Ideal Segmentation Research Articles

Recognition and Volume Measurement of Intracranial Hematoma through CT Images under Intelligent Recognition Algorithm

In order to realize the automatic recognition of intracranial hematoma and accurate measurement of hematoma volume in patients with ICH (intracerebral hemorrhage), the FCM algorithm (fuzzy c-means algorithm) was improved in this study, and a new level set segmentation algorithm based on FCM was obtained, FCRLS (fuzzy c-means regularized level set). Then, 120 ICH patients were used as the research objects, and the FCRLS algorithm was evaluated by the recall, precise, and F1-score values to evaluate the effect of intracranial hematoma recognition. The CT images of 48 patients with intracranial hematoma were used as the data set of the FCRLS algorithm. The hematoma was segmented, and the DSC (Dice similarity coefficient) value and running time were used to evaluate the segmentation results of the algorithm. At the same time, the LS (level set) algorithm and the FCM algorithm were introduced for comparison. The results show that the recall value of the FCRLS algorithm is 0.89, the precise value is 0.94, the F1-score value is 0.91, the Dice coefficient is 94.81%, and the running time is 14.48 s. Compared with the LS algorithm and the FCM algorithm, the above five indicators have significant differences ( P < 0.05 ). Hematoma volume measurement found that the average error of FCRLS algorithm from expert measurement results was 5.62%, which was statistically significant compared with LS algorithm and FCM algorithm ( P < 0.05 ). In summary, the FCRLS algorithm can accurately identify the cerebral hematoma area of ICH patients, has an ideal segmentation effect on the hematoma, and can accurately measure the true volume of the hematoma, which is worthy of clinical application.

A level set method based on additive bias correction for image segmentation

Intensity inhomogeneity brings great difficulties to image segmentation. This problem is partly solved by the multiplicative bias field correction model. However, some other problems still exist, such as slow segmentation speed and narrow application field. In this paper, an additive bias correction (ABC) model based on intensity inhomogeneity is proposed. The model divides the observed image into three parts: additive bias function, reflection edge structure function and Gaussian noise. Firstly, the local area and local clustering criterion of intensity inhomogeneity are defined. Secondly, by introducing the level set function, the local clustering criterion is transformed into an energy function based on the level set model. Finally, the structure of the estimated bias field and the reflection edge is computed through the process of minimizing the energy function while the image is segmented. In order to improve the stability of the system, a de-parameterized regularization function and an adaptive data-driven term function are designed. Compared with the traditional multiplicative model, the addition model has faster calculation speed. The proposed model can obtain ideal segmentation effect for images with intensity inhomogeneity. Experiment results show that the proposed method is more robust, faster and more accurate than traditional piecewise and multiplicative models.

Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study

There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.

EvoSeg: Automated Electron Microscopy Segmentation through Random Forests and Evolutionary Optimization.

Electron Microscopy Maps are key in the study of bio-molecular structures, ranging from borderline atomic level to the sub-cellular range. These maps describe the envelopes that cover possibly a very large number of proteins that form molecular machines within the cell. Within those envelopes, we are interested to find what regions correspond to specific proteins so that we can understand how they function, and design drugs that can enhance or suppress a process that they are involved in, along with other experimental purposes. A classic approach by which we can begin the exploration of map regions is to apply a segmentation algorithm. This yields a mask where each voxel in 3D space is assigned an identifier that maps it to a segment; an ideal segmentation would map each segment to one protein unit, which is rarely the case. In this work, we present a method that uses bio-inspired optimization, through an Evolutionary-Optimized Segmentation algorithm, to iteratively improve upon baseline segments obtained from a classical approach, called watershed segmentation. The cost function used by the evolutionary optimization is based on an ideal segmentation classifier trained as part of this development, which uses basic structural information available to scientists, such as the number of expected units, volume and topology. We show that a basic initial segmentation with the additional information allows our evolutionary method to find better segmentation results, compared to the baseline generated by the watershed.

Digital PCR Fluorescence Image Segmentation Algorithm Based on Image Processing

Digital PCR (Droplet digital Polymerase Chain Reaction) technology is currently one of the mainstream technologies for the detection and quantification of nucleic acid samples. This technology is widely used in the field of molecular biology. This technology counts the number of negative and positive reaction chambers in fluorescent images, and realizes absolute quantification of nucleic acid molecules. Due to the influence of the sampling equipment, some fluorescent images have uneven illumination, and the sampled droplet images will have bad spots and large areas of bright spots. The traditional Otsu segmentation algorithm cannot achieve the ideal segmentation effect. Aiming at the problem of under-segmentation of fluorescent images with uneven illumination using the traditional Otsu method, this paper proposes an algorithm for local segmentation based on Otsu segmentation. This algorithm realizes the segmentation of fluorescent images with uneven illumination and detects bright spots in large areas. The problems of dead pixel removal and scratch processing have been well resolved.

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

MR image segmentation based on level set method

In this paper, a level set model combining probabilistic statistics for image segmentation is proposed. Through adding a single-point pixel distribution into the energy function, the step size of each iteration is increased and the efficiency of the algorithm is improved. By adding the membership function of fuzzy clustering and bias field function, this method can effectively segment the image with intensity inhomogeneities. In addition, a new rule item is added to improve the edge segmentation effect of the image. Experiments on MR images of the brain show that the proposed model can provide ideal segmentation results compared with several level set segmentation models.

LinSEM: Linearizing segmentation evaluation metrics for medical images.

Numerous algorithms are available for segmenting medical images. Empirical discrepancy metrics are commonly used in measuring the similarity or difference between segmentations by algorithms and "true" segmentations. However, one issue with the commonly used metrics is that the same metric value often represents different levels of "clinical acceptability" for different objects depending on their size, shape, and complexity of form. An ideal segmentation evaluation metric should be able to reflect degrees of acceptability directly from metric values and be able to show the same acceptability meaning by the same metric value for objects of different shape, size, and form. Intuitively, metrics which have a linear relationship with degree of acceptability will satisfy these conditions of the ideal metric. This issue has not been addressed in the medical image segmentation literature. In this paper, we propose a method called LinSEM for linearizing commonly used segmentation evaluation metrics based on corresponding degrees of acceptability evaluated by an expert in a reader study. LinSEM consists of two main parts: (a) estimating the relationship between metric values and degrees of acceptability separately for each considered metric and object, and (b) linearizing any given metric value corresponding to a given segmentation of an object based on the estimated relationship. Since algorithmic segmentations do not usually cover the full range of variability of acceptability, we create a set (SS) of simulated segmentations for each object that guarantee such coverage by using image transformations applied to a set (ST) of true segmentations of the object. We then conduct a reader study wherein the reader assigns an acceptability score (AS) for each sample in SS, expressing the acceptability of the sample on a 1 to 5 scale. Then the metric-AS relationship is constructed for the object by using an estimation method. With the idea that the ideal metric should be linear with respect to acceptability, we can then linearize the metric value of any segmentation sample of the object from a set (SA) of actual segmentations to its linearized value by using the constructed metric-acceptability relationship curve. Experiments are conducted involving three metrics - Dice coefficient (DC), Jaccard index (JI), and Hausdorff Distance (HD) - on five objects: skin outer boundary of the head and neck (cervico-thoracic) body region superior to the shoulders, right parotid gland, mandible, cervical esophagus, and heart. Actual segmentations (SA) of these objects are generated via our Automatic Anatomy Recognition (AAR) method. Our results indicate that, generally, JI has a more linear relationship with acceptability before linearization than other metrics. LinSEM achieves significantly improved uniformity of meaning post-linearization across all tested objects and metrics, except in a few cases where the departure from linearity was insignificant. This improvement is generally the largest for DC and HD reaching 8-25% for many tested cases. Although some objects (such as right parotid gland and esophagus for DC and JI) are close in their meaning between themselves before linearization, they are distant in this meaning from other objects but are brought close to other objects after linearization. This suggests the importance of performing linearization considering all objects in a body region and body-wide.

Research on Image Segmentation Method Based on Fuzzy Clustering

Image segmentation is to divide an image into several continuous non-overlapping regions with similar characteristics, such as gray level, color and texture information. Through image segmentation, we can extract specific objects from complex background. The image has great uncertainty and fuzziness. Traditional segmentation methods often fail to achieve ideal segmentation effect, which directly affects the subsequent target feature extraction and result analysis. However, the fuzzy theory can well describe the characteristics of the image. Therefore, more and more scholars have studied image segmentation and proposed various related segmentation methods. Firstly, this paper analyses the hierarchical structure of image engineering. Then, this paper analyses the basic concepts of the fuzzy clustering algorithm. Finally, this paper analyses the image segmentation effect of the fuzzy algorithm by experiments.

Color image segmentation based on SLIC and watershed algorithm

In order to overcome the problem of over-segmentation caused by traditional watershed algorithm, a color image segmentation algorithm based on simple linear iterative clustering (SLIC) and watershed algorithm is proposed to achieve an ideal segmentation effect. Firstly, the algorithm calculates the number of super-pixels pre-segmented by image complexity, and uses SLIC to super-pixel segmentation preprocessing of the original image to reduce the redundant information in subsequent processing. Then, an adaptive threshold calculation method is proposed to process the gradient image of the preprocessed image in order to effectively remove noise and obtain more complete contour information. Finally, the watershed segmentation algorithm is used to segment the image extracted by the minimum value marker. Experiments on a large number of images show that the proposed algorithm can effectively suppress the over-segmentation problem caused by the traditional watershed algorithm, and is superior to the traditional algorithm in the comparison of LCE and GCE, and the segmentation quality is improved.

Hippocampal subfields segmentation in brain MR images using generative adversarial networks

BackgroundSegmenting the hippocampal subfields accurately from brain magnetic resonance (MR) images is a challenging task in medical image analysis. Due to the small structural size and the morphological complexity of the hippocampal subfields, the traditional segmentation methods are hard to obtain the ideal segmentation result.MethodsIn this paper, we proposed a hippocampal subfields segmentation method using generative adversarial networks. The proposed method can achieve the pixel-level classification of brain MR images by building an UG-net model and an adversarial model and training the two models against each other alternately. UG-net extracts local information and retains the interrelationship features between pixels. Moreover, the adversarial training implements spatial consistency among the generated class labels and smoothens the edges of class labels on segmented region.ResultsThe evaluation has performed on the dataset obtained from center for imaging of neurodegenerative diseases (CIND) for CA1, CA2, DG, CA3, Head, Tail, SUB, ERC and PHG in hippocampal subfields, resulting in the dice similarity coefficient (DSC) of 0.919, 0.648, 0.903, 0.673, 0.929, 0.913, 0.906, 0.884 and 0.889 respectively. For the large subfields, such as Head and CA1 of hippocampus, the DSC was increased by 3.9% and 9.03% than state-of-the-art approaches, while for the smaller subfields, such as ERC and PHG, the segmentation accuracy was significantly increased 20.93% and 16.30% respectively.ConclusionThe results show the improvement in performance of the proposed method, compared with other methods, which include approaches based on multi-atlas, hierarchical multi-atlas, dictionary learning and sparse representation and CNN. In implementation, the proposed method provides better results in hippocampal subfields segmentation.

Micro-Vessel Image Segmentation Based on the AD-UNet Model

Retinal vessel segmentation plays a vital role in computer-aided diagnosis and treatment of retinal diseases. Considering the low contrast between retinal vessels and the background image, complex structural information as well as blurred boundaries between tissue and blood vessels, the retinal vessel image segmentation algorithm based on the improved U-Net network is proposed in the paper. The algorithm introduces an attention mechanism and densely connected network into the original U-Net network and realizes the automatic segmentation of retinal vessels. According to the test results of the algorithm on commonly-used datasets of the DRIVE and STARE fundus images, respectively, the accuracy is 0.9663 and 0.9684; the sensitivity is 0.8075 and 0.8437; the specificity is 0.9814 and 0.9762; the AUC values are 0.9846 and 0.9765; and the F-measures are 0.8203 and 0.8419, respectively. In the paper, the Attention-Dense-UNet (AD-UNet) algorithm is applied to segment human bulbar conjunctival micro-vessels. The experimental results show that the algorithm can achieve ideal segmentation results.

A level set method based on local direction gradient for image segmentation with intensity inhomogeneity

Many medical and real images are suffered from intensity inhomogeneity and weak edges. For higher image segmentation quality, lots of level set-based methods have been proposed. Some of them however cannot take advantage of image gradient information. And severe intensity inhomogeneity and weak edges are not disposed properly. To address these problems, a new level set method integrated with local direction gradient information is presented in this paper. Firstly, according to the two assumptions on image intensity inhomogeneity adopted by many existing methods, a new pixel classification model based on image gradient is introduced. Secondly, we employ variational level set method combined with image spatial information, which improves the anti-noise capability of the proposed method. Finally, considering the gray gradients in homogeneous regions are close to constants, an improved diffusion process is incorporated into the level set function to make the evolving curves stay around true image edges. To verify our method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.

Pseudo‐color enhancement and its segmentation for femtosecond laser spot image

AbstractWhen using femtosecond laser processing silicon wafer, arises laser spot along with the plasma diffraction. Comparatively studied the spot images of silicon wafer which was in three processing movement states as follows: towards the left, stop, towards the right, found that the three dimensional Gauss mean ablation energy of spot image almost kept the same, this provides experimental support for femtosecond laser feedback processing based on Gauss energy of spot image. Then the following image enhancement strategies are proposed: pseudo color transformation for spot image, color decomposition in RGB space and image superposition of G component, and the quality of the spot image is improved. In addition, adopted the method of Particle Swarm Optimization (PSO) or K‐means respectively, analyzed the segmentation effect for spot image: through traversal compares the gray value of image pixel and fitness function, realized the spot image segmentation with PSO, and the clustering and segmentation for data cluster of image pixel was realized by K‐means. Finally, overcome the shortcomings of PSO and K‐means, the ideal segmentation for spot target image is realized by combining the two methods.

GAUSSIAN MIXTURE MODEL AND RJMCMC BASED RS IMAGE SEGMENTATION

Abstract. For the image segmentation method based on Gaussian Mixture Model (GMM), there are some problems: 1) The number of component was usually a fixed number, i.e., fixed class and 2) GMM is sensitive to image noise. This paper proposed a RS image segmentation method that combining GMM with reversible jump Markov Chain Monte Carlo (RJMCMC). In proposed algorithm, GMM was designed to model the distribution of pixel intensity in RS image. Assume that the number of component was a random variable. Respectively build the prior distribution of each parameter. In order to improve noise resistance, used Gibbs function to model the prior distribution of GMM weight coefficient. According to Bayes' theorem, build posterior distribution. RJMCMC was used to simulate the posterior distribution and estimate its parameters. Finally, an optimal segmentation is obtained on RS image. Experimental results show that the proposed algorithm can converge to the optimal number of class and get an ideal segmentation results.

Objectness Supervised Merging Algorithm for Color Image Segmentation

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory

According to the characteristics of high-resolution remote sensing (RS) images, a new multifeature segmentation method of high-resolution remote sensing images combining the spectrum, shape, and texture features based on graph theory is presented in the paper. Firstly, the quadtree segmentation method is used to partition the original image. Secondly, the spectrum, shape, and texture weight components are calculated all based on the constructed graph. The matching degree between pixels and the texture is computed similarity. Finally, the ratio cut standards combination of the spectrum, shape, and texture weight components is used for the final segmentation. The experimental results show that this method can obtain more ideal results and higher segmentation accuracy applied to RS image than those traditional methods.

Improved Algorithm Based on Level Set Image Segmentation Methods

Image segmentation is one of the most fundamental and important areas in the field of image processing and computer vision. The traditional level set methods need initialize the level set function as a distance function. If the initial contour is selected inappropriate, we may not get the desired ideal segmentation result. In order to solve the problem of level set automation initial, we proposed a new image segmentation algorithm based on level set and marker extraction. First, we extract the internal mark as level set initial curve by using Extended-minima transform. And then, through using the local binary fitting active contour model, we evolve the labeled image to get the final segmentation result. The simulation results show that this method has low computing complexity than the traditional level set method requirements, and can effectively solve the initialization problem of level set.

Texture Superpixels Merging by Color-texture Histograms for Color Image Segmentation

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal

Methods of forest carbon estimation using remote-sensing data and techniques are evolving within a short timeframe as compared to traditional forest inventory methods. Object-based image analysis (OBIA) provided new opportunities to improve biomass and carbon stock estimation and mapping by delineating and classifying crown projection area (CPA) of individual trees. In this paper, image segmentation techniques of OBIA (region growing and valley following) are being applied on GeoEye-1 satellite data and compared in terms of accuracy in Ludhikhola watershed in the Gorkha District of Nepal. Accuracy assessment of tree crown delineation of both segmentation approaches were analyzed using accuracy measures of D and one-to-one (1:1) correspondence. The combination of over-segmentation and under-segmentation, D is interpreted as the “closeness” measure to an ideal segmentation result in relation to a predefined reference set. Region growing and valley following segmentation 68 and 58 % accuracy, respectively, was achieved and linear regression model was developed for carbon stock for Shorea robusta which resulted into a coefficient of determination value of 0.67 at 95 % confidence level and the coefficient of determination resulted into a value of 0.70 for other species. The research concluded that region growing approach showed better delineation as compared to valley follow approach, since it used both features of local maxima and local minima.