Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases. A computer-aided diagnosis system is proposed for detecting retina abnormalities, which combines K means-based segmentation of the retina image, after due preprocessing, followed by machine learning techniques, using several low level and statistical features. Abnormalities in the retina that are classified are caused by age-related macular degeneration and diabetic retinopathy. Performance measures used in the analysis are accuracy, sensitivity, specificity, F-measure, and Mathew correlation coefficient. A comparison with another machine learning technique, the Naïve Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naïve Bayes classifier which yields an accuracy of 83.63%. Graphical abstract Random Forest classifier for abnormality detection in retina images.

The aim of this study was to investigate the effects of Raloxifene (Ral) on degeneration-related changes in osteoarthritis (OA)-like chondrocytes using two- and three-dimensional models. Five-azacytidine (Aza-C) was used to induce OA-like alterations in rat articular chondrocytes and the model was verified at molecular and macrolevels. Chondrocytes were treated with Ral (1, 5 and 10 mu M) for 10 days. Caspase-3 activity, gene expressions of aggrecan, collagen II, alkaline phosphatase (ALP), collagen X, matrix metalloproteinases (MMP-13, MMP-3 and MMP-2), and MMP-13, MMP-3 and MMP-2 protein expressions were studied in two-dimensional model. Matrix deposition and mechanical properties of agarose-chondrocyte discs were evaluated in three-dimensional model. One mu M Ral reduced expression of OA-related genes, decreased apoptosis, and MMP-13 and MMP-3 protein expressions. It also increased aggrecan and collagen II gene expressions relative to untreated OA-like chondrocytes. In three-dimensional model, 1 mu M Ral treatment resulted in increased collagen deposition and improved mechanical properties, although a significant increase for sGAG was not observed. In summation, 1 mu M Ral improved matrix-related activities, whereas dose increment reversed these effects except ALP gene expression and sGAG deposition. These results provide evidence that low-dose Ral has the potential to cease or reduce the matrix degeneration in OA.

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Abstract-In this paper we propose a geometry-topology based algorithm for Japanese Hiragana character recognition. This algorithm is based on center of gravity identification and is size, translation and rotation invariant. In addition, to the center of gravity, topology based landmarks like conjunction points masking the intersection of closed loops and multiple strokes, as well as end points have been used to compute centers of gravity of these points located in the individual quadrants of the circles enclosing the characters. After initial pre-processing steps like notarization, resizing, cropping, noise removal, synchronization, the total number of conjunction points as well as the total number of end points are computed and stored. The character is then encircled and divided into four quadrants. The center of gravity (cog) of the entire character as well as the cogs of each of the four quadrants are computed and the Euclidean distances of the conjunction and end points in each of the quadrants with the cogs are computed and stored. Values of these quantities both for target and template images are computed and a match is made with the character having the minimum Euclidean distance. Average accuracy obtained is 94.1 %.

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