First, three-dimensional (3D) tumor-appearance templates were cross-correlated with MR brain images to evaluate their similarity, and a correlation threshold was established for metastasis candidates. The training group consisted of eight patients harboring 20 metastases. Two radiologists and a radiation oncologist marked the location of the brain metastases. Nineteen patients were scanned using a 1.5 Tesla MR scanner. To develop and evaluate a method for an automatic detection of brain metastases in MR images. While in the testing stage we used three classifiers (Euclidian Distance, Modify of the Standardize Euclidian Distance and Correlation) and the results show that (Euclidian Distance gave 94.4%, Modify of the Standardize Euclidian Distance gave 100% and Correlation gave 94.4%) We create our database (three videos with 250 frames) for training and one video for testing.

The results in the training stage were (C4.5 gave 100%, k-mean gave 95.2% k-medoid gave 91.9% and ANN gave 91.27%). The proposed algorithm for Convert Gestures of Arabic Words into Voice, record video of gesture (of the dumb person) then convert the video into frames (images), preprocessing for the resulted image must done by remove the noise, resize the images and increase the contrast, then calculate the distance to clustering the words by using (C4.5, k-mean, k- medoid and artificial neural network), calculate the distance (or features) by using Euclidean distance and slope where, there are eighteen features (eight features from Euclidean distance, eight features from slop, Area, and perimeter). In this paper, we suggest an algorithm for recognizing hand gestures of Arabic words (اتمنى لك حیاة سعیدة-اقتباس) to by using dumb (through signs) and convert the sings into voice corresponding to sings words.

Gestures are one of the best ways of communication between dumbs and other people using the expression of signs language.

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