Minimum Gray Value Research Articles

Correlation between pathology and quantitative ultrasonographic evaluation of pancreatic fat with ultrasonographic classification.

No studies of the relationship between grayscale sonographic findings and pancreatic fat content have been reported to date. This study aimed to investigate the correlation between echogenicity and fat content of resected specimens using quantitative analysis. Forty-two consecutive patients who underwent pancreatoduodenectomy or distal pancreatectomy for pancreatic tumors were enrolled in this study. Ultrasonographic images were compared with quantitative pathological analysis. Subjective evaluation of echogenicity was classified as hypoechoic, isoechoic, hyperechoic, and super hyperechoic. The total and intralobular fat areas were measured. The mean, median, modal, minimum, and maximum ultrasound gray values correlated with the proportion of total fat area (r = 0.349; 0.357, 0.486, 0.466, and 0.347; p = 0.024, 0.020, 0.014, 0.019, and 0.089, respectively), but did not correlate with the proportion of intralobular fat area. Subjective classification was correlated with median gray value (p < 0.001), intralobular fat area (p = 0.118), and total fat area (p = 0.011). Cases were classified as hypoechoic (n = 3), isoechoic (n = 7), hyperechoic (n = 30), and super hyperechoic (n = 2). The subjective classification was correlated with the median gray value (p < 0.001) and total fat area (p = 0.005), and not correlated with the intralobular fat area (p = 0.118). Hyperechoic or super hyperechoic pancreatic parenchyma contains over 19.7% fat. Computed tomography values correlated with the proportion of intralobular fat area (r = -0.479, p = 0.004) and total fat area (r = -0.541, p < 0.001). Echogenicity classified based on subjective evaluation and image analysis were correlated with the proportion of fat in the pancreas.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

BackgroundThe purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.MethodsThe preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.ResultsFor ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.ConclusionTexture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

基于区域灰度极小值的网孔织物图像分割算法

基于区域灰度极小值的网孔织物图像分割算法

Relationship between pubertal testicular ultrasonographic evaluation and future reproductive performance potential in Piétrain boars

New ways of predicting sperm quality and output performance in young artificial insemination (AI) boars are important for breeding companies to ensure that the pubertal boars delivered to the AI studs have a high chance of meeting minimum quality standards to be used for insemination and therewith dissemination of desirable characteristics. The aim of the current study was to characterize the testicular development of 218 pubertal Piétrain boars (Line 408, Pig Improvement Company) to identify traits with predictable characteristics relative to their sperm quality as an adult AI boar. Scrotum, testes and epididymis were examined ultrasonographically at day (d) 100 (on-test) and 170 (off-test) followed by a computer-assisted grayscale analysis (GSA). Over the test period, paired testicular volume increased 7.3-fold from 22.7 ± 10.8 cm3 to 166.6 ± 62.2 cm3. The right testis was significantly (P = 0.014) larger than the left one at the off-test. Based on the sperm quality (ejaculate volume, sperm concentration, total sperm number, morphologically abnormal sperm and total sperm motility at day 3 of semen storage), 82.11% (n = 179) of the boars were classified as “productive” boars. These boars had a significantly (P = 0.039) larger paired testicular volume than “non-productive” boars (45.9 ± 19.9 cm3vs. 38.5 ± 12.6 cm3) at the on-test. For the right testis at on-test, significant differences for the standard deviation of mean gray value (P = 0.022), area under the curve (P = 0.004) and mean gradient value (GRAD, P = 0.030) regarding the future sperm production capacity (SPC) were shown. At off-test, there was a significant difference for minimum gray value (MIN GV, P = 0.003) and mean gray value (P = 0.001) related to SPC. To find SPC related cut-off values for GSA data, a two segmental non-linear regression analysis was carried out indicating breakpoints for GRAD ≥12 and MIN GV ≥ 40 for boars with low SPC. Off-test boars with MIN GV ≥ 40 showed a 2.4 higher risk to display low SPC (Odds ratio = 2.4 [1.1, 5.4]; P = 0.024). The results may enable breeding companies to include new sperm quality associated traits in their boar testing and selection programs.

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion.

Quantitative image analysis is one of the methods to overcome the lack of objectivity of ultrasound (US). The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions. We retrospectively analyzed grayscale US images with histopathologic evidence of HCC or a precancerous lesion using ImageJ version 1.47 software (National Institutes of Health, Bethesda, MD). A total of 148 nodules were included (borderline lesion, n = 31; early HCC [eHCC], n = 3; well-differentiated HCC [wHCC], n = 16; moderately differentiated HCC [mHCC], n = 79; and poorly differentiated HCC [pHCC], n = 19). A multivariate analysis selected lower minimum gray values (odds ratio [OR], 0.431; P = .003) and a higher standard deviation (OR, 1.880; P = .019) as predictors of HCC smaller than 2 cm. Median (range) minimum gray values of borderline lesions, eHCC, wHCC, mHCC, and pHCC were 29 (0-103), 7 (0-47), 6 (0-60), 10 (0-53), and 2 (0-38), respectively, and gradually decreased from borderline lesions to pHCC (P < 0.001). The multivariate analysis showed a higher aspect ratio (OR, 2.170; P = .001) and lower minimum gray value (OR, 0.475; P = .043) as predictors of MVI. An anechoic area diagnosed by a subjective evaluation was correlated with the minimum gray value (P < .0001). The proportion of the anechoic area gradually increased from eHCC to pHCC (P = .031). In a US image analysis, HCC smaller than 2 cm had features of greater heterogeneity and a lower minimum gray value than borderline lesions. Moderately differentiated HCC was smoother than borderline lesions, and the anechoic area correlated with histologic grading. Microvascular invasion was correlated with a slender shape and a lower minimum gray value.

Investigation of the effectiveness of CBCT and gray scale values in the differential diagnosis of apical cysts and granulomas.

The aim of this study was to investigate the effectiveness of cone-beam CT (CBCT) findings and gray scale values (GSV) in the differential diagnosis of apical cysts and granulomas. Two independent researchers retrospectively analyzed the CBCT images of 21 teeth and histopathologically diagnosed them as having radicular cysts or apical granulomas. In the CBCT images, apical lesions were evaluated and categorized according to 7 criteria. These criteria were determined as relationship of lesions with dental roots, periphery of the lesion, shape, darker focus in the center, root resorption, displacement in related teeth, and cortical bone perforation. In addition, the minimum and maximum gray scale values of the lesions were measured and compared. There was a statistically significant relationship between histopathological (HP) diagnosis and well-defined cortical border and lesion shape (p = 0.003, p = 0.014, respectively). According to the HP diagnosis, no statistically significant difference was found among other variables (p > 0.05) CONCLUSION: The presence of a well-defined cortical border or partial cortical border is a distinctive criterion for cysts. Additionally, the shape of the lesion was found to be a significant criterion for the separation of the two lesions. The shape of cystic lesions was circular and apical granulomas had a more curved shape. There was no relationship between the histopathological diagnosis of lesions and CBCT gray scale values.

Visual Inspection Method of Ceramic Bottle Surface Defects Based on Niblack Optimization

In order to improve the efficiency and accuracy of the defect detection of ceramic bottle production line, a method of visual inspection of ceramic bottle surface defects based on threshold segmentation Niblack optimization algorithm is been proposed. The CLAHE interpolation image enhancement algorithm is used to improve the image contrast, and then the local threshold is obtained by calculating the local mean and standard deviation in the domain window to realize the segmentation of the defective region. Then the minimum gray value difference is introduced and whether there are defects in images are determined to the center pixel gray value range. The characteristics of the defect target are extracted, and the defect classifier is used to classify the defects according to the principle of support vector machine. The experimental results show that the optimization algorithm can realize the effective segmentation of the defect area and reduce the false detection rate by reducing the influence of background noise on the segmentation results with an accuracy rate of 90.8%.

Detecting sugarcane borer diseases using support vector machine

Based on the fact that great labor of artificial selection was needed after the sugarcane seeds were cut by the sugarcane cutting machine, and there was a misjudgment of the sugarcane borer diseases. SVM (support vector machine) method was proposed in this study to detect the sugarcane borer diseases. With the machine vision technology, together with threshold segmentation, filling and corrosion operation to process the three images of the same sugarcane whose interval is 120°. The classification features, minimum average gray value and the corresponding minimum gray value were selected by adaptive threshold segmentation algorithm, and removed the region which area of 1. The study used radial basis function as the kernel function of SVM, and roughly selected the range of regularization parameters of C and kernel function parameter σ. Finally, it selected the optimal parameters by the grid search and the cross validation method to identify sugarcane with diseases. The test showed that correct rate of diseases and disease-free sugarcane is 96%, 95.83% for the test set, so the method can effectively complete the sugarcane borer diseases detection.

A new descriptor for person identity verification based on handwritten strokes off-line analysis

A new descriptor for the verification of people’s identity through the analysis of handwritten text is presented. The proposed descriptor corresponds to a representation of the pattern of writing pressure computed from the grayscale image of a handwritten stroke. Specifically, the descriptor corresponds to the relative position of the minimum gray value points within the stroke. A repository of images for 50 people was created. Each person wrote 50 samples of 6 different symbols which resulted in a total of 15,000 images to carry out the experiments. For each individual’s identity verification, a supervised classifier for non-linearly separable data of the Support Vector Machine type was used, which resulted in the training of a total of 50 classifiers. 50 groups of balanced data were created through the sub-sampling of the majority class for the proper training of the classifiers. Furthermore, K-Fold Cross Validation was used to assess objectively the descriptor performance. The results of the assessment are positive: a hit rate average higher than 95% was achieved for the six analyzed symbols to verify identity. The overall proposal of the paper is interesting because it presents a method based on the processing of very simple characters (the characters are notoriously simpler than a signature). The proposed descriptor has the advantage of being invariant to rotation, which makes the process robust to involuntary changes in the inclination of the sheet containing the strokes. Besides, the descriptor is invariant to scale, as it considers the obtained sign length resizing. This makes the process robust to characters written with different sizes.

Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.

Quantitative sonographic image analysis for hepatic nodules: a pilot study.

The aim of this study was to investigate the feasibility of quantitative image analysis to differentiate hepatic nodules on gray-scale sonographic images. We retrospectively evaluated 35 nodules from 31 patients with hepatocellular carcinoma (HCC), 60 nodules from 58 patients with liver hemangioma, and 22 nodules from 22 patients with liver metastasis. Gray-scale sonographic images were evaluated with subjective judgment and image analysis using ImageJ software. Reviewers classified the shape of nodules as irregular or round, and the surface of nodules as rough or smooth. Circularity values were lower in the irregular group than in the round group (median 0.823, 0.892; range 0.641-0.915, 0.784-0.932, respectively; P = 3.21 × 10(-10)). Solidity values were lower in the rough group than in the smooth group (median 0.957, 0.968; range 0.894-0.986, 0.933-0.988, respectively; P = 1.53 × 10(-4)). The HCC group had higher circularity and solidity values than the hemangioma group. The HCC and liver metastasis groups had lower median, mean, modal, and minimum gray values than the hemangioma group. Multivariate analysis showed circularity [standardized odds ratio (OR), 2.077; 95 % confidential interval (CI) = 1.295-3.331; P = 0.002] and minimum gray value (OR 0.482; 95 % CI = 0.956-0.990; P = 0.001) as factors predictive of malignancy. The combination of subjective judgment and image analysis provided 58.3 % sensitivity and 89.5 % specificity with AUC = 0.739, representing an improvement over subjective judgment alone (68.4 % sensitivity, 75.0 % specificity, AUC = 0.701) (P = 0.008). Quantitative image analysis for ultrasonic images of hepatic nodules may correlate with subjective judgment in predicting malignancy.

Insulator Image Denoising Based on the Simplified PCNN Model

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.

An approach to remove impulse noise from a corrupted image

In this paper, we propose an efficient approach for detecting the impulse noise from corrupted images. This method is based on the principle that the feature of the digital image is usually local correlation and the feature of the impulse noise is usually located near one of the two ends of the image’s maximum and minimum gray values. After the noisy pixel has been detected by the proposed detector, a modified version of the mean filter is proposed to remove the detected impulse noise. Experimental results show that the implementation of the proposed method is simple, and it has better performance than comparison filters with regard to effective noise suppression and preservation of detail, especially when the noise ratio is very high.

An imaging technique using rotational polarization microscopy for the microstructure analysis of carbon/carbon composites

A novel image analysis technique was proposed for microstructure investigation of carbon/carbon (C/C) composites. The rotational polarization microscopy was developed to meet the special imaging requirements. The samples of C/C composites were observed in reflection polarized light microscope, where the analyzer was rotated instead of the stage, and the polarizer was taken out. The bireflectance of like-graphite negative uniaxial crystal was analyzed. It was the theoretic foundation of image collection and data processing. The analyzer was rotated through 36 × 10° intervals without any movement of the specimen. The polished cross-section of C/C composites took micrographs at each analyzer orientation. All image data collected from the same field of view were processed by image registration and image fusion. The synthesized images were obtained by calculating the maximum and minimum gray values and their differences at each point of the million pixels at 18 orientations of the analyzer. They are unique and quite reliable to be applied to analyze the microstructure of C/C composites. Subsequently, image segmentation was performed, and the feature parameters of each component were calculated. Good agreement was found between the results from image analysis and experimental data.

Region Connecting Based Pavement Image Cracks Extraction

The pavement image has three features: the background has similar statistical properties; cracks have less brightness than the background; cracks are connected to linear structure with much bigger area than impurities in the same image. Using these features, we present an algorithm comprising of image segment, breaking region connecting, and acreage comparison to extract pavement crack. Firstly, image segmentation using k-means clustering is implemented, and then the region with the minimum mean gray value is set as the initial extracted crack. The eight-connectivity cracks with small area are excluded to suppress interference and improve detection accuracy rate. Secondly, the minimum distance of every two non-connected crack regions is computed. When the distance is smaller than a given threshold, connect the two regions in the shortest possible path. Finally, morphology method is adopted to extract skeleton. Experimental results testify the effectiveness of the proposed algorithm in extracting pavement image cracks.

Image Segmentation using Effective Region Merging Strategy

A watershed-based image segmentation using effective region merging strategy. The proposed algorithm is a hybrid segmentation technique. Firstly, a filter is implemented to detect the boundary of the objects in the input gray-scale image and we mark the minimum gray value of pixels before adopting watershed transformation. Each region is labeled by a unique number after the transform. Then each region is represented by a graph node and the neighboring nodes of each node are saved in a matrix for the computation of region dissimilarity between the adjacent nodes according to three features: intensity mean, intensity variance and the number of pixels in a region. Last, the two regions which have minimum cost are merged while the information of adjacent nodes' relationship, cost among the merging nodes and their neighboring nodes and the labels of the regions are updated. This will be iterated until the final segmentation result. The experimental result shows that comparing to normalized cut algorithm the proposed method is more efficient and achieves more reasonable segmentation.

An Effective Image Contrast Enhancement Method Using Global Histogram Equalization

Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299 J. Sci. Res. 3 (1), 43-50 (2011)

A MODIFIED ADAPTIVE MEDIAN FILTER METHOD AND ITS APPLICATIONS IN MEDICAL IMAGES

Since the medical image is usually corrupted by noise, the filter method is applied to remove the noise and improve the image quality. In this paper, a modified adaptive median filter method is proposed for filtering the medical images. When identifying noises, by selecting the maximum and the minimum gray values in the image as a criterion of judging the noise pixels, the probability that a nonnoise pixel is misjudged to be a noisy one is reduced, and the processing time for finding the maximum and minimum gray values in each local window is drastically decreased as well. When filtering the image, according to the noise granularity function (NGF) in a 3×3 window, the filtering window size is adaptively adjusted, then the median filter is used to eliminate the current noise-marked pixel in the median image (MI) generated by the adaptive median filter, and at the same time the noise mark is cancelled. The proposed method may both effectively remove the noises, and preserve image detail information well. The experimental results reveal that the proposed method is particularly effective in filtering the impulse noises, also called salt-and-pepper noises superimposed on images, including computed tomography (CT) and magnetic resonance (MR) images.

Technical note: CT thresholding protocols for taking measurements on three‐dimensional models

Computed tomography (CT) is a powerful analytical tool that is becoming increasingly popular for investigating small and difficult to visualize (internal) structures. However, along with this capability come numerous potential difficulties. One vexing problem is how to interpret the images that result from CT scanning. A typical CT slice displays a spectrum of gray-scale values that represent the densities of the structures in the image. To measure a particular structure it is necessary to establish a threshold value (i.e., gray-scale value), which clearly distinguishes the structure of interest from surrounding structures. However, determining the proper threshold is not a trivial task. One problem arises from the fact that the boundaries between adjacent structures (e.g., bone and air) are never clearly defined, but are instead represented by a continuum of gray-scale values. Another problem is related to the viewer control settings (window and leveling values), which can greatly affect the visual appearance of images, particularly along the edges of structures. Simply basing of the threshold value on the apparent (visual) boundaries has been shown to yield erroneous results (Koehler et al., 1979; Baxter and Sorenson 1981; Hara et al., 2002). A handful of studies have sought to investigate this problem, and these have led to several proposals regarding the appropriate method for establishing the threshold value. One approach that has enjoyed increasing attention is the half-maximum height protocol (HMH), which calculates the threshold value as the mean of the maximum and minimum gray scale values along a row of pixels that spans the boundary transition (Ulrich et al., 1980). This method has been found to produce accurate measurements of modern human vertebrae (using regular CT images—Ullrich et al., 1980; Baxter and Sorenson 1981; Seibert et al., 1981; Eubanks et al., 1985) and of extant and fossilized long-bones and teeth (Spoor et al., 1993). More recently, Fajardo et al. (2002) used a variation of the HMH method (applied to high resolution X-ray computed tomography) to accurately reconstruct and measure trabecular architecture of long bones. These researchers also found that it was important to sample the appropriate region of interest because bone types of different density (e.g., cortical versus trabecular) yield different HMH values (Fajardo et al., 2002). One of the limitations of these studies is that the HMH protocol and validation measurements were applied to individual slices. However, when a researcher wishes to take measurements on three-dimensional models created from CT, a single threshold value must be applied to the slices of interest. One potential concern is that applying a single threshold value to a dataset of dozens or hundreds of slices may not accurately represent the real morphology of various structures. Thus, this study sought to investigate the effects of taking linear measurements on three-dimensional models using a modified version of the HMH protocol. To examine the accuracy of the thresholding protocol, measurements were taken from high resolution X-ray computed tomography data and compared with measurements taken on dried specimens. The oval window was selected as the variable of interest because it represents a relatively small structure and also because it is possible to obtain these measurements on the dried skulls of certain taxa with a reasonable amount of accuracy. A Zeiss Discovery V.12 stereo digital microscope was used to take the measurements on the dried specimens (hereafter referred to as Zeiss-based measurements). All scanning was done at the University of Texas High-Resolution X-ray CT facility. These scans were taken with a 68-lm slice thickness and 68-lm interslice spacing. The images were reconstructed from 1,000 views and the field of view was 64 mm yielding a pixel size of 62.5 lm (1024 3 1024 pixel matrix). The final images were 16-bit TIFF files. The image stacks were imported into ImageJ 1.35f (NIH), cropped, converted to 16-bit signed files (to be compatible with the measurement software), and saved as raw stacks. These stacks were then used to construct three-dimensional models using 3D Slicer 2.6 and the measurements were taken using the fiducials module. This module allows the researcher to place markers (fiducials) anywhere on the model and take linear measurements between the markers. It is useful since it permits structures to be measured that are not in the same plane. Each measurement was taken three times and the mean of the three measurements was taken as the final value.

Image Reconstruction Using a Genetic Algorithm for Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) has been used for more than a decade for imaging dielectric processes. However, because of its ill-posedness and non-linearity, ECT image reconstruction has always been a challenge. A new genetic algorithm (GA) developed for ECT image reconstruction uses initial results from a linear back-projection, which is widely used for ECT image reconstruction to optimize the threshold and the maximum and minimum gray values for the image. The procedure avoids optimizing the gray values pixel by pixel and significantly reduces the search space dimension. Both simulations and static experimental results show that the method is efficient and capable of reconstructing high quality images. Evaluation criteria show that the GA-based method has smaller image error and greater correlation coefficients. In addition, the GA-based method converges quickly with a small number of iterations.