Medical image enhancement using sliding weighted empirical mode decomposition

Liang, Lin-Mei; Si, Yanna

doi:10.1109/icinfa.2015.7279829

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The adoption of Verilog scheme was seen in the work of Chiuchisan [13] for prototyping the image enhancing scheme. Usage of sliding decomposition approach for analyzing the detailed feature of medical images in the work of Liang and Si [14]. Gong et al [15] have introduced chaotic approach in order to enhance the performance of immune algorithm for enhancing the brain images.…”

Section: Introductionmentioning

confidence: 99%

Framework for comprehensive enhancement of brain tumor images with single-window operation

Murthy¹,

Sadashivappa²

2020

IJECE

View full text Add to dashboard Cite

Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.

show abstract

Section: Introductionmentioning

confidence: 99%