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The gamma unit is used to irradiate a target within the brain. During such a treatment many parameters, including the number of shots, the coordinates, the collimator size and the weight associated with each shot, affect the amount of dose delivered to the target volume and to the surrounding normal tissues. Hence it is not easy to determine an appropriate set of these parameters by a trial and error method. For this reason, we present here an optimization method to determine mathematically those parameters. This method is composed of two steps: firstly, a quasi-Newton method is used to deal with the continuous variables such as position and weight of shots; the result obtained at the end of this step then serves as the initial configuration for the next step, in which a simulated annealing method is applied to optimize all the aforementioned parameters. Application of the proposed methods to two examples shows that our optimization algorithm runs in a satisfactory way.

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During a treatment using the Leksell gamma unit, the physician and physicist need to determine a treatment plan by changing the parameters such as collimator sizes, the position of isocenters and isocenters' weights. This is a complex problem because the set of parameters is large, especially when targets are geometrically close to a critical structure. For this reason, we present here an optimization algorithm, namely the multiplier penalty method, to mathematically determine those parameters. Two cases are presented in this article: the first one is really planned by a physicist in a clinical treatment, and is redone in our optimization algorithm to show the effectiveness of this method; the second one is theoretical where a critical structure is placed close to the target volume. The results show that this method achieves an excellent conformation to the specified isodose curve with the contour of the target volume, allowing minimal damage to surrounding healthy tissue.

Abstract-Traditionally, only experts who are equipped with professional knowledge and rich experience are able to recognize different species of wood. Applying image processing techniques for wood species recognition can not only reduce the expense to train qualified identifiers, but also increase the recognition accuracy. In this paper, a wood species recognition technique base on Scale Invariant Feature Transformation (SIFT) keypoint histogram is proposed. We use first the SIFT algorithm to extract keypoints from wood cross section images, and then k-means and k-means++ algorithms are used for clustering. Using the clustering results, an SIFT keypoints histogram is calculated for each wood image. Furthermore, several classification models, including Artificial Neural Networks (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are used to verify the performance of the method. Finally, through comparing with other prevalent wood recognition methods such as GLCM and LBP, results show that our scheme achieves higher accuracy.