Novel 2-D histogram based Soft thresholding for Brain Tumor Detection and Image Compression

The objective of image compression is to extract meaningful clusters in a given image. Significant groups are possible with absolute threshold values. 1-D histogram-based multilevel thresholding is computationally complex and reconstructed image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. So, 2-D histogram-based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel hybrid Genetic Algorithm, Particle Swarm Optimization and Symbiotic Organisms Search (hGAPSO-SOS), and the obtained results are compared with state of the art optimization techniques. Recent study reveals that PSNR fails in measuring the visual quality because of mismatch with the objective mean opinion scores (MOS). So, we incorporate a weighted PSNR (WPSNR) and visual PSNR (VPSNR). Experimental results examined on Magnetic Resonance images of brain, and results with 2-D histogram reveal that hGAPSO-SOS method can be efficiently and accurately used in multilevel thresholding problem.

Macular Hole Detection Using a New Hybrid Method: Using Multilevel Thresholding and Derivation on Optical Coherence Tomographic Images

Optical coherence tomography (OCT) is a noninvasive imaging test. OCT imaging is analogous to ultrasound imaging, except that it uses light instead of sound. In this type of image, microscopic quality intratissue images are provided. In addition, fast and direct imaging of tissue morphology and reproducibility of results are the advantages of this imaging. Macular holes are a common eye disease that leads to visual impairment. The macular perforation is a rupture in the central part of the retina that, if left untreated, can lead to vision loss. A novel method for detecting macular holes using OCT images based on multilevel thresholding and derivation is proposed in this paper. This is a multistep method, which consists of segmentation, feature extraction, and feature selection. A combination of thresholding and derivation is used to diagnose the macular hole. After feature extraction, the features with useful information are selected and finally the output image of the macular hole is obtained. An open-access data set of 200 images with the size of 224 × 224 pixels from Sankara Nethralaya (SN) Eye Hospital, Chennai, India, is used in the experiments. Experimental results show better-diagnosing results than some recent diagnosing methods.

Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy

Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Kapur's entropy is considered as its objective function. In the FaFA, a fuzzy logical controller is designed to adjust the control parameters. A total of six satellite remote sensing color images are conducted in the experiments. The performance of the FaFA is compared with FA, BWO, SSA, NaFA and ODFA. Some measure metrics are performed in the experiments. The experimental results show that the FaFA obviously outperforms other five algorithms.