Multilevel Thresholding Approach Research Articles

Multi-level thresholding based on differential evolution and Tsallis Fuzzy entropy

This paper presents a multilevel image thresholding approach which relies on Tsallis entropy using Fuzzy partition with a novel threshold selection technique. In order to compute the optimal threshold values, Differential Evolution (DE) has been employed. The proposed method can further be exploited in image segmentation which is considered to be a critical step in image processing. Our proposed threshold selection technique is based on Tsallis-Fuzzy entropy and the results are compared with Shannon entropy (or fuzzy entropy) and Tsallis entropy based existing threshold selection techniques. The experiments are performed on two different sets of images and the results have been compared with that of existing state-of-the-art methods, namely, Patch Levy Bees' Algorithm (PLBA), Bacterial Foraging optimization (BFO), modified Bacterial Foraging optimization (MBFO) and Bees' Algorithm (BA). Quantitative analysis is carried out based on three image quality metrics viz SSIM, PSNR and SNR. Standard deviation and CPU time for convergence of the objective function have been calculated for performance evaluation. Furthermore, the statistical significance of our method has been estimated using Friedman test and Wilcoxon test. The experimental results manifest that our method produces results superior to the methods in comparison.

Automated identification of normoblast cell from human peripheral blood smear images.

In this paper, we have presented a new computer-aided technique for automatic detection of nucleated red blood cells (NRBCs) or normoblast cell from peripheral blood smear image. The proposed methodology initiates with the localization of the nucleated cells by adopting multilevel thresholding approach in smear images. A novel colour space transformation technique has been introduced to differentiate nucleated blood cells [white blood cells (WBCs) and NRBC] from red blood cells (RBCs) by enhancing the contrast between them. Subsequently, special fuzzy c-means (SFCM) clustering algorithm is applied on enhanced image to segment out the nucleated cell. Finally, nucleated RBC and WBC are discriminated by the random forest tree classifier based on first-order statistical-based features. Experimentally, we observed that the proposed technique achieved 99.42% accuracy in automatic detection of NRBC from blood smear images. Further, the technique could be used to assist the clinicians to diagnose a different anaemic condition.

A New Fuzzy Firefly Algorithm with Adaptive Parameters

Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive firefly algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix

This paper introduces gray-level co-occurrence matrix (GLCM) based color image Segmentation.Cuckoo search (CS) is used in order to effectively enhance the optimal multilevel thresholding.CS based GLCM was found to be more accurate for colored satellite image segmentation.The feasibility of the proposed approach has been tested on various natural and satellite images. Image thresholding is a process that separates particular object within an image from their background. An optimal thresholding technique can be taken as a single objective optimization task, where computation and obtaining a solution can become inefficient, especially at higher threshold levels. In this paper, a new and efficient color image multilevel thresholding approach is presented to perform image segmentation by exploiting the correlation among gray levels. The proposed method incorporates gray-level co-occurrence matrix (GLCM) and cuckoo search (CS) in order to effectively enhance the optimal multilevel thresholding of colored natural and satellite images exhibiting complex background and non-uniformities in illumination and features. The experimental results are presented in terms of mean square error (MSE), peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), computational time (CPU time in seconds), and optimal threshold values for each primary color component at different thresholding levels for each of the test images. In addition, experiments are also conducted on the Berkeley Segmentation Dataset (BSDS300), and four performance indices of image segmentation- Probability Rand Index (PRI), Variation of Information (VoI), Global Consistency Error (GCE), and Boundary Displacement Error (BDE) are tested. To evaluate the performance of proposed algorithm, other optimization algorithm such as artificial bee colony (ABC), bacterial foraging optimization (BFO), and firefly algorithm (FA) are compared using GLCM as an objective function. Moreover, to show the effectiveness of proposed method, the results are compared to existing context sensitive multilevel segmentation techniques based on Tsalli's entropy. Experimental results showed the superiority of proposed technique in terms of better segmentation results with increased number of thresholds.

A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images

Multilevel thresholding is one of the most popular image segmentation techniques due to its simplicity and accuracy. Most of the thresholding approaches use either the histogram of an image or information from the grey-level co-occurrence matrix (GLCM) to compute the threshold. The medical images like MRI usually have vague boundaries and poor contrast. So, segmenting these images using solely histogram or texture attributes of GLCM proves to be insufficient. This paper proposes a novel multilevel thresholding approach for automatic segmentation of tumour lesions from magnetic resonance images. The proposed technique exploits both intensity and edge magnitude information present in image histogram and GLCM to compute the multiple thresholds. Subsequently, using both attributes, a hybrid fitness function has been formulated which can capture the variations in intensity and the edge magnitude present in different tumour groups effectively. Mutation-based particle swarm optimization (MPSO) technique has been used to optimize the fitness function so as to mitigate the problem of high computational complexity existing in the exhaustive search methods. Moreover, MPSO has better exploration capabilities as compared to conventional particle swarm optimization. The performance of the devised technique has been evaluated and compared with two other intensity- and texture-based approaches using three different measures: Jaccard, Dice and misclassification error. To compute these quantitative metrics, experiments were conducted on a series of images, including low-grade glioma tumour volumes taken from brain tumour image segmentation benchmark 2012 and 2015 data sets and real clinical tumour images. Experimental results show that the proposed approach outperforms the other competing algorithms by achieving an average value equal to 0.752, 0.854, 0.0052; 0.648, 0.762, 0.0177; 0.710, 0.813, 0.0148 and 0.886, 0.937, 0.0037 for four different data sets.

An improved multilevel thresholding approach based modified bacterial foraging optimization

In this work, a multilevel thresholding approach that uses modified bacterial foraging optimization (MBFO) is presented for enhancing the applicability and practicality of optimal thresholding techniques. First, the diversity of solutions is considered during the reproduction step. Each weak bacterium randomly selects a strong bacterium from the healthiest bacteria, attempts to reach a location near the chosen strong bacterium, and maintains the same direction. Particle swarm optimization is subsequently incorporated into each chemotactic step to strengthen the global searching capability and quicken the convergence rate of the bacterial foraging algorithm. Finally, the optimal thresholds are obtained by maximizing the Tsallis thresholding functions using the proposed MBFO algorithm. The performance of the proposed algorithm in solving complex stochastic optimization problems is compared with other popular approaches such as a bacterial foraging algorithm, particle swarm optimization algorithm, and genetic algorithm. Experimental results show that the optimal thresholds produced using MBFO require less computation time. The devised algorithm generates more stable results, and the proposed method performs better than the other algorithms in terms of multilevel thresholding. In addition, MBFO method can achieve significantly better results than other compare algorithms on a set of benchmark functions.

CMT: a constrained multi-level thresholding approach for ChIP-Seq data analysis.

Genome-wide profiling of DNA-binding proteins using ChIP-Seq has emerged as an alternative to ChIP-chip methods. ChIP-Seq technology offers many advantages over ChIP-chip arrays, including but not limited to less noise, higher resolution, and more coverage. Several algorithms have been developed to take advantage of these abilities and find enriched regions by analyzing ChIP-Seq data. However, the complexity of analyzing various patterns of ChIP-Seq signals still needs the development of new algorithms. Most current algorithms use various heuristics to detect regions accurately. However, despite how many formulations are available, it is still difficult to accurately determine individual peaks corresponding to each binding event. We developed Constrained Multi-level Thresholding (CMT), an algorithm used to detect enriched regions on ChIP-Seq data. CMT employs a constraint-based module that can target regions within a specific range. We show that CMT has higher accuracy in detecting enriched regions (peaks) by objectively assessing its performance relative to other previously proposed peak finders. This is shown by testing three algorithms on the well-known FoxA1 Data set, four transcription factors (with a total of six antibodies) for Drosophila melanogaster and the H3K4ac antibody dataset.

Multilevel Thresholding Approach Using Modified Bacterial Foraging Optimization

In this work, a multilevel thresholding approach that uses modified bacterial foraging optimization (MBFO) is presented for enhancing the applicability and practicality of optimal thresholding techniques. First, the diversity of solutions is considered during the reproduction step. Each weak bacterium randomly selects a strong bacterium from the healthiest bacteria, attempts to reach a location near the chosen strong bacterium, and maintains the same direction. Particle swarm optimization is subsequently incorporated into each chemotactic step to strengthen the global searching capability and quicken the convergence rate of the bacterial foraging algorithm. Finally, the optimal thresholds are obtained by maximizing the Tsallis thresholding functions using the proposed MBFO algorithm. The performance of the proposed algorithm in solving complex stochastic optimization problems is compared with other popular approaches such as a bacterial foraging algorithm, particle swarm optimization algorithm, and genetic algorithm. Experimental results show that the optimal thresholds produced using MBFO require less computation time. In addition, MBFO method can achieve significantly better segmentation results; the devised algorithm generates more stable results, and the proposed method performs better than the other algorithms in terms of multilevel thresholding.

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR), structural similarity (SSIM) index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy— A Differential Evolution Approach

Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark--the Berkley segmentation data set (BSDS300) with 300 distinct images.

Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images

In this article, we present a new color image segmentation method, based on multilevel thresholding and data fusion techniques which aim at combining different data sources associated to the same color image in order to increase the information quality and to get a more reliable and accurate segmentation result. The proposed segmentation approach is conceptually different and explores a new strategy. In fact, instead of considering only one image for each application, our technique consists in combining many realizations of the same image, together, in order to increase the information quality and to get an optimal segmented image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the most significant peaks of the histogram. For this purpose, an optimal multi-level thresholding is used based on the two-stage Otsu optimization approach. In the second step, the evidence theory is employed to merge several images represented in different color spaces, in order to get a final reliable and accurate segmentation result. The notion of mass functions, in the Dempster-Shafer (DS) evidence theory, is linked to the Gaussian distribution, and the final segmentation is achieved, on an input image, expressed in different color spaces, by using the DS combination rule and decision. The algorithm is demonstrated through the segmentation of medical color images. The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experiments were conducted on an extensive set of color images. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions.

A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance

This paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic scenes. The proposed method identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis techniques. First, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied to effectively extract bright objects of interest. This automatic multilevel thresholding approach provides a robust and adaptable detection system that operates well under various nighttime illumination conditions. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The proposed real-time vision system has also been implemented and evaluated on a TI DM642 DSP-based embedded platform. The system is set up on elevated platforms to perform traffic surveillance on real highways and urban roads. Experimental results demonstrate that the proposed traffic surveillance approach is feasible and effective for vehicle detection and identification in various nighttime environments.

Two-microphone separation of speech mixtures based on interclass variance maximization

Sparse methods for speech separation have become a discussed issue in acoustic signal processing. These sparse methods provide a powerful approach to the separation of several signals in the underdetermined case, i.e., when there are more sources than sensors. In this paper, a two-microphone separation method is presented. The proposed algorithm is based on grouping time-frequency points with similar direction-of-arrival (DOA) using a multi-level thresholding approach. The thresholds are calculated via the maximization of the interclass variance between DOA estimates and allow to identify angular sections, wherein the speakers are located with a strong likelihood. These sections define a set of time-frequency masks that are able to separate several sound sources in realistic scenarios and with little computational cost. Several experiments carried out under different mixing situations are discussed, showing the validity of the proposed approach.

Speech separation based on multi-level segmentation.

Speech separation is still a challenging issue in acoustic signal processing. To deal with this problem, sparse methods are usually employed when there are more sources than sensors (underdetermined problem). In this paper, a two-microphone separation method is presented. The proposed algorithm is based on grouping time-frequency points with similar direction-of-arrival (DOA) using a multi-level thresholding approach. The thresholds are calculated via the maximization of the interclass variance between DOA estimates and allow to identify angular sections wherein the speakers are located with a strong likelihood. Separation is finally performed by means of time-frequency masks. Several experiments conducted under different mixing scenarios are discussed, showing the benefits of the proposed approach.

Automated extraction of PCB components based on specularity using layered illumination

This paper investigates the methodologies for locating and identifying components on a printed circuit board (PCB) used for surface mount device inspection. It's the foundation of other inspections, such as solder joint inspection, component type recognization and so on. The proposed scheme consists of two stages: solder joint extraction and protective coating extraction. This work uses automatic multilevel thresholding approach for detecting specular areas which contain solder joints. Some invalid specular areas, such as markings and via-holes are recognized and removed by comparing the colour distribution features of the target objects and the reference objects. A novel approach based on connection graph and the segmented gray-scale PCB images is developed to classify all recognized solder joints as several clusters. And then, the protective coating is extracted by the positions of the clustered solder joints. Experimental results show that the proposed method can recognize most of components effectively.