Automatic Spiking Pulse-Coupled Neural Network for Image Segmentation

Xi, Jun Tong

doi:10.14257/ijfgcn.2017.10.2.03

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computed tomography (CT) has the advantages of fast imaging and high image resolution. It is an important electronic imaging technology [1,2]. As an important detection method in routine clinical examinations, it has become a computer-assisted human organ examination, and followup an important basis for medical treatment is currently widely used in clinical medicine [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions are as follows: (1) Using superpixels to improve the region growth criterion, the determination of the initial contour is more accurate. (2) The paper improves the Mumford-Shah model, fully considers the pixel texture characteristics, and increases the accuracy of CT image segmentation. (3)Various types of CT image data are used for experimental analysis, and the selection of experimental indicators is abundant, which greatly increases the validity of the experimental results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

Wei

Wang

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%