During the past two decades, Oil Spill Detection (OSD) received widespread attention from research communities. Both detection and analysis of OSD have fundamental importance for improving the efficiency of maritime environment ecosystems. Most recently, thermal imaging devices are used for oil detection and disaster management projects since they can provide spilling information at Day/Night time and can work under adverse weather conditions. Nevertheless, the quality of these images are poor, they are noisy, blurry, and they have low resolution. As well as a thermal image contrast between oil and water is often so small, that makes OSD problematic and challenging. The goal of this paper is to automatically detect and analyze the OSD on the upper sea/ocean layer that may help in the visualization of oil spills for disaster management purpose. For the purposes of comparison, quantitative and qualitative analysis was conducted on the existing segmentation approaches, namely OTSU, and Sauvola by using two new databases composed each of 100 diversified images extracted from 2 different videos. The performance of the proposed also evaluated by examining statistical measures of boundary error (BE), probabilistic rand index (PRI), variation of information (VI), global consistency error (GCE), and structural similarity index (SSI). The obtained results proved that the proposed method is more robust, accurate, and straightforward. Future research recommendations and conclusions are presented.
Enhancement and segmentation of suspicious regions of a thermal breast image are among the most significant challenges facing radiologists while examining and interpreting the thermogram images. The proposed focuses to following problems: How can increase the contrast between cancer regions and the background, how to adjust the intensity of the presence of BC region to be more homogeneous in the infrared image; how to efficiently segment tumors as suspicious regions with a very weak contrast to their background and how to extract the relevant features which separate tumors from background. The proposed cancer segmentation scheme composed of three main stages: (i) image enhancement; (ii) detection of the tumor region; (iii) features extraction from the segmented tumor area followed by coloring the segmented region. The performance of the proposed enhancement and segmentation method was evaluated on DMR-IR database and the average segmentation Accuracy, MCC, Dice and Jaccard obtained are 98.8%, 47.96%, 43.03%, and 34.8% respectively which is better than FCM, LCV-LSM, and EM-GMM methods. Besides, we also investigate the role of thermal image enhancement in tumor characterization and feature extraction.
The primary objective of enhancement is to improve the contrast an image, that the outcome image is more appropriate than the original image for the given application. One of the simplest, computationally effective and most used empirical algorithms that may improve overall contrast is the class (linear stretching and non-linear stretching) of stretching methods. However, linear and non-linear stretching suffer from several issues, for instance, a low-contrast effect by organizing intensities or an over-brightness effect by super-imposing intensities. The goal of this paper is to present new techniques for image contrast enhancement: (1) a bi-non-linear contrast-stretching algorithm, (2) the optimized combination of linear contrast and non-linear contrast stretching algorithms, and (3) the optimized combination of a linear contrast, a non-linear contrast stretching and a local histogram equalization algorithm. Computer simulations on publicly available Thermal Focus Image Database and the Tufts Face Database show that the proposed methods increase the dynamic image range and demonstrate a significantly improved global and local contrast by taking the most exquisite details and edges. In addition, the simulation results show that the proposed method well correlates with subjective evaluations of image quality. The presented concept is useful in guiding the future design of cutting-edge image enhancement methods.
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