Computerized detection of masses from digitized mammograms: Comparison of single-image segmentation and bilateral-image subtraction

1995 ◽  
Vol 2 (12) ◽  
pp. 1056-1061 ◽  
Author(s):  
Bin Zheng ◽  
Yuan-Hsiang Chang ◽  
David Gur
Keyword(s):  
Image Segmentation ◽  
Single Image ◽  
Image Subtraction

Single Image Noise Estimation Based on Image Segmentation and Scatter Statistics of Noise

2016 ◽  
Vol 53 (4) ◽  
pp. 041006
Author(s):  
华玮平 Hua Weiping ◽  
赵巨峰 Zhao Jufeng ◽  
李梦 Li Meng ◽  
崔光茫 Cui Guangmang
Keyword(s):  
Image Segmentation ◽  
Image Noise ◽  
Noise Estimation ◽  
Single Image

scholarly journals Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

2018 ◽  
Vol 7 ◽  
Author(s):  
Yuma Kinoshita ◽  
Hitoshi Kiya
Keyword(s):  
Image Segmentation ◽  
Image Enhancement ◽  
State Of The Art ◽  
Compensation Method ◽  
Parameter Setting ◽  
Segmentation Method ◽  
Single Image ◽  
Exposure Fusion ◽  
Comparison Results ◽  
Enhancement Method

In this paper, an automatic exposure compensation method is proposed for image enhancement. For the exposure compensation, a novel image segmentation method based on luminance distribution is also proposed. Most single-image-enhancement methods often cause details to be lost in bright areas in images or cannot sufficiently enhance contrasts in dark regions. The image-enhancement method that uses the proposed compensation method enables us to produce high-quality images which well represent both bright and dark areas by fusing pseudo multi-exposure images generated from a single image. Here, pseudo multi-exposure images are automatically generated by the proposed exposure compensation method. To generate effective pseudo multi-exposure images, the proposed segmentation method is utilized for automatic parameter setting in the compensation method. In experiments, image enhancement with the proposed compensation method outperforms state-of-the-art image enhancement methods including Retinex-based methods, in terms of both entropy and statistical naturalness. Moreover, visual comparison results show that the proposed compensation method is effective in producing images that clearly present both bright and dark areas.

Medical Image Segmentation Algorithm Based on Granular Computing

2012 ◽  
Vol 532-533 ◽  
pp. 1578-1582
Author(s):  
Fang Wang ◽  
Juan Juan Ruan ◽  
Gang Xie
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Granular Computing ◽  
Research Direction ◽  
Medical Image Segmentation ◽  
Image Feature ◽  
Segmentation Method ◽  
Single Image ◽  
Quotient Spaces ◽  
Image Segmentation Algorithm

Granular Computing theory is a interesting research direction in artificial intelligence field. In this paper, granular computing theory is applied to medical image segmentation. Granularity thinking in image segmentation is expounded, and a novel medical image segmentation method is proposed. Firstly, we construct different granularities according to different features that the image contained, secondly, do the attributes combination to the obtained quotient spaces according to the quotient space granularity synthesis principle, and then complete the image segmentation. Compared with the methods adopting single image feature, this method may fully use the image information in a more effective way and may obtain better segmentation effects.

scholarly journals Single Image Segmentation with Estimated Depth

2012 ◽  
Author(s):  
Ryo Yonetani ◽  
Akisato Kimura ◽  
Hitoshi Sakano ◽  
Ken Fukuchi
Keyword(s):  
Image Segmentation ◽  
Single Image

An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

2013 ◽  
Vol 333-335 ◽  
pp. 839-844
Author(s):  
Kai Hong Shi ◽  
Zong Qing Lu ◽  
Qing Min Liao
Keyword(s):  
Image Segmentation ◽  
Pharmaceutical Industry ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Surface Fitting ◽  
Segmentation Method ◽  
Image Subtraction ◽  
Low Contrast ◽  
X Ray ◽  
Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

scholarly journals Gaussian Dynamic Convolution for Efficient Single-Image Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Xin Sun ◽  
Xin Sun ◽  
Changrui Chen ◽  
Xiaorui Wang ◽  
Junyu Dong ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Single Image

scholarly journals Vit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS

2021 ◽  
Author(s):  
Yigit Gunduc
Keyword(s):  
Image Segmentation ◽  
Depth Perception ◽  
General Purpose ◽  
Single Image ◽  
Semantic Image Segmentation ◽  
Image Translation ◽  
Image Depth

In this paper, we have developed a general-purpose architecture, Vit-Gan, capable of performing most of the image-to-image translation tasks from semantic image segmentation to single image depth perception. This paper is a follow-up paper, an extension of generator based model [1] in which the obtained results were very promising. This opened the possibility of further improvements with adversarial architecture. We used a unique vision transformers-based generator architecture and Conditional GANs(cGANs) with a Markovian Discriminator (PatchGAN) (https://github.com/YigitGunduc/vit-gan). In the present work, we use images as conditioning arguments. It is observed that the obtained results are more realistic than the commonly used architectures.

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