Specific Segmentation Methods Research Articles

Comparison of specific segmentation methods used for copy move detection

<p><span lang="EN-US">In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.</span></p>

Pipe Fault Prediction for Water Transmission Mains

Every network of supply waterlines experiences thousands of yearly bursts, breaks, leakages, and other failures. These failures waste a great amount of resources, as not only the waterlines need to be repaired, but also water is wasted and the distribution service is interrupted. For that reason, many water facilities employ proactive maintenance strategies in their networks, where they replace likely-to-fail pipes in advance to prevent the failures. In this paper, we aim to establish a reliable prediction model that can accurately predict faults in waterlines prior to their occurrence. We propose a specific segmentation method for long transmission mains, as well as three data-driven models and one rule-based prediction model. We evaluate a real world waterline network used in Israel, operated by Mekorot company, using three common metrics. The results show that the data-driven algorithms outperform the rule-based model by at least 5% in each of the metrics. Additionally, their prediction becomes more accurate as they are trained with more data, but enhancing these data with geographically related features does not improve the accuracy further.

A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization

The segmentation process is considered the significant step of an image processing system due to its extreme inspiration on the subsequent image analysis. Out of various approaches, thresholding is one of the most popular schemes for image segmentation. In segmentation, image pixels are arranged in various regions based on their intensity levels. In this paper, a straightforward and efficient fusion-based fuzzy model for multilevel color image segmentation using grasshopper optimization algorithm (GOA) has been proposed. Thresholding based segmentation lacks accuracy in segmenting the ambiguous images due to their complex characteristics, uncertainties and inherent fuzziness. However, the fuzzy entropy resolves these problems, but it is unable for segmenting at higher levels and also the complexity level for selecting suitable thresholds is high. The selection of metaheuristic GOA reduces this problem by selecting optimal threshold values. Therefore, to increase the quality of the segmented image, a simple and effective multilevel thresholding method is exploited by using the concept of fusion which is based on the local contrast. Experimental outputs demonstrate that fusion-based multilevel thresholding is better than most specific segmentation methods and can be validated by comparing the different numerical parameters. Experiments on standard daily-life color and satellite images are conducted to prove the effectiveness of the proposed scheme.

A review on brain structures segmentation in magnetic resonance imaging.

Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches. We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods. We further discuss each category's strengths and weaknesses and analyze its performance in segmenting different brain structures providing a qualitative and quantitative comparison. We compare the results of the analyzed works for the following brain structures: hippocampus, thalamus, caudate nucleus, putamen, pallidum, amygdala, accumbens, lateral ventricles, and brainstem. The structures on which more works have focused on are the hippocampus and the caudate nucleus. In general, the accumbens (0.69 mean DSC) is the most difficult structure to segment whereas the structures that seem to get the best results are the brainstem, closely followed by the thalamus and the putamen with 0.88, 0.87 and 0.86 mean DSC, respectively. Atlas-based approaches achieve good results when segmenting the hippocampus (DSC between 0.75 and 0.90), thalamus (0.88-0.92) and lateral ventricles (0.83-0.93), while deformable methods perform good for caudate nucleus (0.84-0.91) and putamen segmentation (0.86-0.89). There is not yet a single automatic segmentation approach that can emerge as a standard for the clinical practice, providing accurate brain structures segmentation. Future trends need to focus on combining multi-atlas methods with learning-based or deformable approaches. Employing atlases to provide spatial robustness and modeling the structures appearance with supervised classifiers or Active Appearance Models could lead to improved segmentation results.