An Improved Watershed Image Segmentation Algorithm Based on Fuzzy Threshold

The cell image segmentation has very high practical significance in medical diagnosis. But the cell image has the problems of accretive cells, incoherent cell boundary, and the internal cavity that make it difficult to image segmentation. In this paper, a watershed algorithm based on distance transform is proposed to solve images of cells adhesion. Firstly, image enhancement is carried out as the image preprocessing, then the OTSU threshold segmentation is used to rough segment the image, finally the watershed algorithm by optimizing the seed points is adopt for fine segmentation. Experiment results showed that the proposed algorithm is effectively solved the problems of cell adhesion and over-segmentation, the image segmentation accuracy is more than the traditional watershed algorithm, and remained the cell shape in maximum extent. Therefore, the watershed segmentation based on distance transformation transform is practical according to the accretive cell images.

It is the key step of automatic apple picking and sorting to segment of the apple image in natural environment using image information processing technology. In order to improve the intellectualization of apple picking process and effectively extract the target of image segmentation, a novel image segmentation method of Huaniu apple based on pulse coupled neural network (PCNN) and watershed algorithm is proposed. Firstly, the PCNN model is improved and optimized on the basis of image RGB to L*A*B space conversion. Then, the improved PCNN combined with watershed algorithm was used to segment the apple image on considering the image texture and color feature information. The theoretical analysis and experimental results show that the novel method is effectively able to segment target image, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the traditional watershed and other related algorithms.

For industrial flotation foam image processing, accurate bubble size measurement and feature extraction are very important to optimize the flotation process and to improve the recovery of mineral resources. This paper presents an improved algorithm to investigate mineral flotation foam image segmentation for mineral processing. Several libraries implemented for the Python programming language are used for image enhancement and compensation, quantitative analysis of factors influencing the image segmentation accuracy, and suggestions for improvement of the flotation foam image processing. The bubble characteristics-size and morphology-and the influence of the flotation conditions on the flotation foam image are analyzed. A Python implementation of the Retinex image compensation method-region-adaptive and multiscale-is proposed to address known issues of uneven illumination and shadows affecting flotation foam images, thereby improving brightness uniformity. Finally, an improved version of the watershed segmentation algorithm included in the Python Open Source Computer Vision library is used for segmentation analysis. The accuracy of the flotation foam image segmentation is 3.3% higher than for the standard watershed algorithm and the segmentation time is 9.9% shorter.

Deep learning-based segmentation algorithms for medical image require massive training datasets with accurate annotations, which is costly since it takes much human effort to manually labeling from scratch. Therefore, interactive image segmentation is important and may greatly improve the efficiency and accuracy of medical image labeling. Some interactive segmentation methods (e.g. Deep Extreme Cut and Deepgrow) may improve the labeling through minimal interactive input. However these methods only utilize the initial manually input information, while existing segmentation results (such as annotations produced by nonprofessionals or conventional segmentation algorithms) cannot be utilized. In this paper, an interactive segmentation method is proposed to make use of both existing segmentation results and human interactive information to optimize the segmentation results progressively. In this framework, the user only needs to click on the foreground or background of the target individual on the medical image, the algorithm could adaptively learn the correlation between them, and automatically completes the segmentation of the target. The main contributions of this paper are: (1) We adjusted and applied a convolutional neural network which takes medical image data and user's clicks information as input to achieve more accurate segmentation of medical images. (2) We designed an iterative training strategy to realize the applicability of the model to deal with different number of clicks data input. (3) We designed an algorithm based on false positive and false negative regions to simulate the user's clicks, so as to provide enough training data. By applying the proposed method, users can easily extract the region of interest or modify the segmentation results by multiple clicks. The experimental results of 6 medical image segmentation tasks show that the proposed method achieves more accurate segmentation results by at most five clicks.

Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization; however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −1024 Hu~−107 Hu, −1024 Hu~−103 Hu, and −1024 Hu~−160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −103 Hu~−50 Hu, −100 Hu~−38 Hu, −153 Hu~−51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from −8 Hu~18 Hu, −8 Hu~13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu~3071 Hu, 20 Hu~3071 Hu, and the brine CT values of the Arctic sea ice ranged from −6 Hu~3071 Hu. The errors between the three-dimensional ice model divided by threshold ranges and measured sediment content were less than 0.003 g/cm3, which verified the high accuracy of the established microscopic model. It provided a scientific basis for ice engineering, ice remote sensing, and ice disaster prevention.

In recent years, the use of computer technology in medical image processing and analysis has been a hot issue of research and application at home and abroad, and the application of the PACS system has created a new era of digital medical treatment. Based on the technology of PACS system for post-processing segmentation, an improved watershed algorithm has been brought up to the medical image segmentation. Compared with the original algorithm, the improved algorithm greatly improves the accuracy of image segmentation; and the problem of over-segmentation of the original algorithm has been solved better. Dynamic merge rule is added in process of implementing watershed algorithm and the formation of over-segmentation regions due to the algorithm itself are restrained directly. Applying the improved algorithm to the PACS system, it has been proved that the algorithm can effectively extract the medical image lesions.KeywordsPACS systemMedical image segmentationWatershed algorithmDynamic merge

Image segmentation is the process of partitioning an image into meaningful segments. The meaning of the segments is subjective due to the definition of homogeneity is varied based on the users perspective hence the automation of the segmentation is challenging. Watershed is a popular segmentation technique which assumes topographic map in an image, with the brightness of each pixel representing its height, and finds the lines that run along the tops of ridges. The results from the algorithm typically suffer from over segmentation due to the lack of knowledge of the objects being classified. This paper presents an approach to reduce the over segmentation of watershed algorithm by assuming that the different adjacent segments of an object have similar color distribution. The approach demonstrates an improvement over conventional watershed algorithm.

In this paper, an effective synthetic aperture radar image segmentation method is proposed. Gaussian mixture models optimized by greedy expectation maximization algorithm are applied. The immune genetic algorithm is employed to initialize greedy expectation maximization algorithm and search the optimal values in the whole range, instead of general k-means algorithm, which is different from the traditional algorithm. Experimental results show our method can get better results for target segmentation. It can effectively segment the object from SAR images and inhibit speckle noise.

Coal particle characteristic extraction is essential to prevent dust explosions. It is traditionally analyzed by manual screening, which is relatively time-consuming and cannot automatically capture outlining. To address this issue, an accurate and automatic particle image segmentation method is highly demanded in the smart mine. Thus, a novel characteristic learning approach that applied simplified VGGNet as a backbone network is investigated to learn the feature details of particle image sample. The sample set includes 3000 dust images captured from coal preparation plants. First, hierarchical features are extracted step by step on the improved VGGNet. Meanwhile, the feature maps obtained via convolution are sent to the dual attention mechanism module to determine the global feature weights, and the particle characteristic information is optimized. Afterward, an erosion-dilation module is applied to achieve dense texture separation in the deep feature map. Finally, the particles are segmented by upsampling on unpooling. The experimental results show that the proposed method achieves better precision, recall, and ${F}1$ with 0.8743, 0.8351, and 0.8543, respectively, than other previous methods. Compared with laser diffractometry, the maximum error $\varepsilon $ is 5.115% in the range of $R \le 75~\mu \text{m}$ , and this is consistent with the expected. The proposed method outperformed other state of the arts on segmentation results and can be applied in coal dust detection for enterprise as a viable alternative.

Objective. Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach. Approach. In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information. Main results. The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods. Significance. Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.

Background/Objectives: Clinical imaging techniques, such as magnetic resonance imaging and computed tomography, play a vital role in supporting clinicians by aiding disease diagnosis and facilitating the planning of appropriate interventions. This is particularly important in malignant conditions like hepatocellular carcinoma, where accurate image segmentation, delineating the liver and tumor, is a critical initial step in optimizing diagnosis, staging, and treatment planning, including interventions like transplantation, surgical resection, radiotherapy, portal vein embolization, and other procedures. Therefore, effective segmentation methods can significantly influence both diagnostic accuracy and treatment outcomes. Method: In this paper, we propose a deep learning-based approach aimed at accurately segmenting the liver in medical images, thus addressing a critical need in hepatic disease diagnosis and treatment planning. We consider a U-Net architecture with residual connections to capture fine-grained anatomical details. We also take into account the prediction explainability, aiming to highlight, in the image under analysis, the areas that are symptomatic for a certain segmentation. In detail, by exploiting the U-Net architecture, two different models are trained with two annotated datasets of computed tomography medical images, resulting in four different experiments. Results: We consider two different datasets to improve robustness and generalization across diverse patient populations and imaging conditions. Experimental results demonstrate that the proposed method obtains interesting performances, with an accuracy ranging from 0.81 to 0.93. Conclusions: We thus show that the proposed method can provide a reliable and efficient solution for automated liver segmentation, promising significant advancements in clinical workflows and precision medicine.

Accurate segmentation of ore images plays a significant role in automatic geometric parameter detecting and composition analyzing of ore dressing progress. Semantic segmentation based on deep learning is a promising method for accurate ore image segmentation. However, the similar appearance with low contrast and blurry boundary of ores in image hamper segmentation accuracy. Moreover, it is difficult to train a deep network due to limited available ore images. In this work, an improved encoder-decoder network based on U-Net is proposed to handle above challenges. A contour awareness loss (CAL) is proposed to improve model sensitivity to misclassified pixels, pixels of similar appearance, and pixels near the boundary. The proposed scheme is verified on ore images by benchmarking against state-of-the-art segmentation methods. Experiment results show that the proposed scheme achieves 88.6% pixel-wise accuracy (PA) and 66.0% mean intersection over union (mIoU).

New nanomaterials (metal nanoclusters, graphene, etc.) are favored by researchers due to their unique properties and are widely used in biomedical detection. The excellent fluorescence characteristics of gold nanoclusters are utilized to develop a fast and highly sensitive bionic nanomaterial with non-label and dual functions, which can detect silver ions and mercury ions and study the particularity of TEM nanoparticle images. The particle segmentation of TEM nanoparticle images is studied to compare the traditional watershed algorithm and watershed transformation algorithm. The experiment results show that silver ions can enhance the fluorescence of gold nanoclusters to form gold-silver nanoclusters with strong yellow fluorescence, and mercury ions can quickly weaken the fluorescence of gold-silver nanoclusters. Based on the biomimetic nanomaterials, a dual-function fluorescent probe is designed to detect silver ions and mercury ions in lake with detection accuracy of 8 nM and 33 nM respectively; the sensing excitation of the fluorescent probe is further analyzed. Because the metal-enhanced fluorescence (MEF) effect enables the silver element and Au nanoparticles to form fluorescence-enhancing effect, the high metalphilic interaction between mercury ions and silver ions quenches the fluorescence effect of gold nanocluster; the rapid watershed transformation/region fusion method can achieve better particle image segmentation combined with the image segmentation algorithms of different TEM nanoparticles, which can be better applied to the characterization analysis of the preparation of gold nanomaterials.

One of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%.