In this study, we apply multilevel thresholding segmentation to color images of plant disease. Given that thresholdingsegmentation is just an optimization problem, we use Otsu's function as the objective function...
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In this study, we apply multilevel thresholding segmentation to color images of plant disease. Given that thresholdingsegmentation is just an optimization problem, we use Otsu's function as the objective function. To solve this optimization problem, we implement five metaheuristic algorithms, namely artificial bee colony (ABC), cuckoo search (CS), teaching-learning-based optimization (TLBO), teaching-learning-based artificial bee colony (TLABC) and a modified version of TLABC proposed in this work, known as MTLABC. This version is a hybridization between TLABC and Levy flight where the search equations of TLABC are changed according to Levy flight equations;this modification, based on the experimental results, yields a significant improvement in TLABC. Various numbers of thresholding levels are tried to compare the performance of the optimization algorithms at multiple dimensions. The performance is measured according to five measures: the objective function, CPU time, peak noise-to-signal ratio, structural similarity index and color feature similarity. These measures indicate that our proposed algorithm, with the best values of the measures in most images and levels, ranks first. Also, Friedman and Wilcoxon signed-rank tests are used to analyze the results statistically. These two tests prove that our proposed algorithm is significantly different from the other four algorithms.
In the coastal video image segmentation, images are partitioned into land and sea classes, and each of these classes could have different segmentation qualities. In order to cope with variations in image quality and o...
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ISBN:
(纸本)9781450361064
In the coastal video image segmentation, images are partitioned into land and sea classes, and each of these classes could have different segmentation qualities. In order to cope with variations in image quality and opaque areas, this paper has proposed a multilevel threshold technique based on the Cuckoo Search (CS) algorithm as an optimization algorithm for selecting optimum threshold values. The optimum threshold values are determined by maximizing Otsu's or Kapur's objective function using CS algorithm. The CS algorithm uses McCulloch's method for Levy flight generation and combined with Otsu's and Kapur's objective functions to analyze CS algorithm performance. Based on the evaluations of PSNR, MSE, FSIM and CPU time parameters, the McCulloch's method based on CS algorithm with Otsu's objective function is the most promising and computationally efficient for segmenting coastal video images.
Automated segmentation of vasculatures in retinal images is vital for the detection of Diabetic Retinopathy (DR). An attempt has been made to generate continuous vasculature information using evolutionary based Harmon...
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ISBN:
(纸本)9781479980819
Automated segmentation of vasculatures in retinal images is vital for the detection of Diabetic Retinopathy (DR). An attempt has been made to generate continuous vasculature information using evolutionary based Harmony Search Algorithm (HSA) combined with conventional multilevelthresholding (MLT) methods. The preprocessed normal and abnormal retinal images are segmented using HSA based Otsu and Kapur MLT methods by the best objective functions. The segmentation is validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images and are analyzed. Content Based Image Retrieval (CBIR) is used to assist physicians in clinical diagnoses and research fields. The CBIR systems are developed based on both the MLT segmentation techniques and the obtained features. Similarity matching is carried out between the features of query and database images using the Euclidean Distance measure. Similar images are ranked and retrieved. This work shows high retrieval performances such as precision (96%) and recall (58%) for the CBIR system using HSA based Otsu MLT segmentation method than the other method. Hence this CBIR system could be recommended in computer assisted diagnosis for a better screening of the diabetic retinopathy.
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