Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering.

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%-93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

3D video visualization employing wavelet multilevel decomposition.

This study analyzed the implementation and performance of a framework that can be efficiently applied to three-dimensional (3D) video sequence visualization. The proposed algorithm is based on wavelets and wavelet atomic functions used in the computation of disparity maps. The proposed algorithm employs wavelet multilevel decomposition and 3D visualization via color anaglyphs synthesis. Simulations were run on synthetic images, synthetic video sequences, and real-life video sequences. Results shows that this novel approach performs better in depth and spatial perception tasks compared to existing methods, both in terms of objective criteria such as quantity of bad disparities and similarity structural index measure and the more subjective measure of human vision.