Automatic Image Segmentation Algorithm Research Articles

Random Walk Algorithm-Based Computer Tomography (CT) Image Segmentation Analysis Effect of Spiriva Combined with Symbicort on Immunologic Function of Non-Small-Cell Lung Cancer.

The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms (P < 0.05). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M (H = 14.200, P = 0.001), but there was no great difference from the group C and the group W (P > 0.05). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M (H = 12.800, P = 0.002), and there was no statistical difference between the group C and W (P > 0.05). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.

Estimating boreal forest ground cover vegetation composition from nadir photographs using deep convolutional neural networks

Ground cover and surface vegetation information are key inputs to wildfire propagation models and are important indicators of ecosystem health. Often these variables are approximated using visual estimation by trained professionals but the results are prone to bias and error. This study analyzed the viability of using nadir or downward photos from smartphones (iPhone 7) to provide quantitative ground cover and biomass loading estimates. Good correlations were found between field measured values and pixel counts from manually segmented photos delineating a pre-defined set of 10 discrete cover types. Although promising, segmenting photos manually was labor intensive and therefore costly. We explored the viability of using a trained deep convolutional neural network (DCNN) to perform image segmentation automatically. The DCNN was able to segment nadir images with 95% accuracy when compared with manually delineated photos. To validate the flexibility and robustness of the automated image segmentation algorithm, we applied it to an independent dataset of nadir photographs captured at a different study site with similar surface vegetation characteristics to the training site with promising results.

Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images

Colorectal cancer is one of the deadliest and most widespread types of cancer in the world. Colonoscopy is the procedure used to detect and diagnose polyps from the colon, but today's detection rate shows a significant error rate that affects diagnosis and treatment. An automatic image segmentation algorithm may help doctors to improve the detection rate of pathological polyps in the colon. Furthermore, segmenting endoscopic tools in images taken during colonoscopy may contribute towards robotic assisted surgery. In this study, we trained and validated both pre-trained and not pre-trained segmentation models on two different data sets, containing images of polyps and endoscopic tools. Finally, we applied the models on two separate test sets and the best polyp model got a dice score 0.857 and the test instrument model got a dice score 0.948. Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.

Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset

Using deep learning methods to identify cash crop diseases has become a current hotspot in the field of plant disease identification. However, recent studies have demonstrated that the complex background information of crop images from practical application and insufficient training data can cause the wrong recognition of deep learning. To address this problem, in this paper we present an identification method of cash crop diseases using automatic image segmentation and deep learning with expanded dataset. An Automatic Image Segmentation Algorithm(AISA) based on the GrabCut algorithm is designed to remove the background information of images automatically while retaining the disease spots. It doesn‘t need to select the object manually during image processing and is of much lower time cost compared with the GrabCut algorithm. The MobileNet Convolutional Neural Network(CNN) model is selected as the deep learning model and plenty of crop images from the Internet and practical planting bases are added to expand the public dataset PlantVillage for the purpose of improving the generalization ability of MobileNet. The images are processed by the AISA before they can be used for extracting disease features, which reduces calculations significantly and ensures that the disease features of the crop leaf can be extracted accurately. Moreover, we design a cash crop disease identification system for mobile smart devices. The experimental results show that the system has a correct recognition rate of more than 80% for the 27 diseases of 6 crops described in this paper and then has a high value of practical application.

Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering

Although automatic fuzzy clustering framework (AFCF) based on improved density peak clustering is able to achieve automatic and efficient image segmentation, the framework suffers from two problems. The first one is that the adaptive morphological reconstruction (AMR) employed by the AFCF is easily influenced by the initial structuring element. The second one is that the improved density peak clustering using a density balance strategy is complex for finding potential clustering centers. To address these two problems, we propose a fast and automatic image segmentation algorithm using superpixel-based graph clustering (FAS-SGC). The proposed algorithm has two major contributions. First, the AMR based on regional minimum removal (AMR-RMR) is presented to improve the superpixel result generated by the AMR. The binary morphological reconstruction is performed on a regional minimum image, which overcomes the problem that the initial structuring element of the AMR is chosen empirically, since the geometrical information of images is effectively explored and utilized. Second, we use an eigenvalue gradient clustering (EGC) instead of improved density peak (DP) algorithms to obtain potential clustering centers, since the EGC is faster and requires fewer parameters than the DP algorithm. Experiments show that the proposed algorithm is able to achieve automatic image segmentation, providing better segmentation results while requiring less execution time than other state-of-the-art algorithms.

Object Motion Deblurring in Single Image Under Static Background

When shooting a moving object, as the object moves too fast or the camera’s exposure time is too long, smears may occur in the image, which would result in motion blur. The blind restoration of object motion blur is a challenging inversive problem. To effectively extract useful information from blurred images, this paper proposes a new method to remove motion blur, which is based on the maximum a posterior (MAP) framework. Firstly, the framework combines guided filtering and automatic GrabCut image segmentation algorithm in order to divide the image into different layers. Afterwards, it uses the image gradient to estimate the blur kernel through an alternating iterative optimization strategy. The iteratively reweighted least squares algorithm (IRLS) is used to optimize the solution of the model. Finally, we use the unsharp masking algorithm to improve the high-frequency components of the image and enhance the edge and details of the image. Therefore, the algorithm can effectively remove the blur caused by the motion of the object, suppress the noise and ringing effect, and recover a higher quality clear image, which can be demonstrated on benchmark problems.

Brain MRI Image Segmentation using Grab cut Algorithm

Brain tumor detection is an important task in medical image analysis. Tumor in the brain leads cancer and is to be diagnosed at earlier stage itself. Image processing techniques are applied for MRI images to segment and detect tumor. Detection of tumor in brain is done manually which is complex, tedious task and experience is necessary to detect a simulation results. Hence , many Automatic image segmentation algorithms are developed to detect and segment tumor from MRI images. This image segmentation algorithms uses either texture information or edge information for segmentation of MRI images. Graph-cut optimization approach has been developed recently, which utilizes both texture and edge information. The proposed work extends the graph cut approach in three steps. First a more powerful, iterative version of the optimization is developed. In the second step in order to reduce the user interaction a powerful iterative algorithm is applied for producing the result. Finaly,, a robust algorithm has been developed for border connecting to predict both the alpha-matte around an object boundary and the colours of foreground pixels.

RETRACTED: Virtual scene dynamic interactive image segmentation technology based on OpenGL module

A virtual scene dynamic interactive image segmentation technology based on OpenGL sky module is proposed to improve the performance of image dynamic interactive recognition algorithm and the recognition effect of image segmentation. First, an automatic dynamic interactive image segmentation algorithm is proposed, a probability segmentation model of Gauss mixture fracture based on multi-scale crack enhancement filter is established, and expectation maximization algorithm is used to determine the parameters of GMM (Gaussian Mixture Model) and the underlying segmentation; second, the drawing function of OpenGL is used to draw the graph, and the drawing method needs to be calculated. That is to say, a mathematical model is established, which converts the graph into the calculation of vertex coordinates for drawing. It uses an extended cost function to guide dynamic interactive image segmentation using a set of cracks from the population to be segmented. Finally, the validity of the algorithm is verified by experimental simulation.

A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests

Objective: A novel fuzzy reasoning model using temporal grayscale change and texture information of acetic acid test cervical images was developed to classify the patients at risk for Cervical Intraepithelial Neoplasia (CIN). Methods: Pre- and post-acetic acid test images were obtained from 505 patients (383 CIN negative and 122 CIN positive). An automatic image segmentation algorithm was implemented to extract the acetowhite region, followed by feature extraction of the: 1) temporal grayscale change from pre- to post-images; 2) texture coarseness; and 3) texture complexity of post-test images. The index of CIN was derived based on fuzzy reasoning incorporating expert knowledge. Logistic regression was built with cross validation to compare the classification performance between different combinations of features. Results: The averaged sensitivity was 80.8%, 80.9%, and 82.8% and specificity was 82.0%, 87.4%, and 86.2% for three individual features, respectively. The combination of all three features significantly improved the sensitivity (84.7%) without a significant loss of specificity (86.5%). The fuzzy reasoning model further improved the sensitivity significantly (sensitivity 85.9% and specificity 86.6%). Conclusion: Image texture features outperformed temporal grayscale change in specificity. Combining grayscale- and texture-based features could improve the overall classification performance, creating a synergistic effect. The proposed fuzzy reasoning model could further increase the sensitivity without requiring additional features. Significance: The proposed automatic CIN classification algorithm could provide a useful screening tool in the prevention of cervical cancers.

Automatic Image Segmentation With Superpixels and Image-Level Labels

Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing. We propose an automatic image segmentation algorithm based on superpixels and image-level labels. The proposed algorithm consists of three stages. At the stage of superpixel segmentation, we adaptively generate the initial number of superpixels using the minimum spatial distance and the total number of pixels in the image. At the stage of superpixel merging, we define small superpixels and directly merge the most similar superpixel pairs without considering the adjacency, until the number of superpixels equals the number of groupings contained in image-level labels. Furthermore, we add a stage of reclassification of disconnected regions after superpixel merging to enhance the connectivity of segmented regions. On the widely used Microsoft Research Cambridge data set and Berkeley segmentation data set, we demonstrate that our algorithm can produce high-precision image segmentation results compared with the state-of-the-art algorithms.

Automatic segmentation and classification of olive fruits batches based on discrete wavelet transform and visual perceptual texture features

The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive’s skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine.

Validating an image segmentation program devised for staging lymphoma.

Hybrid positron emission tomography-computed tomography (PET-CT) imaging systems are an important tool for assessing the progression of lymphoma. PET-CT systems offer the ability to quantitatively assess lymphocytic bone involvement throughout the body. There is no standard methodology for staging lymphoma patients using PET-CT images. Automatic image segmentation algorithms could offer medical specialists a means to evaluate bone involvement from PET-CT images in a consistent manner. To devise and validate an image segmentation program that may assist staging lymphoma by determining the degree of bone involvement based from PET-CT studies. A custom-made program was developed to segment regions-of-interest from images by utilising an enhanced fuzzy clustering technique that incorporates spatial information. The program was subsequently tested on digital and physical phantoms using four different performance metrics before being employed to extract the bony regions of clinical PET-CT images acquired from 248 patients staged for lymphoma. The algorithm was satisfactorily able to delineate regions-of-interest within all phantoms. When applied to the clinical PET-CT images, the algorithm was capable of accurately segmenting bony regions in less than half of the subjects (n = 103). The performance of the algorithm was adversely affected by the presence of oral contrast, metal implants and the poor image quality afforded by low dose CT images in general. Significant changes are necessary before the algorithm can be employed clinically in an unsupervised fashion. However, with further work performed, the algorithm could potentially prove useful for medical specialists staging lymphoma in the future.

Scan-Less Line Field Optical Coherence Tomography, with Automatic Image Segmentation, as a Measurement Tool for Automotive Coatings

The measurement of the thicknesses of layers is important for the quality assurance of industrial coating systems. Current measurement techniques only provide a limited amount of information. Here, we show that spectral domain Line Field (LF) Optical Coherence Tomography (OCT) is able to return to the user a cross sectional B-Scan image in a single shot with no mechanical moving parts. To reliably extract layer thicknesses from such images of automotive paint systems, we present an automatic graph search image segmentation algorithm. To show that the algorithm works independently of the OCT device, the measurements are repeated with a separate time domain Full Field (FF) OCT system. This gives matching mean thickness values within the standard deviations of the measured thicknesses across each B-Scan image. The combination of an LF-OCT with graph search segmentation is potentially a powerful technique for the quality assurance of non-opaque industrial coating layers.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

PCB welding spot detection with image processing method based on automatic threshold image segmentation algorithm and mathematical morphology

Purpose To enhance the detection efficiency of printed circuit board (PCB) soldered dot, this study aims to detect PCB soldered dot with image processing method. Design/methodology/approach Through a series of image processing algorithms, threshold segmentation and feature extraction of the solder joint images were completed; then, sphericity was confirmed based on the area and perimeter, as well as the shape parameter and eccentricity ratio of the calculated region, paving the way for defect pattern recognition. Findings It proves that the method with high efficiency and precision can satisfy the requirements of PCB soldered dot detection. Originality/value This paper is original in presenting a method for PCB welding spot detection.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics.ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms.ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases.ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Performance of automatic image segmentation algorithms for calculating total lesion glycolysis for early response monitoring in non-small cell lung cancer patients during concomitant chemoradiotherapy

Background and purposeThis study evaluated the use of total lesion glycolysis (TLG) determined by different automatic segmentation algorithms, for early response monitoring in non-small cell lung cancer (NSCLC) patients during concomitant chemoradiotherapy. Materials and methodsTwenty-seven patients with locally advanced NSCLC treated with concomitant chemoradiotherapy underwent 18F-fluorodeoxyglucose (FDG) PET/CT imaging before and in the second week of treatment. Segmentation of the primary tumours and lymph nodes was performed using fixed threshold segmentation at (i) 40% SUVmax (T40), (ii) 50% SUVmax (T50), (iii) relative-threshold-level (RTL), (iv) signal-to-background ratio (SBR), and (v) fuzzy locally adaptive Bayesian (FLAB) segmentation. Association of primary tumour TLG (TLGT), lymph node TLG (TLGLN), summed TLG (TLGS=TLGT+TLGLN), and relative TLG decrease (ΔTLG) with overall-survival (OS) and progression-free survival (PFS) was determined using univariate Cox regression models. ResultsPretreatment TLGT was predictive for PFS and OS, irrespective of the segmentation method used. Inclusion of TLGLN improved disease and early response assessment, with pretreatment TLGS more strongly associated with PFS and OS than TLGT for all segmentation algorithms. This was also the case for ΔTLGS, which was significantly associated with PFS and OS, with the exception of RTL and T40. ConclusionsΔTLGS was significantly associated with PFS and OS, except for RTL and T40. Inclusion of TLGLN improves early treatment response monitoring during concomitant chemoradiotherapy with FDG-PET.

A Segmentation-Based CFAR Detection Algorithm Using Truncated Statistics

Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation-maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments.

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images.

Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images. In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage. Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found. In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach.