data compression

Body sensor network is a key technology that is used for supervising the physiological information from a long distance that enables physicians to predict and diagnose effectively the different conditions. These networks include small sensors with the ability of sensing where there are some limitations in calculating and energy. In the present research, a new compression method based on the analysis of principal components and wavelet transform is used to increase the coherence. In the present method, the first analysis of the main principles is to find the principal components of the data in order to increase the coherence for increasing the similarity between the data and compression rate. Then, according to the ability of wavelet transform, data are decomposed to different scales. In restoration process of data only special parts are restored and some parts of the data that include noise are omitted. By noise omission, the quality of the sent data increases and good compression could be obtained. Pilates practices were executed among twelve patients with various dysfunctions. The results showed 0.7210, 0.8898, 0.6548, 0.6765, 0.6009, 0.7435, 0.7651, 0.7623, 0.7736, 0.8596, 0.8856 and 0.7102 compression ratios in proposed method and 0.8256, 0.9315, 0.9340, 0.9509, 0.8998, 0.9556, 0.9732, 0.9580, 0.8046, 0.9448, 0.9573 and 0.9440 compression ratios in previous method (Tseng algorithm). Comparing compression rates and prediction errors with the available results show the exactness of the proposed method.

With increase of digital imaging, the need for storage space and transmission speed also increases. Compressed images need less storage space and decrease the transmission time. However, compression could compromise image quality. The aim of this study was to evaluate the infl uence of image compression on the identifi cation of cephalometric points on direct digital lateral cephalogram images, compared with the digital imaging and communications in medicine (DICOM) format. In this analytical-descriptive study, 19 direct digital lateral cephalograms saved in DICOM format were used. They were converted to joint photographic experts group (JPEG) 2000 format with quality factors 85, 75, and 60 adding up to 76 images (DICOM, JPEG 85, 75, and 60). The images were randomized and eight cephalometric points were identifi ed on each image by a professional, using the x-y coordinate system. Analysis of variance (ANOVA) was applied to investigate if there was a statistically signifi cant difference in the location of cephalometric points between each group of images. All tests were applied at a signifi cance level of 5%. The results did not demonstrate any statistically signifi cant difference in the identifi cation of the eight cephalometric points between the DICOM images and the JPEG2000 quality factors 85, 75, and 60. JPEG2000 images of lateral cephalograms with quality factors 85, 75, and 60 did not demonstrate any alterations in the identifi cation of cephalometric points compared with the DICOM format. JPEG2000 is a reliable fi le format for the compression of digital lateral cephalograms.