) examined, we could see that the water and polystyrene behaved like soft tissue and blood. However, The PMMA material didn't match with water and other materials. As a result, dose distribution for any materials is independent of its atomic number. Density of material is more important for dose distribution at MV energies. For dosimetric control, density of material should be chosen close the water properties. PMMA material shouldn't use instead of water for dose control

Medical Image Compression for Telemedicine Applications

Transferring medical images from one center to another is common use in telemedicine. These high-quality images stored in DICOM format require higher bandwidth for transmission and large storage space in PACS (Picture Archiving and Communication System) memory. Therefore, reducing the image size by preserving diagnostic information has become a need. In this sense, medical image compression is a technique that overcomes both transmission and storage cost by suggesting lossy and lossless compression algorithms. There are numerous compression methods developed for region-based studies generally used in the radiography, computed tomography (CT) and magnetic resonance (MR) images. In this review, information about region based medical image compression and recent studies with different approaches are expressed

Investigation of ballistic gelatin based phantom models for computed tomography, x-ray and ultrasound imaging devices

Simulation-based medical education provides a learner-centered environment in which novice, intermediate and advanced practitioners can learn or practice their skills without harming patients. Medical device phantoms are specially designed objects that are used in simulation based trainings as well as technical features such as evaluating, analyzing and adjusting the performance of devices. Ballistic gelatin is a member of the 250A-Bloom hydrogel family, which mimics human muscle tissue in terms of mechanical properties. In this study, ballistic gelatinbased phantoms were produced and examined on medical device images. According to the results, it is suggested to use phantom models as a medical device phantom in device training. The main advantages of these models are that their production is practical and economical.Simülasyon temelli tıbbi eğitim; acemi, orta ve ileri düzey pratisyenlerin hastalara zarar vermeden becerilerini öğrenebilecekleri veya pratik yapabilecekleri, öğrenen merkezli bir ortam sağlar. Tıbbi cihaz fantomları, cihazların performansını değerlendirmek, analiz etmek ve ayarlamak gibi teknik özelliklerinin yanında simülsyon temelli eğitimlerde de kullanılan ve özel olarak tasarlanan nesnelerdir. Balistik jelatin, mekanik özellikleri açısından insan kas dokusunu taklit eden 250A-Bloom hidrojel ailesinin bir üyesidir. Bu çalışmada balistik jelatin esaslı fantomlar üretilmiş ve tıbbi cihaz görüntülerinde incelenmiştir. Elde edilen sonuçlara göre, hazırlanan fantom modellerinin cihaz eğitimlerinde bir tıbbi cihaz fantomu olarak kullanılması önerilmektedir. Bu modellerin en büyük avantajları üretiminin pratik ve ekonomik olmasıdır

An Early Warning Algorithm to Predict Obstructive Sleep Apnea (OSA) Episodes

Sleep apnea is a common respiratory disorder during sleep. It is characterized by shallow or no breathing during sleep for at least 10 seconds. Decrease in sleep quality may effect the next day daily routine unfavorably. In some cases apnea period (not breathing interval) can last more than 30 seconds causing fatal outcomes. 14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States. Patients may face apnea for more than 300 times in a single overnight sleep. Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, having Snorring, SpO2, Nasal Airflow EEG, EMG, ECG signals, performed in sleep study laboratories. In this study, a fully automatic apnea detection algorithm is mentinoed and an early warning system is proposed to predict OSA episodes by extracting time-series features of pre-OSA periods and regular respiration using nasal airflow signal. Extracted features are then reduced by RANSAC and entropy based approaches to improve the performance of prediction algorithm. Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, k-Nearest Neighbor and a modified Linear Regression are implemented for learning and classification of nasal airflow signal episodes. The results show that OSA episodes are predicted with 86.9% of accuracy and 91.5% of sensitivity, 30 seconds before patient faces apnea. By the use of predicting an apnea episode before happening, it is possible to prevent patient to face apnea by early warning which can minimize the possible health risks