Adaptive Snake Research Articles

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Photovoltaic (PV) systems play an increasingly vital role in the global new energy landscape. However, when PV arrays are affected by partial shading, their power-voltage (P-U) characteristic curves exhibit dynamic multi-peak features, posing challenges to traditional Maximum Power Point Tracking (MPPT) techniques, including slow convergence speed, low tracking efficiency, and oscillations around the Maximum Power Point (MPP), leading to unnecessary power losses. Therefore, this study proposes a dual-layer control MPPT technology that integrates dynamic adaptive snake optimization algorithm with variable step perturbation and observation (ISO-IP&O) to address the MPP extraction problem of PV systems under complex weather conditions. This study compares and analyzes the proposed ISO-IP&O controller with current renowned MPPT techniques, including Grey Wolf Optimization combined with P&O (GWO-P&O), Improved Squirrel Search Algorithm (ISSA), Original Snake Optimization (SO), Horse Herd Optimization (HHO), and Adaptive Factor Selection Multi-Swarm Particle Swarm Optimization (FMSPSO), validating the effectiveness of the proposed ISO-IP&O controller. Simulation results demonstrate that the ISO-IP&O technology achieves an average tracking efficiency of 99.91 % under various shading conditions, with an average convergence speed of only 0.07 seconds. Compared with other existing methods, the ISO-IP&O technology exhibits superior performance in terms of GMPP tracking speed, maximum power tracking efficiency, and stability. Experimental validation on the HIL+RCP physical experimental platform shows that under eight partial shading conditions(PSC), the proposed method still achieves the highest average tracking efficiency of 99.68 %, with the fastest average tracking speed of 0.66 seconds. The ISO-IP&O controller achieves rapid, robust, and efficient GMPP tracking under various complex shading conditions, contributing to the enhancement of PV system performance.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Adaptive snake optimization-enabled deep learning-based multi-classification using leaf images

Adaptive snake optimization-enabled deep learning-based multi-classification using leaf images

An Adaptive Snake Based Shadow Segmentation for Robust Driver Fatigue Detection: A 3D Facial Feature Based Photometric Stereo Perspective

An Adaptive Snake Based Shadow Segmentation for Robust Driver Fatigue Detection: A 3D Facial Feature Based Photometric Stereo Perspective

Antivenomics and in vivo preclinical efficacy of six Latin American antivenoms towards south-western Colombian Bothrops asper lineage venoms.

BackgroundBothrops asper represents the clinically most important snake species in Central America and Northern South America, where it is responsible for an estimated 50–80% of snakebites. Compositional variability among the venom proteomes of B. asper lineages across its wide range mirrors clinical differences in their envenomings. Bothropic antivenoms generated in a number of Latin American countries commonly exhibit a certain degree of paraspecific effectiveness in the neutralization of congeneric venoms. Defining the phylogeographic boundaries of an antivenom's effectivity has implications for optimizing its clinical use. However, the molecular bases and impact of venom compositions on the immune recognition and neutralization of the toxic activities of across geographically disparate populations of B. asper lineages has not been comprehensively studied.Methodology/Principal findingsThird-generation antivenomics was applied to quantify the cross-immunorecognizing capacity against the individual components of venoms of three B. asper lineages (B. asper (sensu stricto), B. ayerbei and B. rhombeatus) distributed in south-western (SW) Colombia, of six Latin American antivenoms, produced against homologous (Colombia, INS-COL and PROBIOL) and Costa Rica (ICP)), and heterologous (Argentina (BIOL), Perú (INS-PERU) and Venezuela (UCV)) bothropic venoms. In vivo neutralization assays of the lethal, hemorrhagic, coagulant, defibrinogenating, myotoxic, edematogenic, indirect hemolytic, and proteolytic activities of the three SW Colombian B. asper lineage venoms were carried to compare the preclinical efficacy of three (Colombian INS-COL and PROBIOL, and Costa Rican ICP) antivenoms frequently used in Colombia. Antivenomics showed that all the six antivenom affinity matrices efficiently immunoretained most of the B. asper lineages venom proteins and exhibited impaired binding towards the venoms' peptidomes. The neutralization profile of the INS-COL, PROBIOL and ICP antivenoms towards the biological activities of the venoms of SW Colombian B. asper (sensu stricto), B. ayerbei and B. rhombeatus lineages was coherent with the antivenomics outcome. In addition, the combination of in vitro (antivenomics) and in vivo neutralization results allowed us to determine their toxin-specific and venom neutralizing antibody content. Noteworthy, heterologous INS-PERU, BIOL, and UCV bothropic antivenoms had equal or higher binding capacity towards the venoms components of SW Colombian B. asper lineages that the homologous Colombian and Costa Rican antivenoms.Conclusions/SignificanceThe combined in vitro and in vivo preclinical outcome showed that antivenoms manufactured in Colombia and Costa Rica effectively neutralize the major toxic activities of SW Colombian B. asper lineage venoms. The antivenomics profiles of the heterologous antivenoms manufactured in Argentina, Venezuela, and Perú strongly suggests their (pre)clinical adequacy for the treatment of B. asper lineage envenomings in SW Colombia. However, their recommendation in the clinical setting is pending on in vivo neutralization testing and clinical testing in humans. Bothrops asper is a highly adaptable snake species complex, which is considered the most dangerous snake throughout much of its distribution range from the Atlantic lowland of eastern México to northwestern Perú. Antivenoms are the only scientifically validated treatment of snakebite envenomings. Venom variation is particularly common in wide ranging species, such as B. asper, and may result in variable clinical presentations of envenomings, as is the case for the B. asper species complex, potentially undermining the efficacy of snakebite treatments depending on the immunization mixture used in the generation of the antivenom. Conversely, phylogenetic conservation of antigenic determinants confers an unpredictable degree of paraspecificity to homologous antivenoms produced for a geographic area, but also to heterologous congeneric antivenoms, towards the venom components of allopatric conspecific populations. This work aimed at comparing the preclinical profile of a panel of Latin American homologous and heterologous antivenoms against the venoms of B. asper lineages distributed in SW Colombia. The outcome of this study strongly suggests the suitability of considering the heterologous antivenoms BIOL (Argentina), UCV (Venezuela) and INS-PERU (Perú) as alternatives to homologous Colombian INS-COL and PROBIOL and Costa Rican ICP antivenoms for the treatment of envenomings by B. asper (sensu stricto) in W Colombia and Ecuador, B. ayerbei in Cauca and Nariño (Colombia), and B. rhombeatus in Cauca river valley, SW Colombia.

Grape leaf segmentation for disease identification through adaptive Snake algorithm model

Fruits are the eminent export agriculture product for any country, especially grape, it is used for making wines and developing raisins. Moreover, Viticulture has proven to be one of the highly profitable industry from the economic point of view. However, in viticulture grape quality should be of top-notch quality, moreover throughout the research it is observed that grape quality is degraded mainly due to the plant disease. In past several researcher have tried their efforts to detect the early identification of disease. Segmentation plays bigger role identifying the disease, Hence in this paper we propose an Adaptive Snake Model for segmentation and region identification. ASA (Adaptive Snake Model) is two phase segmentation model namely common segmentation and absolute segmentation. Through common segmentation, we achieve the fast segmentation and through the absolute segmentation, we achieve the better accuracy. Moreover for evaluation of Adaptive Snake Algorithm two standard dataset i.e. PlantLevel and PlantVillage dataset, for further evaluation we have compared with various state-of-art technique in terms of various performance metric such as PSNR, Dice, Manhattan, Recall and Jaccard. Adaptive Snake Algorithm performs better than the other existing methodology.

Design and Development of a Wheel-less Snake Robot with Active Stiffness Control for Adaptive Pedal Wave Locomotion

This paper presents the design and manufacture process of a wheel-less, modular snake robot with series elastic actuators to reliably measure motor torque signal and investigate the effectiveness of active stiffness control for achieving adaptive snake-like locomotion. A polyurethane based elastic element to be attached between the motor and the links at each joint was designed and manufactured using water jet cutter, which makes the final design easier to develop and more cost-effective, compared to existing snake robots with torque measurement capabilities. The reliability of such torque measurement mechanism was examined using simulated dynamical model of pedal wave motion, which proves the efficacy of the design. A distributed control system was also designed, which with the help of an admittance controller, enables active control of the joint stiffness to achieve adaptive snake robot pedal wave locomotion to climb over obstacles, which unlike existing methods does not require prior information about the location of the obstacle. The effectiveness of the proposed controller in comparison to open-loop control strategy was verified by the number of experiments. The results show the capability of the robot to successfully climb over obstacles with the height of more than 55% of the diameter of the snake robot modules.

Automatic Balloon Snake Method for Topology Adaptive Water Boundary Extraction: Using GF-1 Satellite Imagery as an Example

Topology adaptive boundary extraction for water in satellite images using parametric snakes remains challenging in the domain of image segmentation due to the inability of topology inflexible parametric snakes (TIPS) to split and merge curves and the limited accuracy or inefficiency of topology flexible parametric snakes (TFPS). Based on the balloon snake (B-snake) method, an outstanding representative of TIPS, this paper proposed the automatic B-snake (AB-snake) method capable of automatic initialization and topology transformation to overcome the drawbacks of nonautomatic initialization and topology inflexibility of the B-snake method while ensuring both high accuracy and efficiency of TFPS. Six GF-1 wide field view satellite images covering different inner island numbers, water body sizes, boundary complexities, boundary clarities, and background complexities were used as experimental images. To test its feasibility and advantages, the extracted results of the AB-snake method were compared with those of the B-snake and the orthogonal topology adaptive snake (OT-snake) methods, a newer representative of TFPS. The results demonstrated that the AB-snake method could effectively implement automatic initialization and topology transformation, successfully meeting its design objectives. Four indices, including correctness, completeness, area overlap measure, and efficiency, were adopted to quantitatively assess the performances of the B-snake, the AB-snake, and the OT-snake methods. The results suggested that the proposed AB-snake method not only significantly outperformed the B-snake method in topology collision handling but also demonstrated great advantages over the OT-snake method in accuracy. The applicability, sensitivity, parameter selection, and limitations of the AB-snake method were also elaborated.

Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example

Topology adaptive water boundary extraction from satellite images using parametric snakes remains challenging in the domain of image segmentation. This paper proposed a modified balloon snake (MB-Snake) method based on the balloon snake (B-Snake) method, overcoming the B-Snake’s drawbacks of inaccurate positioning, topology inflexibility, and non-automatic contour evolution termination. Six satellite images, acquired by the GF-1 wide field of view sensor and with water bodies of different types, inner land numbers, areas, boundary and background complexities, and digital number value contrasts, were used as experimental images, in which the MB-Snake method, and two comparison methods, the B-Snake and the orthogonal topology adaptive snake (OT-Snake) methods, were applied for water boundary extraction. All the extracted results were first qualitatively assessed and further quantitatively evaluated via three indexes, including correctness, completeness, and area overlap measure. Both of the qualitative and quantitative evaluation results consistently demonstrated that the MB-Snake method can efficiently improve the positioning accuracy, detect and dispose of topology collisions, and perform automatic contour evolution termination, successfully meeting its design objectives, and exhibiting great superiority to the existing topology-flexible parametric snakes. The sensitivity to initial contours, the effects of model parameters, and spatial resolutions of satellite images, and image demands of the MB-Snake method was also analyzed.

Rectification-conducted adaptive snake for segmenting complex-boundary objects from textured backgrounds

In this paper, an improved contour-fitting adaptive snake, namely a rectification-conducted adaptive snake (RCA-snake) is proposed for segmenting complex-boundary objects from the textured background. Based on the snaxels’ initialization, the RCA-snake comprises two steps. Initially, edge-conducted evolution (ECE) is employed for adaptations of model coefficients that accommodate ECE itself to the characteristics of salient edges for enhancing curve fitting in tracking. Following ECE, direction-induced rectification evolution corrects the boundary-unmatched snake fragments by handling the initializations of their snake-force direction and tensile-force weighting. The two steps of the RCA-snake are coordinated to enhance control of the snake model for segmenting an object with high-curvature boundaries. Simulation results demonstrate that better object-boundary coincidence can be obtained via the RCA-snake than other snake models, such as the gradient vector flow (GVF) and improved GVF, e.g., NGVF, in segmenting a complex-boundary object from textured-background images.

Texture Analysis and Modified Level Set Method for Automatic Detection of Bone Boundaries in Hand Radiographs

Rheumatoid Arthritis (RA) is a chronic inflammatory joint disease characterized by a distinctive pattern of bone and joint destruction. To give an RA diagnosis, hand bone radiographs are taken and analyzed. A hand bone radiograph analysis starts with the bone boundary detection. It is however an extremely exhausting and time consuming task for radiologists. An automatic bone boundary detection in hand radiographs is thus strongly required. Garcia et al. have proposed a method for automatic bone boundary detection in hand radiographs by using an adaptive snake method, but it doesn’t work for those affected by RA. The level set method has advantages over the snake method. It however often leads to either a complete breakdown or a premature termination of the curve evolution process, resulting in unsatisfactory results. For those reasons, we propose a modified level set method for detecting bone boundaries in hand radiographs affected by RA. Texture analysis is also applied for distinguishing the hand bones and other areas. Evaluating the experiments using a particular set of hand bone radiographs, the effectiveness of the proposed method has been proved.

A segmentation framework for abdominal organs from CT scans

Computed tomography images are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies, and the 3D volume rendering of these abdominal organs. Their automatic segmentation is the first and fundamental step in all these studies, but it is still an open problem. In this paper we propose a fully automatic, gray-level based segmentation framework based on a multiplanar fast marching method. The proposed segmentation scheme is general, and employs only established and not critical anatomical knowledge. For this reason, it can be easily adapted to segment different abdominal organs, by overcoming problems due to the high inter- and intra-patient gray-level, and shape variabilities; the extracted volumes are then combined to produce the final results. The system has been evaluated by computing the symmetric volume overlap (SVO) between the automatically segmented (liver and spleen) volumes and the volumes manually traced by radiological experts. The test dataset is composed of 60 images, where 40 images belong to a private dataset, and 20 images to a public one. Liver segmentation has achieved an average SVO congruent with94, which is comparable to the mean intra- and inter-personal variation (96). Spleen segmentation achieves similar, promising results (SVO congruent with93). The comparison of these results with those achieved by active contour models (SVO congruent with90), and topology adaptive snakes (SVO congruent with92) proves the efficacy of our system. The described segmentation method is a general framework that can be adapted to segment different abdominal organs, achieving promising segmentation results. It has to be noted that its performance could be further improved by incorporating shape based rules.

Spatially adaptive active contours: a semi-automatic tumor segmentation framework

Tumor segmentation constitutes a crucial step in simulating cancer growth and response to therapy. Incorporation of imaging data individualizes the simulation and assists clinical correlation with the predicted outcome. We adapted snakes to improve tumor segmentation including difficult cases with inherently inhomogeneous structure and poorly defined margins. Snakes are flexible curves, based on the parameter-controlled deformation of an initial user-defined contour toward the boundary of the desired object, through the minimization of a suitable energy function. Although parameter-adjustment can yield fairly good results in homogeneous regions, traditional snakes often fail to provide an accurate segmentation result when both rigid and very elastic behavior is needed simultaneously to delineate the true outline of the tumor. We developed and tested a spatially adaptive active contour technique by introducing local snake bending, to improve traditional snakes performance for segmenting tumors. The key point of our method is the use of adaptable snake parameters, instead of constant ones, to adjust the bending of the curve according to the local edge characteristics. Our algorithm discriminates image regions according to underlying image features, such as gradient magnitude and corner strength. More specifically, it assigns each region a different "localized" set of parameters, one corresponding to a very flexible snake, and the other corresponding to a very rigid one, according to the local image characteristics. Qualitative results on more than 150 real MR images, as well as quantitative validation based on agreement with an expert clinician's annotations of the true tumor boundaries, demonstrate our approach is highly efficient compared to traditional active contours and region growing. Due to the use of adaptable parameters in the snake evolution process, our approach outperforms the other two methods, and consistently follows an expert's annotations. Statistical tests indicated significant difference between the results produced by our approach and two other algorithms traditional snakes and region growing, while multiple comparison showed that our method consistently outperformed those algorithms, with an average overlap of 89%, over the entire data set, while traditional snakes were at 82.5% and region growing at 59.2%. Furthermore, we performed several tests that demonstrate our method's stability to different initial contours, as well as, to lower resolution images. Our adaptive snake algorithm can spatially adapt to diverse image characteristics, producing outlines that mimic the true tumor boundaries. Results in MR datasets are very close to an expert clinician's intuition about the tumor boundaries.

An intensive restraint topology adaptive snake model and its application in tracking dynamic image sequence

The aim of this work is to propose an improved intensive restraint topology adaptive snake model for detecting and tracking target contours with complex topological morphology from dynamic gray-scale image sequences. The image is discretized by orthogonal equidistant grid lines and snake nodes are constrained to move only from one grid vertex to another along grid lines. Thus, the computation is much simpler than those of other T-snakes since complicated calculation of intersection points of snake curve and grid lines is not required. Topological transformation is implemented through splitting snake nodes as well as automatically detecting and eliminating topological collision. An adaptive weighted expansion energy term is introduced into snake’s energy function driving the model to pass across the region with non-uniform intensity distribution inside the target region. The required manual interaction is reduced to only select a random point within the target region in the first frame of the image sequence. For subsequent frames, the detection result in the previous frame is regarded as the initial model in the current frame. Through experiments on simulated image data and clinical X-ray coronary angiographic sequences, the model’s topological flexibility and lower computation cost compared with other T-snakes are demonstrated. Also, the sensitivity of the model to weights of the energy terms and grid size as well as the initial plan of the model is discussed based on experimental results.

Graphical processing unit–based machine vision system for simultaneous measurement of shrinkage and soil release in fabrics

We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.

Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images

In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

Surface reconstruction of the coronary arterial walls from intravascular ultrasound images

Three-dimensional surface reconstruction of the coronary arterial from intravascular ultrasound (IVUS) images plays an important role in diagnosis and treatment of the atherosclerosis. A simplified 3-D topologically adaptable snake model (3-D T-snake) is proposed; nevertheless, it can successfully address the significant problem of conventional T-snakes when dealing with the model self-intersections. A method of surface reconstruction of IVUS coronary arterial walls based on the simplified 3-D T-snake is presented. The experimental results indicate that the method is accurate and robust, and the simplified 3-D T-Snake is feasible and valid.

Corrections to “Adaptive Snakes Using the EM Algorithm”

Corrections to “Adaptive Snakes Using the EM Algorithm”

Semiautomatic segmentation of the cochlea using real-time volume rendering and regional adaptive snake modeling.

The human cochlea in the inner ear is the organ of hearing. Segmentation is a prerequisite step for 3-dimensional modeling and analysis of the cochlea. It may have uses in the clinical practice of otolaryngology and neuroradiology, as well as for cochlear implant research. In this report, an interactive, semiautomatic, coarse-to-fine segmentation approach is developed on a personal computer with a real-time volume rendering board. In the coarse segmentation, parameters, including the intensity range and the volume of interest, are defined to roughly segment the cochlea through user interaction. In the fine segmentation, a regional adaptive snake model designed as a refining operator separates the cochlea from other anatomic structures. The combination of the image information and expert knowledge enables the deformation of the regional adaptive snake effectively to the cochlear boundary, whereas the real-time volume rendering provides users with direct 3-dimensional visual feedback to modify intermediate parameters and finalize the segmentation. The performance is tested using spiral computed tomography (CT) images of the temporal bone and compared with the seed point region growing with manual modification of the commercial Analyze software. Our method represents an optimal balance between the efficiency of automatic algorithm and the accuracy of manual work.