Dataset Information

Automatic round-the-clock detection of whales for mitigation from underwater noise impacts.

ABSTRACT: Loud hydroacoustic sources, such as naval mid-frequency sonars or airguns for marine geophysical prospecting, have been increasingly criticized for their possible negative effects on marine mammals and were implicated in several whale stranding events. Competent authorities now regularly request the implementation of mitigation measures, including the shut-down of acoustic sources when marine mammals are sighted within a predefined exclusion zone. Commonly, ship-based marine mammal observers (MMOs) are employed to visually monitor this zone. This approach is personnel-intensive and not applicable during night time, even though most hydroacoustic activities run day and night. This study describes and evaluates an automatic, ship-based, thermographic whale detection system that continuously scans the ship's environs for whale blows. Its performance is independent of daylight and exhibits an almost uniform, omnidirectional detection probability within a radius of 5 km. It outperforms alerted observers in terms of number of detected blows and ship-whale encounters. Our results demonstrate that thermal imaging can be used for reliable and continuous marine mammal protection.

SUBMITTER: Zitterbart DP

PROVIDER: S-EPMC3741354 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Automatic round-the-clock detection of whales for mitigation from underwater noise impacts.

Zitterbart Daniel P DP Kindermann Lars L Burkhardt Elke E Boebel Olaf O

PloS one 20130812 8

Loud hydroacoustic sources, such as naval mid-frequency sonars or airguns for marine geophysical prospecting, have been increasingly criticized for their possible negative effects on marine mammals and were implicated in several whale stranding events. Competent authorities now regularly request the implementation of mitigation measures, including the shut-down of acoustic sources when marine mammals are sighted within a predefined exclusion zone. Commonly, ship-based marine mammal observers (MM ...[more]

PMID: 23951113

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Project description:IntroductionOne major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias.MethodsA phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A "difference image" created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria.ResultsSeveral radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected.ConclusionTo have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates.

Dataset Information

Automatic round-the-clock detection of whales for mitigation from underwater noise impacts.

Publications

Automatic round-the-clock detection of whales for mitigation from underwater noise impacts.

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