VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

Stroke Lesion Segmentation and Analysis using Entropy/Otsu’s Function – A Study with Social Group Optimization

Background: Stroke is one of the major causes for the momentary/permanent disability in the human community. Usually, stroke will originate in the brain section because of the neurological deficit and this kind of brain abnormality can be predicted by scrutinizing the periphery of brain region. Magnetic Resonance Image (MRI) is the extensively considered imaging procedure to record the interior sections of the brain to support visual inspection process. Objective: In the proposed work, a semi-automated examination procedure is proposed to inspect the province and the severity of the stroke lesion using the MRI. associations while known disease-lncRNA associations are required only. Method: Recently discovered heuristic approach called the Social Group Optimization (SGO) algorithm is considered to pre-process the test image based on a chosen image multi-thresholding procedure. Later, a chosen segmentation procedure is considered in the post-processing section to mine the stroke lesion from the pre-processed image. Results: In this paper, the pre-processing work is executed with the well known thresholding approaches, such as Shannon’s entropy, Kapur’s entropy and Otsu’s function. Similarly, the postprocessing task is executed using most successful procedures, such as level set, active contour and watershed algorithm. Conclusion: The proposed procedure is experimentally inspected using the benchmark brain stroke database known as Ischemic Stroke Lesion Segmentation (ISLES 2015) challenge database. The results of this experimental work authenticates that, Shannon’s approach along with the LS segmentation offers superior average values compared with the other approaches considered in this research work.</P>

Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

Printed media is still popular now days society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR) system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extraction, classifier, model and recognition. In preprocessing, the median filter is used to clarify the image from noise and the Otsu’s function is used to binarize the image. It followed by character segmentation using connected component labeling. Artificial neural network (ANN) is used for feature extraction to recognize the character. The result shows that this system enable to recognize the characters in the image whose success rate is influenced by the training of the system.

A Global Multilevel Thresholding Using Differential Evolution Approach

Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.