Proc. SPIE. 6516, Medical Imaging 2007: PACS and Imaging Informatics
KEYWORDS: Image compression, Computer aided diagnosis and therapy, JPEG2000, Lung, Medical imaging, Image quality, Neural networks, Computed tomography, CAD systems, Picture Archiving and Communication System
Image compression has been used to increase the communication efficiency and storage capacity. JPEG 2000
compression, based on the wavelet transformation, has its advantages comparing to other compression methods, such as
ROI coding, error resilience, adaptive binary arithmetic coding and embedded bit-stream. However it is still difficult to
find an objective method to evaluate the image quality of lossy-compressed medical images so far. In this paper, we
present an approach to evaluate the image quality by using a computer aided diagnosis (CAD) system. We selected 77
cases of CT images, bearing benign and malignant lung nodules with confirmed pathology, from our clinical Picture
Archiving and Communication System (PACS). We have developed a prototype of CAD system to classify these images
into benign ones and malignant ones, the performance of which was evaluated by the receiver operator characteristics
(ROC) curves. We first used JPEG 2000 to compress these cases of images with different compression ratio from
lossless to lossy, and used the CAD system to classify the cases with different compressed ratio, then compared the ROC
curves from the CAD classification results. Support vector machine (SVM) and neural networks (NN) were used to
classify the malignancy of input nodules. In each approach, we found that the area under ROC (AUC) decreases with the
increment of compression ratio with small fluctuations.
Proc. SPIE. 6145, Medical Imaging 2006: PACS and Imaging Informatics
KEYWORDS: Content based image retrieval, Image compression, Pathology, Feature extraction, Lung, Medical imaging, Computed tomography, Image retrieval, Radiology, Picture Archiving and Communication System
Traditionally, Picture Archiving and Communication Systems (PACS) use textual-based retrieval, which have their limitations. General-purposed content-based image retrieval (CBIR) systems often do not perform well in medical images and are not integrated with PACS and Radiology Information System (RIS). In this presentation we design a CBIR system that is integrated with PACS and RIS, by using a user-supplied query image to retrieve similar images from PACS and get corresponding reports from RIS. We also employ ACR index for radiological diagnosis to reduce the search space and to provide meaningful results in our CBIR system. We use high resolution CT lung images as the test data. A key image is selected for each series, and after a radiologist delineates the pathology bearing region, local texture features as well as ACR indexes and series UID are stored in a CBIR server. Series UID can be used to retrieve images from PACS and to obtain corresponding reports from RIS. The system is a useful learning tool for radiology education and can provide valuable references for radiologists when a new case comes.
Proc. SPIE. 5371, Medical Imaging 2004: PACS and Imaging Informatics
KEYWORDS: Surgery, Synthetic aperture radar, Image processing, Networks, Control systems, Medical imaging, Image transmission, Computer architecture, Teleradiology, Picture Archiving and Communication System
Severe acute respiratory syndrome (SARS) is a respiratory illness that had been reported in Asia, North America, and Europe in last spring. Most of the China cases of SARS have occurred by infection in hospitals or among travelers. To protect the physicians, experts and nurses from the SARS during the diagnosis and treatment procedures, the infection control mechanisms were built in SARS hospitals. We built a Web-based interactive teleradiology system to assist the radiologists and physicians both in side and out side control area to make image diagnosis. The system consists of three major components: DICOM gateway (GW), Web-based image repository server (Server), and Web-based DICOM viewer (Viewer). This system was installed and integrated with CR, CT and the hospital information system (HIS) in Shanghai Xinhua hospital to provide image-based ePR functions for SARS consultation between the radiologists, physicians and experts inside and out side control area. The both users inside and out side the control area can use the system to process and manipulate the DICOM images interactively, and the system provide the remote control mechanism to synchronize their operations on images and display.