Otsu Algorithm Research Articles

An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu's Algorithm.

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

Sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function

Aiming at the difficulty of sugarcane nodes identification and location during automatic cutting of sugarcane seeds, based on machine vision system, this paper proposed a sugarcane nodes identification algorithm based on sum of local pixel of minimum points of vertical projection function. Firstly, according to the color and texture characteristics of yellow sugarcane, the captured RGB image of sugarcane was converted into HSV color image. Then, the S-component image in the HSV image was extracted, and the S-component image was binarized by the Otsu algorithm to obtain the binary image, and then the binary image was processed by morphological closed operation, which can eliminate the noise and fill holes of binary image. Subsequently, the sugarcane area was segmented as the region of interest through the horizontal projection of the binary image, which lowered the amount of calculation while reducing interference. Finally, the vertical projection function of the binary image of the region of interest was established, and the function was continuously derived to obtain the minimum points, and then the position of sugarcane nodes was preliminarily determined by the obtained minimum points. Then, the final position of the sugarcane nodes were determined according to the number of nodes to be identified and the sum of pixels of each 5 columns on both sides of the minimum points. The pixel column positions corresponding to the minima of the sum are the accurate nodes positions determined by the proposed algorithm. The experimental results show that the algorithm proposed in this paper has a single node identification rate of 100%, with an average time consumption of 0.15 s, and a position deviation of less than 0.34 mm; a double nodes identification rate of 98.5%, with an average time consumption of 0.21 s, and a position deviation of less than 0.42 mm. Compared with other nodes identification algorithms mentioned in this paper, it has higher identification rate and accuracy.

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine

Tidal flats are threatened by tidal reclamation and climatic changes around the world. Particular challenges exist in China where tidal flats are changing rapidly along with accelerated economic development in coastal regions. The unique and important ecosystem functions and services that tidal flats provide in coastal regions warrant the necessary of mapping such a particular land cover type in high precision and accuracy. Existing national tidal flat maps of China, which were derived from the 30-m resolution Landsat imagery and auxiliary data, are insufficient to support practical management efforts. In this study, in order to produce an accurate tidal flat map with finer spatial resolution, we employed 28,367 scenes of time series Sentinel-2 images acquired in 2019 and 2020 along the entire coastal line of China. The short revisit cycle (2–5 days) of the Sentinel-2 improved the opportunities of obtaining the highest and lowest tide images, and the finer spatial resolution (10-m) enhanced the capacity of precision tidal flat extraction. A rapid, robust, and automated tidal flat mapping approach is essential to large-scale applications. In this study, we developed an approach by integrating the maximum spectral index composite (MSIC) and the Otsu algorithm (OA), and so named MSIC-OA. By GEE platform, we automated the execution of MSIC-OA to Sentinel-2 images, and produced an up-to-date 10-m spatial resolution tidal flat map of China (China_Tidal Flat, CTF). Validated by massive field-based observations and selected edge-points, the CTF map achieved an overall accuracy of 95% and the F1 score of 0.93. As we calculated, the total area of tidal flats in China was 858,784 ha, and Jiangsu Province accounted the largest proportion (24%) of the national total. This study is the first attempt to delineate tidal flats automatically at a 10-m spatial resolution. The CTF map can provide essential information for management of coastal ecosystems and facilitate the implementations of coastal and marine related Sustainable Development Goals.

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship Yukun of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents.

Surface Water Extraction and Dynamic Analysis of Baiyangdian Lake Based on the Google Earth Engine Platform Using Sentinel-1 for Reporting SDG 6.6.1 Indicators

Surface water is an essential element that supports natural ecosystem health and human life, and its losses or gains are closely related to national or local sustainable development. Monitoring the spatial-temporal changes in surface water can directly support the reporting of progress towards the sustainable development goals (SDGs) outlined by the government, especially for measuring SDG 6.6.1 indicators. In our study, we focused on Baiyangdian Lake, an important lake in North China, and explored its spatiotemporal extent changes from 2014 to 2020. Using long-term Sentinel-1 SAR images and the OTSU algorithm, our study developed an automatic water extraction framework to monitor surface water changes in Baiyangdian Lake at a 10 m resolution from 2014 to 2020 on the Google Earth Engine cloud platform. The results showed that (1) the water extraction accuracy in our study was considered good, showing high consistency with the existing dataset. In addition, it was found that the classification accuracy in spring, summer, and fall was better than that in winter. (2) From 2014 to 2020, the surface water area of Baiyangdian Lake exhibited a slowly rising trend, with an average water area of 97.03 km2. In terms of seasonal variation, the seasonal water area changed significantly. The water areas in spring and winter were larger than those in summer and fall. (3) Spatially, most of the water was distributed in the eastern part of Baiyangdian Lake, which accounted for roughly 57% of the total water area. The permanent water area, temporary water area, and non-water area covered 49.69 km2, 97.77 km2, and 171.55 km2, respectively. Our study monitored changes in the spatial extent of the surface water of Baiyangdian Lake, provides useful information for the sustainable development of the Xiong’an New Area and directly reports the status of SDG 6.6.1 indicators over time.

Locating Famous Tea’s Picking Point Based on Shi-Tomasi Algorithm

To address the difficulty of locating the picking point of a tea sprout during the intelligent automatic picking of famous tea, this study proposes a method to obtain information on the picking point on the basis of the Shi-Tomasi algorithm. This method can rapidly identify a tea sprout’s picking point and obtain its coordinates. Images of tea sprouts in a tea garden were collected, and the G-B component of tea sprouts was segmented using the Otsu algorithm. The region of interest was set with the lowest point of its contour as the center. The characteristics of tea buds and branches in the area were extracted, and the Otsu algorithm was used for a second segmentation of tea sprout images. The tea buds were segmented using the improved Zhang algorithm. The branch feature binary image was used to refine the skeleton, and the Shi-Tomasi algorithm was used to detect the corners of the skeleton and calculate and mark the picking points of the shoots. Sixty sets of samples were tested. The test identified 1,042 effective shoots for tender buds, and 887 picking points were marked, with a success rate of 85.12%, thereby verifying the effectiveness of the method and providing a theoretical reference for the visual positioning of the automatic picking of famous tea.

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multiseasonal Sentinel-2 images. Specifically, five indices (i.e., the biophysical composition index calculated using the Gram-Schmidt orthogonalization method, biophysical composition index calculated using a principal component-based Procrustes analysis, Normalized Built-up Area Index (NBAI), combinational build-up index, and perpendicular impervious surface index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for ISA extraction from Sentinel-2 images in relation to binary methods and seasonal effects.

Moving Object Detection Algorithm based on Improved Visual Background Extractor

Aiming at the problems that the ViBe algorithm causes error detection when the camera jitter, ghosts appear when processing moving targets, and the detected moving targets are incomplete, an improved ViBe algorithm based on motion compensation is proposed. In the background modeling stage, the motion compensation method based on KLT is used to obtain background model to enhance the robustness of the algorithm to the dynamic background. In the foreground detection stage, combined the background model of the current pixel with 8 neighbor pixel background model, by the double discriminant algorithm to eliminate ghost areas caused by detecting the real background points as foreground points, introduce OTSU algorithm to obtain the optimal threshold for foreground detection. In the post processing stage, filter the connected components for segmentation masks and update models to limit the wrong background points scattered in the foreground.Finally, the connected domain is repaired by morphological opening operation. The results show that the algorithm can effectively eliminate the interference caused by camera jitter, and suppress the ghost phenomenon to obtain more accurate foreground images.

Study on the Diagnostic Value of Magnetic Resonance Imaging in Patients with Malignant Glioma

Objective: To explore the value of multi-voxel-based monomer 1H-MRS (magnetic resonance imaging) in the classification of gliomas. Methods: 26 gliomas confirmed by surgery and pathology were examined by conventional MRI, multi-voxel 2D, and monomer 1H-MRS before surgery. Three types of regions of interest (ROI) were used to measure Cho/NAA (Choline hydroxide solution/N-acetyl aspartic acid) values in the tumor. Methods: In the multi-voxel 2D hydrogen proton spectrum, the solid part of the tumor or the most intensive region of the tumor was selected to determine the Cho/NAA value. At the same time, in the multi-voxel 2D hydrogen proton spectrum, the Fun/tool spectra's copy-2DBmin analysis software was used to obtain the Cho/NAA value distribution. Anatomically superimposed the pseudo-color image, and measure the Cho/NAA value in its largest Cho/NAA value region. In addition, based on the level set algorithm, it uses the Otsu algorithm to initially locate the edema area and provide the initial contour. Results: The Cho/NAA values measured by the ROI selection method were statistically significant, and the sensitivity and specificity of judging the benign and malignant gliomas indicated that Method 3 had the highest diagnostic value. Conclusion: Multi-voxel-based monomeric 1H-MRS magnetic resonance imaging has the highest value in guiding glioma classification and can improve the accuracy of glioma classification.

Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat.

Subsea pipeline leak inspection by autonomous underwater vehicle

Faced with the difficulty in finding and locating leakage points of submarine pipelines timely as well as the high cost of pipeline routine inspection, a scheme using autonomous underwater vehicle (AUV) equipped with multibeam echo sounder (MBES) and forward looking sonar (FLS) for automatic inspection of submarine pipelines was developed. In the process of inspection, the AUV maintains autonomous navigation along the pre-set pipeline route at a fixed height above the pipeline, while the MBES collects water column images. After extracting the outline characteristics of gas-filled bubbles and making a leakage risk judgment, it rises to the sea surface to issue an alarm to the shore-based command center via satellite. The results of sea trial verify the effectiveness of sub-sea pipeline leak detection and real-time obstacle avoidance of the vehicle proposed in this study. Additionally, a variable buoyancy system (VBS) is adopted, which enhances the navigation efficiency of the vehicle. On the other hand, considering the complex operation environment of the pipeline inspection, the online collision avoidance is indispensable. The traditional image segmentation algorithm is not ideal for the high noise and bottom reverberation in operation sea area. Based on the traditional algorithm, an improved Otsu algorithm is proposed to improve the operation speed and denoise effect. As a result, an improved Otsu algorithm is proposed to accurately identify obstacles. Moreover, Kalman filtering is also introduced to estimate dynamic obstacles.

ESTIMACIÓN DE LA FRACCIÓN DE COBERTURA DE LA VEGETACIÓN EN MAÍZ (Zea mays) MEDIANTE IMÁGENES DIGITALES TOMADAS POR UN VEHÍCULO AÉREO NO TRIPULADO (UAV)

La fracción de cobertura de la vegetación es una variable importante en el monitoreo de cultivos que se relaciona con características biofísicas como la densidad, la fenología, el índice de área foliar, la germinación, la capacidad fotosintética del follaje, la evapotranspiración, la productividad y el rendimiento de los cultivos. El objetivo de este estudio fue estimar la fracción de cobertura vegetal durante el desarrollo del cultivo de maíz a partir de imágenes tomadas por un sensor que captura información en el espectro del rojo, verde y azul (RGB) montado en un vehículo aéreo no tripulado, con índices de vegetación y métodos de umbralización. Se realizaron seis vuelos, lo que generó seis ortomosaicos durante el desarrollo del cultivo. La captura de la imagen fue a 30 m de altura sobre el nivel del suelo, con resoluciones de 0.75 a 0.80 cm/pixel. Para la extracción de la vegetación y transformación de los ortomosaicos a escala de grises se emplearon cuatro índices de vegetación (IV): índice de verdor triangular (TGI), índice exceso de verde (EXG), índice de resistencia atmosférica visible (VARI) e índice de diferencia normalizado verde-rojo (NGRDI). La umbralización se realizó con los algoritmos Otsu, IsoData, Fuzzy y conservación de momentos a cada imagen en escala de grises generada con los IV para separar y clasificar los pixeles de las imágenes en dos clases, suelo y vegetación. Los índices utilizados para extraer la vegetación mostraron alta precisión en las primeras etapas de desarrollo, por lo que es posible utilizarlos para conocer la germinación, densidad y vigor inicial del cultivo. Se encontró que el índice EXG presentó errores de 2.2 a 17.8 % en la estimación de la fracción de cobertura de la vegetación durante el desarrollo del cultivo; errores menores al 5 % se obtuvieron mediante el umbral calculado con el método Fuzzy hasta los 58 días después de siembra. El uso de las imágenes digitales de alta resolución desde el vehículo aéreo no tripulado (UAV) permitió estimar la cobertura vegetal con errores menores al 5 % en las primeras etapas de desarrollo del cultivo del maíz.

Improved efficiency of urine cell image segmentation using droplet microfluidics technology.

Recent advances in the recognition of biological samples using machine vision have made this technology increasingly important in research and detection. Image segmentation is an important step in this process. This study focuses on how to reduce the interference factors such as the overlap between different types (or within the same type) of urine cells according to microfluidics and improve the machine vision segmentation accuracy for cell images. In this study, we demonstrate that the platform can realize this hypothesis using urine cell image segmentation as an example application. We first discuss the reported urine cell droplet microfluidic chip system, which can realize the test conditions in which urine cells are encapsulated in the droplet and isolated from salt crystallization and/or bacteria and other urine-formed elements. Then, based on the analysis conditions set in the aforementioned experiment, the proportions of red blood cells, white blood cells, and squamous epithelial cells covered by various formed elements in the total urine cells in the same urine sample are measured. We simultaneously analyze the percentage of urine cells covered by salt crystallization and the incidence of overlapping between urine cells. Finally, the Otsu algorithm is used to segment the urine cell images encapsulated by the droplet and the urine cell images not encapsulated by the droplet, and the Dice, Jaccard, precision, and recall values are calculated. The results suggest that the method of encapsulating single cells based on droplets can improve the image segmentation effect without optimizing the algorithm.

Characterisation of the combustion behaviours of individual pulverised coal particles entrained by air using image processing techniques

The combustion characteristics of individual coal particles are the basis for a deep understanding of the macroscopic pulverised coal combustion in power plant boilers. This work proposes a quantitative method to characterise the combustion behaviours of individual pulverised coal particles by measuring a set of physical parameters from digital images of the particles. The combustion process of pulverised particles of bituminous coal in a visual drop tube furnace was recorded by a high-speed camera with a frame rate of 6200 frames per second. An improved-Canny algorithm was developed to extract the combustion zones of a coal particle in both the volatile and char combustion phases. Using the improved-Canny and Otsu algorithms, the unburned part of the particle was identified in the volatile combustion phase. Characteristic parameters of coal particles, including the area, brightness, length, width and aspect ratio of volatile flame, and falling velocity, were derived from the processed images. The results obtained show that the volatile and char combustion took place successively and the volatile matter was combusted almost as soon as it was released. The particle travelled upward for around 14 ms during the early stage of combustion due to the influence of devolatilisation and volatile combustion. The particle also exhibited a slight difference in the rotation frequency at different combustion phases.

Generalized self‐tuning system for adaptive threshold estimators in cognitive radio systems using swarm and evolutionary‐based approaches

AbstractParameter‐based adaptive threshold estimators (ATEs) are widely used for signal detection in cognitive radio (CR) systems. However, their performance deteriorates under dynamic spectra conditions owing to a lack of valid methods to accurately self‐tune the different parameters of such ATEs. In this article, we address this limitation by proposing a generalized system for self‐tuning the parameters of any ATE based only on the input signal measured per time. We adopt swarm and evolutionary‐based metaheuristic optimization techniques to effectively search for the optimal parameter values of any ATE. Our system controls the search process by applying the between‐class variance function adapted from Otsu's algorithm as the objective function. We tested the system using five different metaheuristic optimization algorithms (MOAs) to self‐tune two different ATEs, namely the recursive one‐sided hypothesis testing (ROHT) technique and the histogram partitioning algorithm under Rayleigh and Rician fading channels, as well as under different modulation schemes, including the 4‐quadrature amplitude modulation and 4‐phase shift keying schemes. Our findings suggest that our proposed system yields generally a small error rate irrespective of the MOA used. In addition, the ROHT‐cuckoo search optimization configuration yielded a reasonably high and low probability of detection and probability of false alarm, respectively, as a function of the signal‐to‐noise‐ratio of the input signal at a fast average processing time of 0.0699 seconds. We concluded that our system presents an effective mechanism that can be used to automatically tune the parameters of any ATE for useful signal detection in CR.

Geomaterial segmentation method using multidimensional frequency analysis based on electrical resistivity tomography

Abstract The determination of specific geotechnical materials in complex geotechnical environments is important for engineering construction and geological disaster risk management, e.g., for karst caves and fracture zones. As a fast and convenient field test method, electrical resistivity tomography is extensively applied in geotechnical engineering. The resistivity distribution of the underground rock mass and soil can be obtained after inversion of the apparent resistivity. To quantitatively determine the resistivity threshold of specific geotechnical materials, the multidimensional frequency analysis and threshold determination algorithm of image segmentation based on the structural similarity between resistivity grid data and image voxels is employed. The frequency of the resistivity data is evaluated comprehensively with regard to two aspects: the resistivity magnitude and neighboring difference. The neighboring difference is introduced to describe the variation degree of the resistivity in space. Moreover, the Otsu algorithm is adopted to determine the resistivity threshold for identifying categories of the geotechnical material. To validate the material segmentation method, two different cases with rock mass conditions and soil conditions are studied. For the underground cavities left by human mining activities in a rock mass, multidimensional frequency analysis can effectively reveal the frequency components of the cavity resistivity. For an ancient soil landslide with a narrow distribution range of the resistivity, multidimensional frequency analysis can not only quantitatively determine the resistivity threshold of the soil sliding body, but also identify the materials with small proportions in the whole area, e.g., the sliding zone, according to the correlation between the neighboring difference and the material properties. Multidimensional frequency analysis can provide a quantitative and effective segmentation criterion for electrical resistivity tomography results, exhibiting potential for applications in geomaterial investigations.

RETRACTED: A Fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO

In order to reduce the time of lung tissue image segmentation, we proposed a fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO. Firstly, in the 2-D Otsu algorithm, the search scope of 2-D gray threshold is limited in a long and narrow region, which is the neighbourhood of the diagonal from the third region to the first region of the 2-D gray histogram, and the search scope and the computation are reduced, operation speed is improved. Secondly, In the PSO algorithm, the position of the particles is adjusted during iterating based on the principle of symmetric disposition, so as to avoid PSO falling into local optimal solution and improves the accuracy of threshold searching. Finally, a lung CT image with 1280×960 resolutions is segmented by our algorithm and other traditional algorithms, and a comparison is given. The segmentation threshold of our method is 85, the difference is less than 5 comparing with that of other traditional algorithms, and it shows that our method has almost the same searching precision as the traditional algorithms. The time cost is only 162ms, which is far less than the traditional algorithms, and it shows that our method improve the segmentation speed. It can be concluded that our method can not only satisfy the requirement of segmentation precision, but also meet the requirement of operation speed.

A Computationally Efficient Skull Scraping Approach for Brain MR Image

Background: In the process of volumetric evaluation of the damaged region in the human brain from a MR image it is very crucial to remove the non-brain tissue from the acquainted image. At times there is a chance during the process of assessing the damaged region through automated approaches might misinterpret the non-brain tissues like skull as damaged region due to their similar intensity features. So in order to address such issues all such artefacts. Objective: In order to mechanize an efficient approach that can effectively address the issue of removing the non-brain tissues with minimal computation effort and precise accuracy. It is very essential to keep the computational time to be as minimal as possible because the processes of skull removal is used in conjunction with segmentation algorithm, and if the skull scrapping approach has consumed a considerable amount of time, they it would impact the over segmentation and volume assessment time which is not advisable. Methods: In this paper a completely novel approach named Structural Augmentation has been proposed, that could efficiently remove the skull region from the MR image. The proposed approach has several phases that include applying of Hybridized Contra harmonic and Otsu AWBF filtering for noise removal and threshold approximation through Otsu based approach and constructing the bit map based on the approximated threshold. Morphological close operation followed by morphological open operation with reference to a structural element through the generated bitmap image. Results: The experiment are carry forwarded on a real time MR images of the patient at KGH hospital, Visakhapatnam and the images from open sources repositories like fmri. The experiment is conducted on the images of varied noise variance that are tabulated in the results and implementation section of the article. The accuracy of the proposed method has been evaluated through metrics like Accuracy, Sensitivity, Specificity through true positive, true negative, False Positive and False negative evaluations. And it is observed that the performance of the proposed algorithm seems to be reasonable good. Conclusion: The skull scrapping through structural Augmentation is computationally efficient when compared with other conventional approaches concerning both computational complexity and the accuracy that could be observed on experimentation. The Adaptive Weighted Bilateral Filter that acquire the weight value from the approximated contra harmonic mean will assist in efficient removal of poison noised by preserving the edge information and Otsu algorithm is used to determine the appropriate threshold value for constructing the bitmap image of the original MRI image which is efficient over the earlier mean based approach for estimating the threshold. Moreover, the efficiency of the proposed approach could be further improved by using customized structural elements and incorporating the fuzzy based assignments among the pixels that belong to brain tissue and skull effectively.

A Fusion Method for Atomic Force Acoustic Microscopy Cell Imaging Based on Local Variance in Non-Subsampled Shearlet Transform Domain

Atomic force acoustic microscopy (AFAM) is a measurement method that uses the probe and acoustic wave to image the surface and internal structures of different materials. For cellular material, the morphology and phase images of AFAM reflect the outer surface and internal structures of the cell, respectively. This paper proposes an AFAM cell image fusion method in the Non-Subsampled Shearlet Transform (NSST) domain, based on local variance. First, NSST is used to decompose the source images into low-frequency and high-frequency sub-bands. Then, the low-frequency sub-band is fused by the weight of local variance, while a contrast limited adaptive histogram equalization is used to improve the source image contrast to better express the details in the fused image. The high-frequency sub-bands are fused using the maximum rule. Since the AFAM image background contains a lot of noise, and improved segmentation algorithm based on the Otsu algorithm is proposed to segment the cell region, and the image quality metrics based on the segmented region will make the evaluation more accurate. Experiments with different groups of AFAM cell images demonstrated that the proposed method can clearly show the internal structures and the contours of the cells, compared with traditional methods.