Ideal Segmentation Research Articles

Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Application of Image Segmentation Algorithms in Computer Vision

In the field of computer vision (CV), image segmentation technology, as a fundamental part, has a crucial impact on the accuracy of subsequent image processing tasks. Image segmentation is not only a crucial transitional step from image processing to image analysis, but also a hot and difficult research topic in the field of CV. Although significant progress has been made in the research of image segmentation algorithms, existing segmentation algorithms may still face challenges in certain specific scenarios due to the complexity and diversity of images, making it difficult to achieve ideal segmentation results. In recent years, the rapid development of deep learning (DL) technology has brought new breakthroughs to the field of image segmentation. DL models, especially Convolutional Neural Networks (CNNs), can capture semantic information of images more accurately by automatically learning feature representations in images, thereby achieving more precise image segmentation. This article delves into the research and application of image segmentation algorithms in CV, with a focus on the application of DL in the field of image segmentation. With the continuous development of advanced technologies such as DL, it is believed that image segmentation technology will play a greater role in more fields in the future.

Learning Generalized Segmentation for Foggy-Scenes by Bi-directional Wavelet Guidance

Learning scene semantics that can be well generalized to foggy conditions is important for safety-crucial applications such as autonomous driving. Existing methods need both annotated clear images and foggy images to train a curriculum domain adaptation model. Unfortunately, these methods can only generalize to the target foggy domain that has seen in the training stage, but the foggy domains vary a lot in both urban-scene styles and fog styles. In this paper, we propose to learn scene segmentation well generalized to foggy-scenes under the domain generalization setting, which does not involve any foggy images in the training stage and can generalize to any arbitrary unseen foggy scenes. We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style. As the content (e.g., scene semantic) rests more in low-frequency features while the style of urban-scene and fog rests more in high-frequency features, we propose a novel bi-directional wavelet guidance (BWG) mechanism to realize the above three objectives in a divide-and-conquer manner. With the aid of Haar wavelet transformation, the low frequency component is concentrated on the content enhancement self-attention, while the high frequency component is shifted to the style and fog self-attention for de-correlation purpose. It is integrated into existing mask-level Transformer segmentation pipelines in a learnable fashion. Large-scale experiments are conducted on four foggy-scene segmentation datasets under a variety of interesting settings. The proposed method significantly outperforms existing directly-supervised, curriculum domain adaptation and domain generalization segmentation methods. Source code is available at https://github.com/BiQiWHU/BWG.

BIOMEDICAL IMAGE SEGMENTATION BASED ON DEEP LEARNING ALGORITHMS

A new biomedical image segmentation method based on deep learning networks has been constructed. Under the framework of deep learning, a dual-branch deep learning network structure was designed to perform average pooling and maximum pooling on input image data, respectively. The results of pooling processing are sent to multi-layer convolution for further processing, where 2D convolution and dilated convolution are used, respectively, and the more flexible SoftMax function is selected for activation processing. Multiple fully connected processes were used between each link and between two branches to form better fusion criteria. The experimental results show that our method has a more ideal segmentation effect in both homologous and heterogeneous training segmentation experiments, with AC, SE, and SP indicators reaching over 95%, 84%, and 99%, respectively.

Adaptive active contour model based on local bias field estimation and saliency

Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively.

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching.

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

MF-Net: Automated Muscle Fiber Segmentation From Immunofluorescence Images Using a Local-Global Feature Fusion Network.

Histological assessment of skeletal muscle slices is very important for the accurate evaluation of weightless muscle atrophy. The accurate identification and segmentation of muscle fiber boundary is an important prerequisite for the evaluation of skeletal muscle fiber atrophy. However, there are many challenges to segment muscle fiber from immunofluorescence images, including the presence of low contrast in fiber boundaries in immunofluorescence images and the influence of background noise. Due to the limitations of traditional convolutional neural network-based segmentation methods in capturing global information, they cannot achieve ideal segmentation results. In this paper, we propose a muscle fiber segmentation network (MF-Net) method for effective segmentation of macaque muscle fibers in immunofluorescence images. The network adopts a dual encoder branch composed of convolutional neural networks and transformer to effectively capture local and global feature information in the immunofluorescence image, highlight foreground features, and suppress irrelevant background noise. In addition, a low-level feature decoder module is proposed to capture more global context information by combining different image scales to supplement the missing detail pixels. In this study, a comprehensive experiment was carried out on the immunofluorescence datasets of six macaques' weightlessness models and compared with the state-of-the-art deep learning model. It is proved from five segmentation indices that the proposed automatic segmentation method can be accurately and effectively applied to muscle fiber segmentation in shank immunofluorescence images.

Research on Chinese Audio and Text Alignment Algorithm Based on AIC-FCM and Doc2Vec

‘‘Audiobook” is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describes an audio and text alignment algorithm using deep learning and neural network technology to improve the efficiency and quality of audiobook production. The algorithm first uses dual-threshold endpoint detection technology to segment long audio into short audio with sentence dimensions and recognizes it as short text. The threshold is calculated by AIC-FCM optimized based on simulated annealing genetic algorithm. Then the algorithm uses Doc2vec optimized by the threshold prediction method based on the average length of the short text to calculate the text similarity. Finally, proofread and output the text sequence and audio segment aligned in the time dimension to meet the needs of audiobook production. Experiments show that compared to traditional audio and text alignment algorithms, the proposed algorithm is closer to the ideal segmentation result in long audio segmentation, and the alignment effect is basically the same as Doc2vec and the time complexity is reduced by about 35%.

Exploring Semantic Information Extraction From Different Data Forms in 3D Point Cloud Semantic Segmentation

As a critical step in 3D scene understanding, semantic segmentation of point clouds has broad application scenarios, including intelligent driving, augmented reality, smart factories, etc. Point cloud data is complex and irregular, and traditional machine learning methods are difficult to achieve ideal segmentation results. Deep learning techniques have yielded remarkable outcomes for researchers, leading to a surge in interest in investigating the semantic segmentation of point clouds. This article begins by examining the difficulties involved in segmenting point clouds by analyzing the inherent structural characteristics of point clouds. Then, commonly used datasets for point cloud semantic segmentation and evaluation metrics for assessing segmentation performance were introduced. Subsequently, an exploration was carried out on extracting semantic information from different data forms in point cloud semantic segmentation. Based on these findings, the experimental results of these methods on publicly available datasets are compared quantitatively. Lastly, several outlooks are presented regarding the future development of semantic segmentation techniques for 3D point clouds. The point cloud semantic segmentation techniques summarized in this paper are mainly from the state-of-the-art methods presented at top international conferences. The goal is to provide a comprehensive overview of this field’s state of the art and can be used as a reference for researchers and beginners.

Self-supervised machine learning for live cell imagery segmentation

Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall under supervised learning (SL) which requires vast libraries of pre-processed, human-annotated labels to train the ML algorithms. Such SL pre-processing is labor intensive, can introduce bias, varies between end-users, and has yet to be shown capable of robust models to be effectively utilized throughout the greater cell biology community. Here, to address this pre-processing problem, we offer a self-supervised learning (SSL) approach that utilizes cellular motion between consecutive images to self-train a ML classifier, enabling cell and background segmentation without the need for adjustable parameters or curated imagery. By leveraging motion, we achieve accurate segmentation that trains itself directly on end-user data, is independent of optical modality, outperforms contemporary SL methods, and does so in a completely automated fashion—thus eliminating end-user variability and bias. To the best of our knowledge, this SSL algorithm represents a first of its kind effort and has appealing features that make it an ideal segmentation tool candidate for the broader cell biology research community.

Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation.

To study the effect of computerized tomography (CT) images based on deep learning algorithms on the diagnosis of pulmonary nodules and the effect of radiofrequency ablation (RFA), the U-shaped fully convolutional neural network (FCNN) (U-Net) was enhanced. The convolutional neural network (CNN) algorithm was compared with the U-Net algorithm, and segmentation performances were analyzed. Then, it was applied to the CT image diagnosis of 110 lung cancer patients admitted to hospital. The patients in the observation group (55 cases) were diagnosed based on the improved U-Net algorithm, while those in the control group (55 cases) were diagnosed by traditional methods and then treated with RFA. The Dice coefficient (0.8753) and intersection over union (IOU) (0.8788) obtained by the proposed algorithm were remarkably higher than the Dice coefficient (0.7212) and IOU (0.7231) obtained by the CNN algorithm, and the differences were considerable (P < 0.05). The boundary of the pulmonary nodule can be segmented more accurately by the proposed algorithm, which had the segmentation result closest to the gold standard among the three algorithms. The diagnostic accuracy of the pulmonary nodule in the observation group (95.3%) was superior to that of the control group (90.7%). The long diameter, volume, and maximum area of the pulmonary nodule of the observation group were significantly higher than those of the control group, with substantial differences (P < 0.05). Patients were reexamined after one, three, and six months of treatment, and 71 patients (64.55%) had complete remission, 32 patients (29.10%) had partial remission, 6 patients (5.45%) had stable disease, and 1 patient (0.90%) had disease progression. The remission rate (complete remission + partial remission) was 93.65%. The improved U-NET algorithm had good image segmentation performance and ideal segmentation effect. It can clearly display the shape of pulmonary nodules, locate the lesions, and accurately evaluate the therapeutic effect of RFA, which had clinical application value.

PIIE-DSA-Net for 3D Semantic Segmentation of Urban Indoor and Outdoor Datasets

In this paper, a 3D semantic segmentation method is proposed, in which a novel feature extraction framework is introduced assembling point initial information embedding (PIIE) and dynamic self-attention (DSA)—named PIIE-DSA-net. Ideal segmentation accuracy is a challenging task, since the sparse, irregular and disordered structure of point cloud. Currently, taking into account both low-level features and deep features of the point cloud is the more reliable and widely used feature extraction method. Since the asymmetry between the length of the low-level features and deep features, most methods cannot reliably extract and fuse the features as expected and obtain ideal segmentation results. Our PIIE-DSA-net first introduced the PIIE module to maintain the low-level initial point-cloud position and RGB information (optional), and we combined them with deep features extracted by the PAConv backbone. Secondly, we proposed a DSA module by using a learnable weight transformation tensor to transform the combined PIIE features and following a self-attention structure. In this way, we obtain optimized fused low-level and deep features, which is more efficient for segmentation. Experiments show that our PIIE-DSA-net is ranked at least in the top seventh among the most recent published state-of-art methods on the indoor dataset and also made a great improvement than original PAConv on outdoor datasets.

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

The lesions of COVID-19 CT image show various kinds of ground-glass opacity and consolidation, which are distributed in left lung, right lung or both lungs. The lung lobes are uneven and it have similar gray value to the surrounding arteries, veins, and bronchi. The lesions of COVID-19 have different sizes and shapes in different periods. Accurate segmentation of lung parenchyma in CT image is a key step in COVID-19 detection and diagnosis. Aiming at the unideal effect of traditional image segmentation methods on lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of the lung, so that the segmentation threshold can change adaptively according to the detailed information of lung lobes, trachea, bronchi and ground-glass opacity. Then the lung parenchyma is further processed to obtain the lung parenchyma template, and then the defective template is repaired combined with the improved Freeman chain code and Bezier curve. Finally, the lung parenchyma is extracted by multiplying the template with the lung CT image. The accuracy of lung parenchyma segmentation has been improved in the contrast clarity of CT image and the consistency of lung parenchyma regional features, with an average segmentation accuracy rate of 97.4%. The experimental results show that for COVID-19 and suspected cases, the method has an ideal segmentation effect, and it has good accuracy and robustness.

3D Flattering Amplified Neural Network-Based Segmentation of Amygdala and Hippocampus

Abstract Recent emergence in deep learning resulted in significant improvement in the segmentation accuracy of sub cortical brain structures like hippocampus and amygdala. The traditional methods of segmentation cannot produce an ideal segmentation result that exhibits issues like redundant computations, inconsistencies, coefficient variations and motion artifacts. Therefore, in this paper, an improved 3D Flatteringly Amplified Neural Network model for biomedical imaging is efficiently proposed, which can make full use of the 3D spatial information of MRI image itself to overcome the inconsistency of segmented images along with equalizing the coefficient variation of tiny region of brain image segmentation. Also while equalizing the coefficient, certain significant minute details are lost due to motion artifacts hence, the robust Amyg-Hippo Seg algorithm has been introducing that extracts the features through deep learning, and achieve high-precision segmentation, it reduced the computational complexity without neglecting minute features. In addition, the Daytona dropout function provides uncertainty information and reduces over-fitting problems. The outcome of the proposed work efficiently segments the most significant regions of hippocampus and amygdala with 97.4% accuracy.

Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning.

Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.

A Lightweight Complex-Valued DeepLabv3+ for Semantic Segmentation of PolSAR Image

Semantic image segmentation is one kindof end-to-end segmentation method which can classify the target region pixel by pixel. As a classic semantic segmentation network in optical images, DeepLabv3&#x002B; can achieve a good segmentation performance. However, when this network is directly used in the semantic segmentation of polarimetric synthetic aperture radar (PolSAR) image, it is hard to obtain the ideal segmentation results. The reason is that it is easy to yield overfitting due to the small PolSAR dataset. In this article, a lightweight complex-valued DeepLabv3&#x002B; (L-CV-DeepLabv3&#x002B;) is proposed for semantic segmentation of PolSAR data. It has two significant advantages when compared with the original DeepLabv3&#x002B;. First, the proposed network with the simplified structure and parameters can be suitable for the small PolSAR data, and thus, it can effectively avoid the overfitting. Second, the proposed complex-valued (CV) network can make full use of both amplitude and phase information of PolSAR data, which brings better segmentation performance than the real-valued (RV) network, and the related CV operations are strictly true in the mathematical sense. Experimental results about two Flevoland datasets and one San Francisco dataset show that the proposed network can obtain better overall average, mean intersection over union, and mean pixel accuracy than the original DeepLabv3&#x002B; and some other RV semantic segmentation networks.

Dim Space Target Detection via Convolutional Neural Network in Single Optical Image

Real-time dim space target detection is a significant challenge in space situation awareness. This paper proposes a single-frame space object segmentation and detection method based on deep learning. Firstly, Channel and Space Attention U-net (CSAU-Net) is presented based on space image features. We remove unnecessary feature layers and add attention modules in the traditional encoder and decoder structure to enhance feature fusion and better use original feature layers. The proposed network structure can achieve accurate segmentation of space objects with fewer data training. At the same time, we construct a space target dataset for training, which contains targets with different signal-to-noise ratios to enhance the generalization of convolutional neural networks. After obtaining the segmentation masks, a simple connected component labeling method is applied to extract the centroid of the space target. Experiments show that our approach can achieve an ideal segmentation effect when the signal-to-noise ratio of the space target in the simulated dataset is 0.3. In addition, the proposed algorithm can realize fast segmentation and achieve an accuracy of 98.5%, which is similar to the traditional multi-frame space target detection method in real space image detection.

An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm

Region Growing Algorithm (RGA) is a popular, fast and strongly formed object segmentation method. In RGA, the region is grown from the seed points to adjacent points depending on an aggregation criterion. Despite the huge literature on RGA, none of the proposed aggregation criteria have been analytically proved to be suitable for an ideal segmentation. In this paper, Cauchy-Schwarz Divergence (CSD) is proved to be suitable as an aggregation criterion in RGA for object segmentation. First, RGA is formulated in this context. The Cauchy-Schwarz-based criterion is proposed here in the continuous case for a bimodal image that contains one object in the background while both regions are normally distributed with different parameters (while the assumption of normal distribution of object and background has been used by many researchers in minimum error thresholding method). Then, a proof is given that in the mentioned formulated case, the proposed RGA will lead to an ideal segmentation. The case is also investigated while object and background have heavy-tail distributions like generalized Gaussian function when β < 2. While all formulations and proofs are given in the continuous case, the segmentation results in the discrete case are shown to be good. Comparison of these results with the outcomes of RGA with traditional aggregation criteria, shows how analytical justifications can suggest a better criterion.

Edge Segmentation Algorithm‐Based Prevention of Recurrent Laryngeal Nerve Injury in Treatment of Thyroid Nodules Using Ultrasound Images

The study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery. Specifically, 186 patients with thyroid nodules were selected as the research objects. The segmentation performances of the gradient vector flow (GVF) Snake, Watershed, and Snake algorithms were compared from 6 aspects of image segmentation effects, pixel accuracy (PA), Intersection over Union (IOU) value, algorithm running time, postoperative recurrent laryngeal nerve injury, intraoperative bleeding volume, and postoperative drainage volume. It was found that the average PA value (0.954) and the IOU value (0.866) of the GVF Snake algorithm were obviously higher than those of the other two algorithms. The total incidence of recurrent laryngeal nerve injury based on the GVF Snake algorithm (4.69%) was obviously lower than that of the Snake algorithm (19.35%) and the watershed algorithm (16.13%). The bleeding volume and postoperative drainage volume based on the GVF Snake algorithm were less versus the other two algorithms ( P &lt; 0.05 ). In conclusion, the GVF Snake algorithm demonstrates ideal segmentation effects, which is suggested in the treatment of thyroid nodules to better protect the recurrent laryngeal nerve.

Local Gaussian Distribution Fitting Boundary Image Segmentation Algorithm for Ultrasound Images in Avoiding Recurrent Laryngeal Nerve Injury during Thyroid Nodules Treatment

This work aimed to analyze the effect of the boundary segmentation algorithm in thyroid nodules image segmentation and the influence of its adoption in avoiding recurrent laryngeal nerve (RLN) injury during treatment of thyroid nodules. The nodule boundary was extracted aided by the local Gaussian distribution fitting energy (LGDF) segmentation algorithm, which was compared with the normalized cut (Ncut) algorithm and the Canny algorithm. Then, 51 patients treated with microwave ablation for thyroid nodules were taken as a test group, and 51 patients treated with surgical resection were taken as a control group. The incidence of RLN injury and the levels of free triiodothyronine (FT3), free thyroid hormone (FT4), and thyroid-stimulating hormone (TSH) were compared between the two groups before and after treatment. The results showed that the true positive fraction (TPF) of the LGDF segmentation algorithm was 69.45%, the TPF of the Ncut algorithm and the Canny algorithm were 58.65% and 52.37%, respectively. The TPF of LGDF algorithm was higher than that of the Ncut algorithm and Canny algorithm, with notable differences ( P &lt; 0.05 ). In the control group, there were 10 cases of temporary and permanent damage to the RLN after operation, and the total incidence was 19.61%. In the test group, there were 3 cases of temporary and permanent damage to the RLN after operation, and the total incidence was 5.88%, which was lower than that of the control group ( P &lt; 0.05 ). No evident differences were shown in the levels of FT3, FT4, and TSH between the two groups before treatment ( P &gt; 0.05 ). However, after treatment, the TSH level of the test group (4.58 ± 0.79) was higher than that of the control group (3.19 ± 0.17), and the levels of FT3 and FT4 in the test group were lower than those in the control group, and the differences were remarkable ( P &lt; 0.05 ). In short, the LGDF algorithm had more ideal segmentation effect. In addition, ultrasound-guided microwave ablation was effective in treating benign thyroid nodules, which could reduce damage to the RLN and maintain normal thyroid function.