Salp Swarm Algorithm with Multilevel Thresholding Based Brain Tumor Segmentation Model

Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.

Hand segmentation from a single depth image based on histogram threshold selection and shallow CNN

Real-time hand gesture recognition technology significantly improves the user's experience for virtual reality/augmented reality(VR/AR) applications, which relies on the identification of the orientation of the hand in captured images or videos. A new three-stage pipeline approach for fast and accurate hand segmentation for the hand from a single depth image is proposed. Firstly, a depth frame is segmented into several regions by histogrambased threshold selection algorithm and by tracing the exterior boundaries of objects after thresholding. Secondly, each segmentation proposal is evaluated by a three-layers shallow convolutional neural network(CNN) to determine whether or not the boundary is associated with the hand. Finally, all hand components are merged as the hand segmentation result. Compared with algorithms based on random decision forest(RDF), the experimental results demonstrate that the approach achieves better performance with high-accuracy(88.34% mean intersection over union, mIoU) and a shorter processing time(≤8 ms).

Novel Method to Determine the Image Segmentation Threshold during the Quantitative Test on Meso-structure of Geo-material

As a kind of special material in geotechnical engineering, the mudded weak interlayer plays a crucial part in slope stability. In this paper, we presented a method to determine the threshold value of section micrographs of the mudded weak interlayer in slope during its meso-structure qualification process. Some soil tests, scanning electron microscopy(SEM) and image segmentation technology were performed to fulfill our purpose. Specifically, the relation between 3 D-porosity and the threshold was obtained by least square fitting of the threshold-porosity curves and a simplified pore equivalent model. Using this relation and the 3 D-porosity determined by soil experiments, we can figure out the polynomial equation of the threshold value. The threshold values obtained by the other existing methods in literature were employed to validate our present results.

A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation

Thresholding is a popular image segmentation method that converts gray-level image into binary image. The selection of optimum thresholds has remained a challenge over decades. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this paper, a new intelligence algorithm, particle swarm opti-mization (PSO), is presented for multilevel thresholding in image segmentation. This algorithm is used to maximize the Kapur’s and Otsu’s objective functions. The performance of the PSO has been tested on ten sample images and it is found to be superior as compared with genetic algorithm (GA).

Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new segmentation method that combines the advan-tages of fuzzy C mean algorithm, thresholding and level set method. 3-class Fuzzy C mean thresholding is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. Parameters for performance evaluation are presented and segmentation results are compared with some other state-of-the-art segmentation methods. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of proposed method for skin cancer detection.

Segmentation of Vessels by Morphological Filters and Dynamic Thresholding

A method of segmenting vessels by morphological filters and dynamic thresholding for digital subtraction angiography (DSA) images is presented. The first step is to reduce the noise and enhance the details of image by using morphological operators. The second is to segment vessels by dynamic thresholding combined with global thresholding based on the properties of DSA images. Artificial images and actual images have been tested. Experiment results show that the proposed method is efficient and is of great potential for the segmentation of vessels in medical images.

Quantum watermarking based on threshold segmentation using quantum informational entropy

We propose a new quantum watermarking scheme based on threshold selection using informational entropy of quantum image.The core idea of this scheme is to embed information into object and background of cover image in different ways.First,a threshold method adopting the quantum informational entropy is employed to determine a threshold value.The threshold value can then be further used for segmenting the cover image to a binary image,which is an authentication key for embedding and extraction information.By a careful analysis of the quantum circuits of the scheme,that is,translating into the basic gate sequences which show the low complexity of the scheme.One of the simulation-based experimental results is entropy difference which measures the similarity of two images by calculating the difference in quantum image informational entropy between watermarked image and cover image.Furthermore,the analyses of peak signal-to-noise ratio,histogram and capacity of the scheme are also provided.

The Method of Flotation Froth Image Segmentation Based on Threshold Level Set

A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis;we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.

A Restricted, Adaptive Threshold Segmentation Approach for Processing High-Speed Image Sequences of the Glottis

In this paper, we propose a restricted, adaptive threshold approach for the segmentation of images of the glottis acquired from high speed video-endoscopy (HSV). The approach involves first, identifying a region of interest (ROI) that encloses the vocal-fold motion extent for each image frame as estimated by the different image sequences. This procedure is then followed by threshold segmentation restricted within the identified ROI for each image frame of the original image sequences, or referred to as sub-image sequences. The threshold value is adapted for each sub-image frame and determined by respective minimum gray-scale value that typically corresponds to a spatial location within the glottis. The proposed approach is practical and highly efficient for segmenting a vast amount of image frames since simple threshold method is adapted. Results obtained from the segmentation of representative clinical image sequences are presented to verify the proposed method.

A novel stepwise thresholding for fuzzy image segmentation

A novel stepwise thresholding method for fuzzy image segmentation is proposed. Unlike the published iterative or recursive thresholding mehtods, this method segments regions into sub-regions iteratively by increasing threshold value in a stepwise manner, based on a preset intensity homogeneity criteria. The method is particularly suited to segmentation of the laser scanning confocal microscopy (LSCM) images, computerised tomography (CT) images, magnetic resonance (MR) images, fingerprint images, etc. The method has been tested on some typical fuzzy image data sets. In this paper, the novel stepwise thresholding is first addressed. Next a new method of region labelling for region extraction is introduced. Then the design of intensity homogeneity segmentation criteria is presented. Some examples of the experiment results of fuzzy image segmentation by the method are given at the end.

A novel image segmentation approach for wood plate surface defect classification through convex optimization

Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy.

An improved binarization algorithm of wood image defect segmentation based on non-uniform background

In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

An Efficient Liver-Segmentation System Based on a Level-Set Method and Consequent Processes

This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.

Review of Theory and Methods of Image Segmentation

Image segmentation refers to the technique and process of partitioning a digital image into multiple segments based on image characteristics so as to extract the object of interest from it. It is a key step from image processing to image analysis. In the mid-1950 s,people began to study image segmentation. For decades,various methods for image segmentation have been proposed. In this paper,traditional image segmentation methods and some new methods appearing in recent years were reviewed. Thresholding segmentation methods,region-based,edge detection-based and segmentation methods based on specific theoretical tools were introduced in detail.

Robust video foreground segmentation and face recognition

Face recognition provides a natural visual interface for human computer interaction(HCI) applications.The process of face recognition,however,is inhibited by variations in the appearance of face images caused by changes in lighting,expression,viewpoint,aging and introduction of occlusion.Although various algorithms have been presented for face recognition,face recognition is still a very challenging topic.A novel approach of real time face recognition for HCI is proposed in the paper.In view of the limits of the popular approaches to foreground segmentation,wavelet multi-scale transform based background subtraction is developed to extract foreground objects.The optimal selection of the threshold is automatically determined,which does not require any complex supervised training or manual experimental calibration.A robust real time face recognition algorithm is presented,which combines the projection matrixes without iteration and kernel Fisher discriminant analysis(KFDA) to overcome some difficulties existing in the real face recognition.Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments.The proposed algorithm can also be applied to the video image sequences of natural HCI.

Gravel Image Segmentation in Noisy Background Based on Partial Entropy Method

Because of wide variation in gray levels and particle dimensions and the presence of many small gravel objects in the background, as well as corrupting the image by noise, it is difficult to segment gravel objects. In this paper, we develop a partial entropy method and succeed to realize gravel objects segmentation. We give entropy principles and fast calculation methods. Moreover, we use minimum entropy error automaticly to select a threshold to segment image. We introduce the filter method using mathematical morphology. The segment experiments are performed by using different window dimensions for a group of gravel image and demonstrates that this method has high segmentation rate and low noise sensitivity.