How do I segment an image in Photoshop?
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17 Nov 2009 10 Citations | Hence, to fill the gap, this paper presents an efficient segmentation algorithm that can segment a digital image of interest into a more meaningful arrangement of regions and objects. |
08 Oct 2007 | Experimental results proved that the improved algorithm was an effective method for the image segmentation in the practical application, which could segment the object accurately. |
12 Oct 2008 | We define a good image segment as one which can be easily composed using its own pieces, but is difficult to compose using pieces from other parts of the image. |
| The results demonstrate the potential of this approach in the field of medical image segmentation. By using the proposed method, we can find the appropriate local values and segment the prostate. | |
21 Jun 2004 9 Citations | As simulation results show, this algorithm can segment object image effectively. |
23 Aug 2010 9 Citations | In this paper, we propose a new image segmentation method to segment retinal images. |
4 Citations | The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step. |
11 Citations | This approach can segment different objects in an image. |
Related Questions
How to segment ultrasound image ?5 answersUltrasound image segmentation can be achieved using various methods. One approach is to use convolutional neural networks (CNNs) to segment the images. This has been proven effective in segmenting 2D cardiac ultrasound images. Another method is to use a novel 2D segmentation neural network design inspired by Bayesian networks. This technique samples from the volume around the query slice and performs multiple segmentations to improve accuracy. Additionally, a network combining a transformer model with a deep neural network model has been proposed for ultrasound image segmentation. This model utilizes attention mechanisms and achieves high precision and recall rates. Another approach is to use an end-to-end convolutional neural network (CNN) structure that directly processes raw radio frequency (RF) data to obtain segmented images. This method has shown good results in both simulated medical cysts segmentation and actual industrial defects segmentation. Finally, a segmentation algorithm that combines a learned probabilistic model with energy functionals has been proposed. This approach effectively overcomes the impact of poor image quality and improves segmentation accuracy.
What is the best method for ultrasound segmentation?4 answersThe best method for ultrasound segmentation is a conditional GAN (cGAN)-based approach proposed by You et al.. They use generative adversarial networks to expand the dataset and enhance the reality of breast ultrasound images. They also introduce a new network called EfficientUNet, which combines ResNet18, an attention mechanism, and a deep supervision technique to improve the accuracy of segmentation. Another effective method is presented by Weld et al., where they develop a 2D segmentation neural network inspired by Bayesian networks. This technique samples from the volume around the query slice and performs multiple segmentations to improve accuracy. Dai et al.propose a deep learning model that combines supervised and unsupervised learning for accurate prostate segmentation. Fang et al.integrate an active contour model with a generalized linear model to achieve accurate segmentation results. Zhang et al.propose an end-to-end, multiple-channel, and atrous CNN called MA-Net, which extracts semantic information for ultrasound image segmentation with high generalization ability.
What are the challenges in segmenting an image into regions?5 answersSegmenting an image into regions poses several challenges. These challenges include diverse visual patterns, noise, complex backgrounds, and similar textures present in both the foreground and background. Another challenge is the use of linear interpolation in the up-sampling stage of deep learning networks, which can result in blurred and grided object contours. In addition, segmenting unstructured regions and considering region semantics are also challenging tasks. Furthermore, the manual parameter selection process for complex network models is ineffective and can lead to suboptimal performance. Overall, the challenges in image segmentation involve handling diverse visual patterns, noise, complex backgrounds, similar textures, blurred object contours, unstructured regions, and the selection of optimal parameters for complex network models.
What is the significance of crack segmentation?5 answersCrack segmentation is significant for various reasons. Firstly, it is essential for maintaining the safety of infrastructure by identifying and measuring cracks accurately. Secondly, high-precision pixel-level crack segmentation provides valuable information about the degradation of infrastructure performance. Thirdly, crack segmentation models can be used to optimize coating properties in industrial applications, such as Air Plasma Spray (APS) coatings. Lastly, crack segmentation models based on deep learning technology offer advantages like high detection accuracy, fast detection speed, and simple operation, making them useful for detecting and analyzing cracks in transportation infrastructure.
What means segmentation in medical imaging?5 answersSegmentation in medical imaging refers to the process of dividing a processed image into distinct segments or regions, with the aim of reducing noise and facilitating analysis. It is a crucial step in medical image analysis as it allows for the identification of specific areas or structures of interest within the image. This segmentation can be done even when areas and pixels are not directly correlated, making it easier to detect flaws or abnormalities in the image. Various techniques have been developed for medical image segmentation, each with its own scope, prospective applications, and methods for separating the preprocessed image into segments. The accuracy and precision of these segmentation methods are important factors to consider in their applicability and reliability.
Segment anything 모델을 어떻게 작동해?5 answersThe Segment Anything Model (SAM) is a foundation model for general image segmentation. It can be operated in two main modes: automatic everything and manual prompt. SAM has achieved impressive results in natural image segmentation tasks. However, its performance in medical image segmentation (MIS) needs further validation. SAM's performance was evaluated on a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. It was found that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. SAM's 'segment anything' mode can achieve clinically acceptable segmentation results in most organs-at-risk (OARs) in clinical radiotherapy, with further improvement using the 'box prompt' mode. SAM's performance in medical image segmentation varies depending on the dataset and task, with better performance for well-circumscribed objects and box prompts.