Tumor Motion Tracking in Liver Ultrasound Images Using Mean Shift and Active Contour

In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Edge, texture, and intensity features are extracted from the first frame, and then mean shift algorithm is applied to each feature separately to find the center of ellipse related to that feature in the next frame. The center of ellipse will be the weighted average of these centers. By using mean shift actually we estimate the target movement between two consecutive frames. Once the correct ellipsoid in each frame is known, in the second step we apply the Dynamic Directional Gradient Vector Flow (DDGVF) version of active contour models, in order to find the correct boundary of tumors. We sample a few points on the boundary of active contour then translate those points based on the translation of the center of ellipsoid in two consecutive frames to determine the target movement. We use these translated sample points as an initial guess for active contour in the next frame. Our experimental results show that, the suggested method provides a reliable performance for liver tumor tracking in ultrasound image sequences.

Motion planning using shortest path

In this paper, we propose a new method for path planning to a point for robot in environment with obstacles. The resulting algorithm is implemented as a simple variation of Dijkstra's algorithm. By adding a constraint to the shortest-path, the algorithm is able to exclude all the paths between two points that violate the constraint.This algorithm provides the robot the possibility to move from the initial position to the final position (target) when we have enough samples in the domain. In this case the robot follows a smooth path that does not fall in to the obstacles. Our method is simpler than the previous proposals in the literature and performs comparably to the best methods, both on simulated and some real datasets.

A Modified No Search Algorithm for Fractal Image Compression

Fractal image compression has some desirable properties like high quality at high compression ratio, fast decoding, and resolution independence. Therefore it can be used for many applications such as texture mapping and pattern recognition and image watermarking. But it suffers from long encoding time due to its need to find the best match between sub blocks. This time is related to the approach that is used. In this paper we present a fast encoding Algorithm based on no search method. Our goal is that more blocks are covered in initial step of quad tree algorithm. Experimental result has been compared with other new fast fractal coding methods, showing it is better in term of bit rate in same condition while the other parameters are fixed.