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issn:1618-727
1.  Differentiation of Urinary Stone and Vascular Calcifications on Non-contrast CT Images: An Initial Experience using Computer Aided Diagnosis 
Journal of Digital Imaging  2009;23(3):268-276.
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.
doi:10.1007/s10278-009-9181-0
PMCID: PMC3046652  PMID: 19190962
Ureter stone; CT; computer-aided diagnosis
2.  The Effect of Wireless LAN-Based PACS Device for Portable Imaging Modalities 
The aim of this study was to develop wireless Picture Archiving and Communication System (PACS) device and to analyze its effect on image transfer from portable imaging modalities to the main PACS server. Using a laptop computer equipped with wireless local area network (LAN), the authors developed a wireless PACS device with DICOM modality worklist and DICOM storage server modules. This laptop computer could be easily fixed to portable imaging modalities such as ultrasound machines. From May to August 2007, 112 portable examinations were evaluated. Of these, 62 were done with wireless LAN-based PACS device, and 50 were done without wireless PACS device. To evaluate the impact of the wireless LAN-based PACS device on productivity and workflow, we analyzed the mean time delay and standard deviations (SD) both in cases where wireless LAN-based PACS device was used and in cases where it was not used. Statistical analysis was performed using a t test. The mean time interval from image acquisition to storage in the main PACS when the wireless LAN-based PACS device was used was 342.4 s (5 min and 42.4 s, SD = 509.2 s). When the wireless PACS was not used, the mean time interval was 2,305.5 s (38 min and 25.5 s, SD = 1,371.8 s). The mean time interval was statistically different between the two groups (t test, p < 0.001). The wireless LAN-based PACS device could help in reducing the storage intervals of images obtained by portable machines and in promoting effective and rapid treatment of patients who have undergone portable imaging examinations.
doi:10.1007/s10278-008-9174-4
PMCID: PMC3043767  PMID: 19137373
Wireless LAN; portable modalities; productivity; workflow
3.  Computer-aided Prostate Cancer Detection using Texture Features and Clinical Features in Ultrasound Image 
Journal of Digital Imaging  2008;21(Suppl 1):121-133.
In this paper, we propose a new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor. With the proposed method, we can detect cancerous tissues efficiently with high specificity (about 90–95%)and high sensitivity (about 92–96%) by the measurement of the number of correctly classified pixels. Multiresolution autocorrelation can detect cancerous tissues efficiently, and clinical knowledge helps to discriminate the cancer region by location and shape of the region and increases specificity. The support vector machine is used to classify tissues based on those features. The proposed method will be helpful in formulating a more reliable diagnosis, increasing diagnosis efficiency.
doi:10.1007/s10278-008-9106-3
PMCID: PMC3043871  PMID: 18322751
Cancer detection; classification; computer-aided diagnosis (CAD); decision support techniques; image interpretation
4.  Managing the CT Data Explosion: Initial Experiences of Archiving Volumetric Datasets in a Mini-PACS 
Journal of Digital Imaging  2005;18(3):188-195.
Two image datasets (one thick section dataset and another volumetric dataset) were typically reconstructed from each single CT projection data. The volumetric dataset was stored in a mini-PACS with 271-Gb online and 680-Gb nearline storage and routed to radiologists’ workstations, whereas the thick section dataset was stored in the main PACS. Over a 5-month sample period, 278 Gb of CT data (8976 examinations) was stored in the main PACS, and 738 Gb of volumetric datasets (6193 examinations) was stored in the mini-PACS. The volumetric datasets formed 32.8% of total data for all modalities (2.20 Tb) in the main PACS and mini-PACS combined. At the end of this period, the volumetric datasets of 1892 and 5162 examinations were kept online and nearline, respectively. Mini-PACS offers an effective method of archiving every volumetric dataset and delivering it to radiologists.
doi:10.1007/s10278-005-5163-z
PMCID: PMC3046710  PMID: 15924274
Multidetector row computed tomography; volumetric dataset; mini-PACS

Results 1-4 (4)