Image Segmentation Method Research Articles

A new method for crop image segmentation based on 2D histogram using multi-strategy shuffled frog leaping algorithm

A new method for crop image segmentation based on 2D histogram using multi-strategy shuffled frog leaping algorithm

Convolutional Neural Network‐Based CT Image Segmentation of Kidney Tumours

ABSTRACTKidney tumours are one of the most common tumours in humans and the main current treatment is surgical removal. The CT images are usually manually segmented by a specialist for pre‐operative planning, but this can be influenced by the surgeon's experience and skill and can be time‐consuming. Due to the complex lesions and different morphologies of kidney tumours that make segmentation difficult, this article proposes a convolutional neural network‐based automatic segmentation method for CT images of kidney tumours to address the most common problems of boundary blurring and false positives in tumour segmentation images. The method is highly accurate and reliable, and is used to assist doctors in surgical planning as well as diagnostic treatment, relieving medical pressure to a certain extent. The EfficientNetV2‐UNet segmentation model proposed in this article includes three main parts: feature extractor, reconstruction network and Bayesian decision algorithm. Firstly, for the phenomenon of tumour false positives, the EfficientNetV2 feature extractor, which has high training accuracy and efficiency, is selected as the backbone network, which extracts shallow features such as tumour location, morphology and texture in the CT image by downsampling. Secondly, on the basis of the backbone network, the reconstruction network is designed, which mainly consists of conversion block, deconvolution block, convolution block and output block. Then, the up‐sampling architecture is constructed to gradually recover the spatial resolution of the feature map, fully identify the contextual information and form a complete encoding–decoding structure. Multi‐scale feature fusion is achieved by superimposing all levels of feature map channels on the left and right sides of the network, preventing the loss of details and performing accurate tumour segmentation. Finally, a Bayesian decision algorithm is designed for the edge blurring phenomenon of segmented tumours and cascaded over the output of the reconstruction network, combining the edge features of the original CT image and the segmented image for probability estimation, which is used to improve the accuracy of the model edge segmentation. Medical images in NII special format were converted to Numpy matrix format using python, and then more than 2000 CT images containing only kidney tumours were selected from the KiTS19 dataset as the dataset for the model, and the dimensions were standardised to 128 × 128, and the experimental results show that the model outperforms many other advanced models with good segmentation performance.

The effect of imaging programs and segmentation methods on the accuracy of volume measurements of teeth

ObjectiveThis study investigated whether there are differences between software programs, voxel sizes, segmentation techniques, and intraoral scanners in terms of volume measurement in incisor teeth. Study DesignThirty extracted teeth were scanned using a 3D intraoral scanner. Physical volumes were measured using the water displacement method (WDM) as the gold standard. Cone Beam Computed Tomography (CBCT) images were taken in two voxel sizes (0.3- 0.4 voxel). The volumes of the teeth were calculated using the manual segmentation technique in the 3D Doctor and ImageJ (Fiji) Program, manual and automatic segmentation methods in the ITK-Snap program, and automatic segmentation methods in the 3D Slicer program. The data were analyzed using the Statistical Package for Social Sciences (SPSS, v 20.0). ResultsThere is a significant difference between WDM and 3D Doctor volume measurements at the voxel size of 0.4 (p <0.05). The 3D Doctor program showed more than 13% difference compared to the WDM. There is no significant difference between WDM, intraoral scanner, ITK-Snap, ImageJ (Fiji), 3D Slicer, and 3D-Doctor volume measurements at the voxel size of 0.3. ConclusionMeasurements with manual segmentation in ITK-Snap and ImageJ programs give the closest results to the physical volume measurements of the teeth. The automatic segmentation method in ITK-Snap and 3D Slicer programs may be preferred due to its ease of use and less time consuming. In the 3D Doctor software, volume measurements tend to increase with larger voxel size. Statement of Clinical RelevanceDifferent software programs, voxel size, and segmentation techniques used in CBCT images affect the measurement results of tooth volumes. Very close to physical volume was obtained with the intraoral scanner. Measurements obtained through manual segmentation yield more accurate results.

Image semantic segmentation of indoor scenes: A survey

This survey provides a comprehensive evaluation of various deep learning-based segmentation architectures. It covers a wide range of models, from traditional ones like FCN and PSPNet to more modern approaches like SegFormer and FAN. In addition to assessing the methods in terms of segmentation accuracy, we propose to also evaluate the methods in terms of temporal consistency and corruption vulnerability. Most of the existing surveys on semantic segmentation focus on outdoor datasets. In contrast, this survey focuses on indoor scenarios to enhance the applicability of segmentation methods in this specific domain. Furthermore, our evaluation consists of a performance analysis of the methods in prevalent real-world segmentation scenarios that pose particular challenges. These complex situations involve scenes impacted by diverse forms of noise, blur corruptions, camera movements, optical aberrations, among other factors. By jointly exploring the segmentation accuracy, temporal consistency, and corruption vulnerability in challenging real-world situations, our survey offers insights that go beyond existing surveys, facilitating the understanding and development of better image segmentation methods for indoor scenes.

Image Segmentation Techniques: An In-Depth Review and Analysis

In the fields of computer vision and image processing, image segmentation is a very important task. The image is partitioned into segments or regions then the visual data can be understood, analyzed and used easily. In this article, a comprehensive review of image segmentation methods is presented. It covers both the strengths and the advantages of some techniques as well as the weaknesses and limitations of other algorithms. The techniques used in image segmentation can be divided into four groups based on thresholding, region, edge and deep learning techniques. The fundamental principles, popularity of algorithms and evaluation metrics are mentioned. Also, the applications domains and future directions with current challenges are presented.

ResTrans‐Unet: A Residual‐Aware Transformer‐Based Approach to Medical Image Segmentation

ABSTRACTThe convolutional neural network has significantly enhanced the efficacy of medical image segmentation. However, challenges persist in the deep learning‐based method for medical image segmentation, necessitating the resolution of the following issues: (1) Medical images, characterized by a vast spatial scale and complex structure, pose difficulties in accurate edge information extraction; (2) In the decoding process, the assumption of equal importance among different channels contradicts the reality of their varying significance. This study addresses challenges observed in earlier medical image segmentation networks, particularly focusing on the precise extraction of edge information and the inadequate consideration of inter‐channel importance during decoding. To address these challenges, we introduce ResTrans‐Unet (residual transformer medical image segmentation network), an automatic segmentation model based on Residual‐aware transformer. The Transformer is enhanced through the incorporation of ResMLP, resulting in enhanced edge information capture in images and improved network convergence speed. Additionally, Squeeze‐and‐Excitation Networks, which emphasize channel relationships, are integrated into the decoder to precisely highlight important features and suppress irrelevant ones. Experimental validations on two public datasets were carried out to assess the proposed model, comparing its performance with that of advanced models. The experimental results unequivocally demonstrate the superior performance of ResTrans‐Unet in medical image segmentation tasks.

Aggregate-aware model with bidirectional edge generation for medical image segmentation

Accurate segmentation of lesion areas plays an important role in medical imaging-assisted diagnosis and treatment. Accurate boundary information can help doctors develop precise surgical plans and improve patient prognosis. However, automatic segmentation methods often struggle to accurately segment edges due to the random shape, size, and location of regions of interest (ROI). This problem is compounded in medical images, where the difference in pixel intensity between foreground and background is significantly smaller than in natural images. In this study, we propose an aggregate-aware model with bidirectional edge generation (Ambeg) for medical image segmentation. To overcome the problem of blurred edges between foreground and background in medical images, we design a deep learning model via a multi-task learning strategy and obtain richer visual features to guide segmentation. Furthermore, an Edge Feature Fusion (EFF) module is developed to combine spatial correlation information of lesion edges between adjacent images for more accurate edge segmentation. Finally, we design a new evaluation metric, the Boundary DSC segmentation consistency measure, to evaluate the edge segmentation accuracy of medical image segmentation methods. We utilize dilation and erosion operations in morphological methods to construct lesion edge labels. In addition, we use expansion and erosion rates to regulate the dimensions of the edge region to assess the requirements of different diseases for edge segmentation accuracy. The proposed approach is particularly noteworthy for achieving state-of-the-art results on medical image segmentation datasets, including BraTS 2022 (MRI), BraTS 2020 (MRI) and COVID-19–20 (CT), which have different modalities of datasets. It has an impressive Hausdorff distance of 4.62 mm and a sensitivity score of 92.45 % on BraTS 2020. Compared with existing assessment methods such as Dice score, Boundary DSC segmentation consistency measure focuses on the hard-to-segment lesion edge region rather than the easy-to-segment lesion center region, which provides a more comprehensive reference for physicians to choose automatic segmentation methods. In addition, since the boundary shapes of medical images are complex and diverse, we utilize morphological methods to obtain the boundary labels to ensure the smoothness of the boundary. Moreover, the approach is easy to implement and has a low computational cost, making it an attractive option for practical medical imaging applications.

Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.

Nighttime image semantic segmentation with retinex theory

Nighttime image semantic segmentation is challenging due to low-light and diverse lighting conditions. A straightforward solution is to first enhance nighttime scene images to resemble daytime scene before performing segmentation. This kind of methods heavily rely on the enhancement quality. Inspired by the Retinex theory for low-light image enhancement, which decomposes an image into reflectance and illumination components, we propose a novel nighttime image segmentation method with Retinex theory (RNightSeg). Our core insight is to obtain high-quality illumination-independent reflectance component to enhance segmentation. Specifically, we apply a decomposition decoder to the backbone network for generating the reflectance component. In addition to the fidelity loss and Total Variation loss for the reflectance component regression, we model the brightening illumination component to enhance the nighttime image and apply the color constancy loss on the enhanced image. This helps to cope with the issue of low-light and diverse lighting in the nighttime scene. Finally, we fuse the reflectance decoder feature with the backbone feature and feed the fused feature to the segmentation decoder. Extensive experimental results on two widely used datasets demonstrate that the proposed RNightSeg achieves superior performance over some state-of-the-art segmentation methods. The code of our implementation is available at https://github.com/sunzc-sunny/RNightSeg.

Evaluating the Effectiveness of Panoptic Segmentation Through Comparative Analysis

Image segmentation method is extensively used in the fields of computer vision, machine learning, and artificial intelligence. The task of segmentation is to distinguish objects in images either by their boundaries or as entire objects from the entire image. Image segmentation methods are implemented as instance, semantic, and panoptic segmentation. In this article, the panoptic segmentation method, seen as an advanced stage of instance and semantic segmentation, has been applied to three datasets and compared with the instance segmentation method. Experimental results are presented visually. Numerical results have been analyzed with the Panoptic Quality (PQ) and Semantic Quality (SQ) metrics. It has been observed that the segmentation outcome was best for the CityScapes dataset for panoptic segmentation.

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

Lung X-ray image segmentation based on improved Unet deep learning network algorithm with GSConv module

In this paper, we propose an optimised image segmentation method by structurally innovating and improving the traditional Unet model and integrating the latest GSConv module. In our experiments, we integrate the GSConv module into the encoder and decoder parts of U-Net to take advantage of its excellent feature extraction and information transfer capabilities. In comparing the training process of the two models of Unet and GSConv Unet, it is found that GSConv Unet has faster convergence speed and better generalisation ability, and finally shows higher segmentation accuracy and iou values in the test part. From the segmentation results, GSConv Unet delineates the lung region more accurately and meticulously compared to Unet, providing an effective idea for lung X-ray image segmentation tasks. This research is of great significance, which not only improves the effectiveness of the image segmentation task but also brings new technological breakthroughs in the field of medical imaging. By introducing the GSConv module and optimising the Unet structure, we have successfully improved the precision and efficiency of lung X-ray image segmentation, providing doctors with a more reliable and accurate diagnostic tool.

Bellybutton: accessible and customizable deep-learning image segmentation

The conversion of raw images into quantifiable data can be a major hurdle and time-sink in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a sub-selection of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.

A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning.

Pathological images are considered the gold standard for clinical diagnosis and cancer grading. Automatic segmentation of pathological images is a fundamental and crucial step in constructing powerful computer-aided diagnostic systems. Medical microscopic hyperspectral pathological images can provide additional spectral information, further distinguishing different chemical components of biological tissues, offering new insights for accurate segmentation of pathological images. However, hyperspectral pathological images have higher resolution and larger area, and their annotation requires more time and clinical experience. The lack of precise annotations limits the progress of research in pathological image segmentation. In this paper, we propose a novel semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning (MCL-Net), which combines consistency regularization methods with pseudo-labeling techniques. The MCL-Net architecture employs a shared encoder and multiple independent decoders. We introduce a Soft-Hard pseudo-label generation strategy in MCL-Net to generate pseudo-labels that are closer to real labels for pathological images. Furthermore, we propose a multi-consistency learning strategy, treating pseudo-labels generated by the Soft-Hard process as real labels, by promoting consistency between predictions of different decoders, enabling the model to learn more sample features. Extensive experiments in this paper demonstrate the effectiveness of the proposed method, providing new insights for the segmentation of microscopic hyperspectral tissue pathology images.

A novel context-sensitive attitude entropy-based multiclass segmentation method for brain MR images using enhanced flow directional algorithm

A novel context-sensitive attitude entropy-based multiclass segmentation method for brain MR images using enhanced flow directional algorithm

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.

A temporal difference matrix for historical cumulative change detection in time series PolSAR data

To detect historical cumulative changes in land cover, this study introduces a novel time series PolSAR change detection method. The objective is to address the inefficiency of traditional detection methods that use multiple pairwise comparisons, as well as to alleviate false positives and false negatives caused by the insufficient utilization of polarization and spatial context information in previous methods. The proposed method initially constructs a temporal difference matrix for time series PolSAR images and computes the difference image (DI) using the maximum eigenvalue of this matrix to depict the cumulative changes over time. Subsequently, the DI is segmented using an MRF-based image segmentation method with a limited amount of supervised information, yielding the cumulative change binary map. The efficacy of the proposed method is validated using three time series datasets covering different scenes acquired by different sensors, namely GaoFen3, Radarsat2 and Sentinel-1. The experimental results demonstrate that the method effectively characterizes cumulative changes in the time series while simultaneously considering disparities in radar backscatter intensity and polarization correlation. As a result, it significantly reduces erroneous and missed change detections and surpasses the existing methods by achieving the highest Recall, F1-score, IOU, Kappa, and overall accuracy. Furthermore, the DI calculation method serves as a versatile change descriptor applicable not only for capturing cumulative changes in time series but also for describing changes between dual temporal images.

Global–local consistent semi-supervised segmentation of histopathological image with different perturbations

A histopathological image is a microscopic image applied to examine cellular and tissue structures and identify any abnormalities or disease processes. Histopathological image segmentation is a prerequisite step for analyzing histopathological images that can divide an image into meaningful regions or objects to accurately classify and analyze tissue structures, cellular regions, or particular histological entities. However, the existing deep learning based pathological image segmentation methods require huge annotation efforts from the pathologists, which is labor-intensive and time-consuming. In this scenario, it has become a hotspot to leverage abundantly available unlabeled data to help learn segmentation models given limited labeled data. In this paper, we propose a global–local consistent semi-supervised segmentation (GLCS) model that enforce the consistency of the segmentation results with weak and strong perturbations on unlabeled data. In GLCS, we firstly generate different weak perturbations for each unlabeled sample, and then add a regularization term to ensure the segmentation consistency among different weak perturbations. Next, different from the existing studies applying the regression methods to match the segmentation results among different perturbations, our methods are based on the generative adversarial learning that can keep the global structure consistency among unlabeled data with different strength of perturbations. Finally, we also add a patch-correlation based regularization term to preserve the local structure similarity among different perturbations images. We validate our GLCS on three datasets, i.e. Glas, Crag and MoNuSeg. The experimental results show that our method can achieve to the dice ratio of 90.35, 82.61 and 81.60 with 1:1 proportion of labeled data, which are significantly superior to the state-of-the-art semi-supervised histopathological image segmentation methods. Our code is public available at https://github.com/ISBELLAG/GLCS.

STUDY OF IMAGE SEGMENTATION METHODS WITH MRI IMAGES

Digital image processing is the use of a digital computer to process digital images. Image processing transforms input images into digital form for certain operations to obtain useful information. Segmentation is a well-known process used in image processing that partitions input images into different regions. Image segmentation is a sub-area of computer vision and digital image processing for grouping similar segments of an image under respective class labels. Several methods were performed with neutrosophic sets on dissimilar image-processing domains. However, the denoising and segmentation were not carried out accurately with minimal time complexity. To address these issues, many image segmentation methods are reviewed.

A lie group semi-supervised FCM clustering method for image segmentation

As an unsupervised clustering method with low overhead, Fuzzy C-means (FCM) clustering has been widely used in a variety of image segmentation tasks. However, existing FCM clustering methods are sensitive to image noises and are either suffer from losing of image detail or falling into local optima in identifying cluster centers. Aiming at these problems, this paper proposes a Lie group semi-supervised FCM (LieSSFCM) clustering method for image segmentation. The method maps the input image from Euclidean space to Lie group manifold by representing each image pixel as a matrix Lie group and calculates geodesic distances between group elements and cluster centers on Lie group manifold. Prior information of the image and neighborhood relationships of pixels are used to guide the initialization and constrain the update of cluster centers and the corresponding fuzzy membership matrix. The proposed LieSSFCM has been validated against two medical image datasets and was compared with seven FCM clustering methods. Experimental results along with a systematic evaluation demonstrated that the method was superior in segmentation accuracy both visually and statistically, robustness to noises, adaptability to different tasks, and stability while maintaining a moderate computational complexity.