Exploiting Quality-Guided Adaptive Optimization for Fusing Multimodal Medical Images

Deep Similarity Learning for Multimodal Medical Images

An effective similarity measure for multi-modal images is crucial for medical image fusion in many clinical applications. The underlining correlation across modalities is usually too complex to be modelled by intensity-based statistical metrics. Therefore, approaches of learning a similarity metric are proposed in recent years. In this work, we propose a novel deep similarity learning method th...

Registration of Multimodal Medical Images

Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Within medical research (e.g. neuroscience research) they are used to investigate disease processes and understand normal development and ageing. Technically, medical imaging mainly processes missing, ambiguous, complementary, redundant and distor...

Model Guided Rendering for Medical Images

Adaptive Particle Swarm Optimization (APSO) for multimodal function optimization

This research paper presents a new evolutionary optimization model based on the particle swarm optimization (PSO) algorithm that incorporates the flocking behavior of a spider. The search space is divided into several segments like the net of a spider. The social information sharing among the swarms are made strong and adaptive. The main focus is on the fitness of the swarms adjusting to the le...

A New Framework for Fusing Stereo Images with Volumetric Medical Images

Abs t rac t . Some medical interventions require knowing the correspondence between an MRI/CT image and the actual position of the patient. Examples are in neurosurgery or radiotherapy, but also in video surgery (laparoscopy). Recently, computer vision techniques have been proposed to find this correspondence without any artificial markers. Following the pioneering work of [GLPI+94], [CZH+94], ...