Mean Curvature Flows, Edge Detection, and Medical Image Segmentation

2003 ◽  
pp. 856-870 ◽  
Author(s):  
Cornelius T. Leondes
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Mean Curvature ◽  
Medical Image ◽  
Medical Image Segmentation

Medical Imaging Importance in the Real World

2020 ◽  
pp. 136-162
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Level Set ◽  
Real World ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Variational Model ◽  
Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

scholarly journals PERFORMANCE ANALYSIS OF SELECTED MEDICAL IMAGE SEGMENTATION ALGORITHMS.

2016 ◽  
Vol 15 (10) ◽  
pp. 7140-7149
Author(s):  
Falohun A.S ◽  
Arulogun O.T ◽  
Ismaila W.O ◽  
Awokola J.A
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Performance Analysis ◽  
Edge Detection ◽  
Data Processing ◽  
Medical Image ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Segmentation Algorithms ◽  
Significant Step

The ability to understand images is one of the key areas of research where the target is to understand the components of an image and interpret its meaning. The components of images are not easily understood and interpreted for analysis, therefore image segmentation technique is very necessary and should be taken as significant step during image processing and analysis. Repeatable experiments are required for this research field to progress as image segmentation needed for data processing cannot be adequately achieved in a particular technique. Hence performance analysis of various, commonly used techniques are often needed. However, in this work, three classes of image segmentation algorithms: Edge Detection, Clustering and Thresholding method were simulated in MatLab on infected bladder. The experiments were performed three times to confirm the accuracy of the results evaluated with Dice, Jaccard and cosine co-efficient of fitness values.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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