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The purpose of this study was to determine whether the use of computer-assisted surgery can improve the clinical results in total knee arthroplasty (TKA) compared with conventional methods of TKA.A literature search of PubMed (1966 to August 2011), CENTRAL (Cochrane Controlled Trials Register; issue 3, 2011), and EMBASE (1984 to August 2011) was conducted. Randomized, controlled trials detecting the clinical outcomes of TKA with or without the use of computer-assisted surgery were identified. A meta-analysis of these clinical trials was then performed. Twenty-one articles were included in the meta-analysis. The results confirmed that operative time was significantly increased with the use of computer-assisted TKA (mean standard difference, 14.68; 95% confidence interval [CI], 11.74 to 17.62; P<.00001], whereas no significant difference existed between the 2 groups regarding the total operative blood loss (mean standard difference, -54.38; 95% CI, -119.76 to 11.00; P=.10). As for other clinical outcomes, including the Knee Society Score (mean standard difference, 4.47; 95% CI, -1.05 to 9.99; P=.36) and range of motion (mean standard difference, 1.38; 95% CI, -1.43 to 4.18; P=.34), the use of computer-assisted TKA did not help to improve function recovery postoperatively.

Abstract. Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before processing in previous image splicing detection algorithms. However, most natural images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in Quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. The support vector machine (SVM) is exploited to classify the authentic and spliced images. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve high classification accuracy.

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