Global Thresholding Method Research Articles

3D mapping of discontinuity traces using fusion of point cloud and image data

A new methodology is presented for 3-D automated mapping of joints that are exposed primarily as traces in a rock face as opposed to planar facets. The method identifies 3-D points in a photogrammetry or a LiDAR derived point cloud that corresponds to the traces of the joints as observed in image data. First, the 2-D trace texture is extracted from image data using a hybrid global and local threshold method and integrating a series of image-processing algorithms. Second, data matching links the pixel locations corresponding to the identified traces in an image to the 3-D coordinates in the point cloud. This matching is accomplished by a coordinate transformation between the image coordinates and point cloud coordinates. Finally, a 3-D discontinuity trace map is acquired by analysing the 3-D spatial features of the traces. A case study of a rock slope along a highway is presented using the proposed method. The results demonstrate that the fusion of image data and point cloud data improves the mapping of discontinuities that primarily appear as traces in outcrops versus that achieved by existing methods that rely only on point cloud data.

Study of Segmentation Technique and Stereology to Detect PCO Follicles on USG Images

Polycystic Ovary Syndrome is a hormonal health problem in women who are in the reproductive period. One feature of this syndrome is that there are 12 or more follicles in the ovary with a diameter of 2-9 mm. This syndrome can cause menstrual cycles problems and even a problem in conceiving a child. Currently, follicle detection is still done manually by Obstetricians and it takes a long time to get accurate result. Therefore, a system that can detect follicles and calculate its size automatically is required. This paper uses Global Basic Threshold and Otsu Threshold method to get the form of follicles in the process of image binarization and follicle segmentation and stereology approach to calculate the number and diameter of the follicle. The result of system testing shows that the system with Global Basic Threshold method and stereology had a sensitivity rate of 86%, a specificity of 15 and a 33% detection size error rate. While using the method Otsu Threshold and stereology showed sensitivity results of 89%, specificity 14 and 35% detection size error rate.

Computerized detection of breast cancer in digital mammograms

In this paper, authors propose an efficient system for the segmentation of lesions in digital mammograms. The proposed detection system comprises three steps. In the first step, efficient pre-processing technique is developed using top-hat morphological transform, wavelet transform, and morphological opening–closing reconstruction filter followed by median filter. In the second step, threshold selection procedure is developed using a combination of Fuzzy C-means (FCM) clustering and Otsu global thresholding method. Finally, computed threshold is used to binarize the pre-processed image thereby yielding the suspicious mass regions from it. The free response operating characteristics (FROC) curve is used to evaluate the performance of the proposed method. Proposed system achieved the sensitivity of 94% at the rate of 1.30 false positives per image.

A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.

Combining global and local scaling methods to detect soil pore space

Computed tomography (CT) images provide very useful information to characterize the pore space structure in different porous materials. Being a non-destructive technique, it allows preserving the real characteristics of the analysed samples. Moreover, providing 3 dimensional (3D) information, it also allows making more exhaustive studies on other characteristics of pore space such as connectivity or pore geometry. However, this technique also has drawbacks such as the insertion of errors and distortions from CT reconstruction that could drive to low contrast greyscale images hindering the detection of solid-void interface.The Singularity-CA (S-CA) binarization method has already proven to be an efficient method when detecting the pore space in CT soil images. This method is primarily based on the existence of self-similar properties in the singularity value (SV) spatial distribution. These self-similar properties derive to thresholds in the singularity map which are used to delimit the pore space. This method detects the medium and large pore sizes with an excellent fit. However, due to its high sensibility to low fluctuations in the greyscale images, some small pores are incorrectly detected and therefore its amount is overestimated.This work attempts to solve this drawback introducing a new approach named the Combining Singularity-CA (CS-CA) method. This improved method is based on the combination of a global thresholding method, the Maximum Entropy method, and the S-CA method. The CS-CA methodology is fully automatic and did not require human adjustment. A set of soil synthetic images with porosities from 3% to 25% were used to validate the new approach. These images were constructed using the Truncated Multifractal (TM) method, especially useful to simulate CT soil images. Based on parameters such as porosity, relative error and misclassification error, CS-CA method gave better pore detection than the S-CA and the Maximum Entropy method applied individually to the images.Finally, it was compared the two main methods used to detect slope-change points in plots with several linear segments: the linear regression (LR) method and the wavelet transform modulus maxima (WTMM) method. These methods are very important in the S-CA methodology since they are responsible of defining the threshold in the binarization step. The WTMM method proved to be easier to deal with when using by the new CS-CA method.

Comparison of Global Histogram-based Thresholding Methods that Applied on Wound Images

Comparison of Global Histogram-based Thresholding Methods that Applied on Wound Images

HYPERSPECTRAL CLASSIFICATION FOR IDENTIFYING DECAYED ORANGES INFECTED BY FUNGI

Fast and nondestructive detection of early decay caused by fungal infection in citrus fruit was a challenging task for the citrus industry during the postharvest fruit processing. In general, workers relied on the ultraviolet induction fluorescence technique to detect and remove the decayed citrus fruits in fruit packing houses. However, this operation was harmful for human health, and was also very inefficient. In this study, navel oranges were used as research object. A novel method combining with hyperspectral imaging technology in the wavelength region between 400 and 1100 nm wavelength was proposed to solve this problem. First, normalization approaches were applied to decrease the variation of spectral reflectance intensity due to natural curvature of navel orange surface. Then, the spectral data of regions of interest (ROIs) from normal and decayed tissues was analyzed by principal component analysis (PCA) for investigating the performance of visible and near infrared (Vis-NIR) hyperspectral data to discriminate these two kinds of tissues. Next, six characteristic wavelength images were obtained by analyzing the loadings of the first principal component (PC1). And, a multispectral image was established by using the corrected six characteristic wavelength images. On basis of the multispectral image, pseudo-color image processing with intensity slicing was utilized to produce a two-dimensional color image with clear contrast between decayed and normal tissues. Finally, an image segmentation algorithm by combining the pseudo-color processing method and a global threshold method was proposed for fast identification of decayed navel oranges. For 240 independent samples, the success rates were 100 and 97.5% for decayed navel oranges infected by Penicillium digitatum and normal navel oranges, respectively. In particular, the proposed algorithm was also applied to detect the decayed navel oranges infected by Penicillium italicum (samples not used for the development of algorithm) and obtained a 91.7% identification accuracy, indicating a well generalization ability and actual application value of the proposed algorithm.

Strategies for Segmenting the Upper Airway in Cone-Beam Computed Tomography (CBCT) Data

The wide availability, low radiation dose and short acquisition time of Cone-Beam CT (CBCT) scans make them an attractive source of data for compiling databases of anatomical structures. However CBCT has higher noise and lower contrast than helical slice CT, which makes segmentation more challenging and the optimal methods are not yet known. This paper evaluates several methods of segmenting airway geometries (nares, nasal cavities and pharynx) from typical dental quality head and neck CBCT data. The nasal cavity has narrow and intricate passages and is separated from the paranasal sinuses by thin walls, making it is susceptible to either over- or under-segmentation. The upper airway was split into two: the nasal cavity and the pharyngeal region (nasopharynx to larynx). Each part was segmented using global thresholding, multi-step level-set, and region competition methods (the latter using thresholding, clustering and classification initialisation and edge attraction techniques). The segmented 3D surfaces were evaluated against a reference manual segmentation using distance-, overlap- and volume-based metrics. Global thresholding, multi-step level-set, and region competition all gave satisfactory results for the lower part of the airway (nasopharynx to larynx). Edge attraction failed completely. A semi-automatic region-growing segmentation with multi-thresholding (or classification) initialization offered the best quality segmentation. With some minimal manual editing, it resulted in an accurate upper airway model, as judged by the similarity and volumetric indices, while being the least time consuming of the semi-automatic methods, and relying the least on the operator’s expertise.

Efficient local adaptive thresholding for night-time vehicle candidate detection

In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, 'Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process?' For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.

Exudates and Blood Vessel Segmentation in Eye Fundus Images using the Fourier and Cosine Discrete Transforms

This paper presents a new method using discrete transforms to segment blood vessels and exudates in eye fundus color images. To obtain the desired segmentation, an illumination correction is previously done based on a homomorphic filter because of the uneven illuminance in the eye fundus image. To distinguish foreground objects from the background, we propose a super Gaussian bandpass filter in the discrete cosine transform DCT domain. These filters are applied on the green channel that contains information to segment pathologies. To segment exudates in the filtered DCT image, a gamma correction is applied to enhance foreground objects; in the resulting image, the Otsu's global threshold method is applied, after which, a masking operation over the effective area of the eye fundus image is performed to obtain the final segmentation of exudates. In the case of blood vessels, the negative of the image filtered with DCT is first calculated, then a median filter is applied to reduce noise and artifacts, followed by a gamma correction. Again, the Otsu's global threshold method for binarization, next a morphological closing operation is employed, and a masking operation gives the corresponding final segmentation. Illustrative examples taken from a free clinical database are included to demonstrate the capability of the proposed methods.

Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images

Image segmentation is the decisive process in object-based image analysis, but segmenting a landscape scene into meaningful geo-objects remains a challenge. In recent years, there has been growing interest in hybrid methods that combine initial segmentation with subsequent region merging, because they exploit spectral signals across an entire geo-object, including both the boundary signals used to delineate the initial segments, and the interior signals used to merge similar segments. However, existing algorithms commonly use a single, global parameter to control the process of region merging, thereby limiting the goodness-of-fit between segments and geo-objects, since homogeneous and heterogeneous segments are treated equally. To overcome this limitation, we developed a new hybrid segmentation method that employs local spectral angle (SA) thresholds for region merging. We implemented our local SA method in three very different landscapes, then compared our region merging method to the global SA method, as well as the global elevation method used in System for Automated Geoscientific Analyses (SAGA). In all three landscapes, the results revealed that the local SA segmentation provides a better fit to reference polygons than the two global threshold methods, as measured using a modified discrepancy measure for the purpose of geo-object recognition (QRM). We also found that the local SA method produced segments with a greater variation in size, indicating the method is effective for achieving multi-scale segmentation.

Segmentation, Feature Extraction and Classification of Astrocytoma in MR Images

The purpose of this research paper is to segment tumorous tissue from brain magnetic resonance images using image processing. For this the astrocytoma, a type of brain tumor is considered in which cells grow abnormally in the brain. Astrocytoma is classified as low grade or high grade using k-nn classifier and then analyzed its performance on the basis of three parameters i.e., accuracy, severity and specificity. The Magnetic Resonance Images (MRI) of astrocytoma has been taken from BRATS database so as to explore different algorithms for segmentation of brain tumour. The enhancement of MRI images is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). After that basic global thresholding method is used for the automatic segmentation of tumorous tissue. In feature extraction step, the shape based features and texture based features are extracted. On the basis of features extracted from the segmented image, K-nn classifier is used to classify the images in two grades i.e., low grade or high grade. The performance of the system is evaluated by parameters accuracy, severity and specificity. The accuracy is coming out to be 93%.

Global and Local Thresholding Methods Applied to X-ray Microtomographic Analysis of Metallic Foams

X-ray based computed microtomography is a non-destructive, well established tool for a three-dimensional characterization of open-cell metallic foams. Macroscopic physical and chemical properties of these materials stay in close relation to their micro-structure parameters. The purpose of the paper is to present two types of thresholding methods so-called global and local thresholding for evaluating the structural parameters of open-cell metal foams based on X-ray microtomography data. Two different methods were chosen: automatic Otsu thresholding (global) and adaptive (mean of minimal and maximal grey values of grayscales within a selected radius). The key parameters of aluminum and nickel-chromium foams fine structure calculated using Otsu and locally thresholded images were significantly different. The proper image segmentation is the key point in metallic foam morphometry. The influence of a radius of the image processing region on the results obtained is discussed for the local thresholding method. Examples of the images artifacts generated by local thresholding method to demonstrate possible results misinterpretation are also given. The optimization of local thresholding parameter (radius of the image processing region) was presented.

Simple approach to improving the extraction of canopy metrics from airborne laser scanning data for tropical forests

We aim to improve the predictive mapping of stem volume with airborne laser scanning (ALS) data acquired in Laos by adapting the area-based approach (ABA) to a tropical context. Separating laser returns of bushes from main stories with a cut-off threshold is a step very important to the ABA. The adaptation focused here on applying global and plot-adaptive cut-off thresholds to improve the extraction of canopy metrics. In order to select the optimal global cut-off threshold for removing understory bushes and ground objects, a sensitivity analysis of the modeling efficacy to the global cut-off threshold was conducted in the range from 0 to 5 m at 0.1-m intervals. To account for structural variation between plots, a simple plot-adaptive method was proposed for adjusting the threshold of each specific plot. The results showed that the optimal global cut-off threshold, which implicitly assumed the forest structure being homogeneous for all plots was 3.6 m. A model based on the plot-adaptive cut-off thresholds achieved better accuracy (RMSE 28%) than did the optimal global threshold-based model (RMSE 30%). It is concluded that the ALS-based canopy metrics extracted using the plot-adaptive method describe the structural heterogeneity of tropical forests adequately, whereas the global thresholding method is contingent on the forest structure being simple.

Using computer aided system to determine the maximum depth of visualization of B-Mode diagnostic ultrasound image

In the service unit of radiology, ultrasound modality is widely used because it has advantages over other modalities, such as relatively inexpensive, non-invasive, does not use ionizing radiation, and portable. Until now, the method for determining the depth visualization on quality control program is through the visual observation of ultrasound image on the monitor. The purpose of this study is to develop a computer-aided system to determine maximum depth of visualization. Data acquisition was done by using B-Mode Diagnostic Ultrasound machine and Multi-purpose Multi-tissue Ultrasound Phantom model 040GSE. Phantom was scanned at fixed frequency of 1,8 MHz, 2,2 MHz, 3,6 MHz and 5,0 MHz with a gain variation of 30 dB, 45 dB, and 60 dB. Global thresholding and Euclidean distance method were used to determine maximum visualization depth. From this study, it is proved that the visualization depth using computer aided provide deeper visualization than visual interpretation. The differences between expert verification and the result of image processing are <6%. Thus, computer aided system can be used for the purpose of quality control in determining maximum visualization depth of B-Mode diagnostic ultrasound image.

Global gray-level thresholding based on object size.

In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes. © 2016 International Society for Advancement of Cytometry.

Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm

Segmentation and Recognition of Korean Vehicle License Plate Characters Based on the Global Threshold Method and the Cross-Correlation Matching Algorithm

Unsupervised Multi-level Thresholding Method for Weather Satellite Cloud Segmentation

Segmentation is one of the most important tasks in image processing, it seeks to determine whether an intensity value corresponds to a predefined class. Global thresholding is the simplest method for segmentation, it separates the image into two distinct classes corresponding to intensity values located below and above a threshold. However, global thresholding methods have a tendency to over-segment or under-segment areas with relatively inhomogeneous intensity. Multi-level thresholding takes into account spatial variations of intensity in an image, it is obtained by applying to each region of the image a different threshold. In this paper we present an unsupervised multi-level thresholding technique for segmenting cloud areas from weather satellites images. Our approach is to initially generate several binary images from a set of predefined threshold values, then extracting and mapping the contours of the cloudy areas included in the image sequence. The segmented image will comprise all regions whose contour coincides with the outline of a region in the original image.

Image analysis of anatomical traits in stalk transections of maize and other grasses.

BackgroundGrass stalks architecturally support leaves and reproductive structures, functionally support the transport of water and nutrients, and are harvested for multiple agricultural uses. Research on these basic and applied aspects of grass stalks would benefit from improved capabilities for measuring internal anatomical features. In particular, methods suitable for phenotyping populations of plants are needed.ResultsTo meet the need for large-scale measurements of stalk anatomy features, we developed custom image processing software that utilized a variety of global thresholding, local filtering, and feature detection methods to measure rind thickness, pith area, vascular bundle counts, and individual vascular bundle size from digital images of hand-cut transections of stalks collected with a flatbed document scanner. The tool determined vascular bundle number with an average accuracy of 90% across maize genotypes that varied five-fold for this trait. The method is demonstrated on maize, sorghum, and Miscanthus stalks. The computer source code is staged for download.ConclusionsSimplicity of sample preparation and semi-automated analyses enabled by this tool greatly increase measurement throughput relative to standard microscopy-based techniques while maintaining high accuracy. The tool is expected to be useful in genetic and physiological studies of the relationships between stalk anatomy and traits such as biofuel suitability, water use efficiency, or nutrient transport.

A Global Thresholding De-Noising Method for Power Quality Signals

The noise component in power quality signals affects the accuracy of analysis result. This paper presents a new approach for power quality signals de-noising. Firstly, the original signals are transformed by S-transform method. Then, the matrix which is get from S-transform result is processed as a two-dimension image. The global thresholding de-noising method is used to filtering the noise component in the power quality signals. The simulation results showed the effectiveness of the new approach.