Adverse events of focused ultrasound surgery for uterine fibroids and adenomyosis

Nguyen Minh Duc,1,* Huynh Quang Huy,1,* Bilgin Keserci2,3 1Department of Radiology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam; 2Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia; 3Department of Radiology, Hospital Universiti Sains Malaysia, USM 16150 Kubang Kerian, Kelantan, Malaysia *These authors contributed equally to this work Abstract: Uterine fibroids and adenomyosis are two common gynecological benign tumors that deteriorate women’s quality of life. The prevalence of uterine fibroid and adenomyosis is approximately 70%–80% and 5%–70%, respectively. The efficacy and safety of focused ultrasound surgery (FUS) therapy for treating uterine fibroid and adenomyosis patients has been proved. Regardless of this fact, there are still some potential adverse events that may arise during and after the provision of FUS treatment, which can degrade patients’ quality of life. Understanding the possible adverse events of FUS treatment can improve the confidence in the selection of eligible patients, optimization of treatment strategy, and monitoring of the treatment procedure to ensure patients’ safety. Keywords: adenomyosis, leiomyoma, adverse events, focused ultrasound surger

Study of Automatic Enhancement for Chest Radiograph

Because of the large difference of the densities in the lung and other structures, the chest x-ray image behaves as a wide-range intensity distribution, which brings on a bit of difficulty to investigate the focus. In the paper, according to the intensity properties of the chest radiograph, the chest radiographic image is divided into three subregions, and a piecewise linear transformation model is established. An approach of automatic enhancement is presented, based on the gray-level normalization. The average enhanced ratios of three subregions of the normal and severe acute respiratory syndrome image are increased by 10.70% and 25.55%, respectively. The technique is proved to be effective through the evaluation of the improved images

Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis

The purpose of this study was to develop methods for the differentiation of urinary stones and vascular calcifications using computer-aided diagnosis (CAD) of non-contrast computed tomography (CT) images. From May 2003 to February 2004, 56 patients that underwent a pre-contrast CT examination and subsequently diagnosed as ureter stones were included in the study. Fifty-nine ureter stones and 53 vascular calcifications on pre-contrast CT images of the patients were evaluated. The shapes of the lesions including disperseness, convex hull depth, and lobulation count were analyzed for patients with ureter stones and vascular calcifications. In addition, the internal textures including edge density, skewness, difference histogram variation (DHV), and the gray-level co-occurrence matrix moment were also evaluated for the patients. For evaluation of the diagnostic accuracy of the shape and texture features, an artificial neural network (ANN) and receiver operating characteristics curve (ROC) analyses were performed. Of the several shape factors, disperseness showed a statistical difference between ureter stones and vascular calcifications (p < 0.05). For the internal texture features, skewness and DHV showed statistical differences between ureter stones and vascular calcifications (p < 0.05). The performance of the ANN was evaluated by examining the area under the ROC curves (AUC, Az). The Az value was 0.85 for the shape parameters and 0.88 for the texture parameters. In this study, several parameters regarding shape and internal texture were statistically different between ureter stones and vascular calcifications. The use of CAD would make it possible to differentiate ureter stones from vascular calcifications by a comparison of these parameters