A Quantitative Measure for Retinal Blood Vessel Segmentation Evaluation

Abstract:
Analysis of retinal blood vessels allows us to identify individuals with the onset of cardiovascular diseases, diabetes and hypertension. Unfortunately, this analysis requires a specialist to identify specific retinal features which is not always possible. Automation of this process will allow the analysis to be performed in regions where specialists are non-existent and also large scale analysis. Many algorithms have been designed to extract the retinal features from fundus images. However, to date, these algorithms have been evaluated using generic image similarity measures without any justification of the reliability of these measures. In this article, we study the applicability of different measures for retinal vessel segmentation evaluation task. In addition, we propose an evaluation measure, F₁, which is based on precision, recall and F-measure concept to deal with this evaluation task. An important property of F₁ is its tolerance of small localization errors which often appear in a segmented image, but do not affect the desired retinal features. The performances of different measures are tested on both real and synthetic datasets which take into account the important properties of retinal blood vessels. The results show that F1 provides the greatest correlation to the desired evaluation measure in all experiments. Thus, it is the most suitable measure for retinal segmentation evaluation task.
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Dolphin Whistle Frequency Estimation using Gaussian Mixture Probability Hypothesis Density Filter

Abstract:
This paper formulates the automation of dolphin whistle track estimation process as a Multiple Target Tracking (MTT) problem using Random Finite Set (RFS) approach. It focuses on achieving possible automation in dolphin whistle tracking using the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. Acoustic recordings of three different dolphin species have been considered. Simulation results corroborate that automation in dolphin whistle tracking has been achieved. The GM-PHD filter has been able to produce reliable estimate of whistle frequencies in the presence of multiple whistles, spontaneous death/birth of whistles and multiple whistles crossing each other.
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Fuzzy Based Adaptive Mean Filtering Technique for Removal of Impulse Noise from Images

Abstract:
This paper proposes a fuzzy based adaptive mean filtering (FBAMF) scheme to remove high density impulse noise from images. The FBAMF is a two-stage filter where, in the first stage, a fuzzy detection technique is used to differentiate between corrupted and uncorrupted pixel by calculating the membership value of each and every pixel. Then, the corrupted pixel subjected to the second stage where they are replaced by mean value of uncorrupted neighboring pixels selected from a window adaptively. If the numbers of uncorrupted pixels in the selected window are not sufficient, a window of next higher size is chosen. Thus, window size is automatically adapted based on the density of noise in the image. As a result window size may vary pixel to pixel while filtering. Comparison shows the proposed filter effectively removes the impulse noise with significant image quality compared with conventional method such as the Standard Median Filter (SMF), Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF) and recently proposed methods such as Efficient Decision Based Algorithm (EDBA), Improved Efficient Decision-Based Algorithm (IDBA) and fuzzy-based decision algorithm (FBDA). The visual and quantitative results show that the performance of the proposed filter in the preservation of edges and details is better even at noise level as high as 95%. The efficiency of the proposed algorithm is evaluated using different standard images.
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