Automatic interpretation and precision detecting of target's axis in image sequences

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.

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Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0023 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Jens Ziegle ◽

Alfredo Illanes ◽

Axel Boese ◽

Michael Friebe

Keyword(s):

Thermal Ablation ◽

Ex Vivo ◽

Cell Damage ◽

Potential Temperature ◽

Image Sequences ◽

Target Tissue ◽

Gray Level ◽

Porcine Liver ◽

Time Frequency ◽

Average Gray Level

AbstractDuring thermal ablation in a target tissue the information about temperature is crucial for decision making of successful therapy. An observable temporal and spatial temperature propagation would give a visual feedback of irreversible cell damage of the target tissue. Potential temperature features in ultrasound (US) B-Mode image sequences during radiofrequency (RF) ablation in ex-vivo porcine liver were found and analysed. These features could help to detect the transition between reversible and irreversible damage of the ablated target tissue. Experimental RF ablations of ex-vivo porcine liver were imaged with US B-Mode imaging and image sequences were recorded. Temperature was simultaneously measured within the liver tissue around a bipolar RF needle electrode. In the B-Mode images, regions of interest (ROIs) around the centre of the measurement spots were analysed in post-processing using average gray-level (AVGL) compared against temperature. The pole of maximum energy level in the time-frequency domain of the AVGL changes was investigated in relation to the measured temperatures. Frequency shifts of the pole were observed which could be related to transitions between the states of tissue damage.

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