Research on image filtering method to combine mathematics morphology with adaptive median filter

Anqing, Yang

doi:10.1049/cp.2010.1152

Cited by 6 publications

(1 citation statement)

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“…Median filters are very well known in light of the fact that, for particular sorts of random noise they give fantastic noise reduction capabilities, with significantly less obscuring than linear smoothing filters of similar size [19]. The median is much less sensitive than the mean to extreme values(called outliers).…”

Section: Median Filtermentioning

confidence: 99%

Detection and 3D Visualization of Brain Tumor using Deep Learning and Polynomial Interpolation

Tuhin

Pramanick

Emon

et al. 2020

2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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Among different imaging techniques MRI, MRSI and CT scans are some of the widely use techniques to visualize brain structures to point out brain anomalies especially brain tumor. Identification of brain tumor accurately in clinical practices has always been a hard decision for neurologist as multiple exceptions might present in images which may lead dubious suggestion from neurologist.In our proposed model we are aiming towards brain tumor detection and 3d visualization of tumor more accurately in efficient way. Our proposed model composed of three stages such as classification of image using CNN whether any tumor exists of not; segmentation using multi thresholding to extract the detected tumor; and 3d visualization using polynomial interpolation. the proposed model enables enhancing the accuracy of tumor detection as compare to existing models as well as segmenting and 3d visualizing the detected tumor. we get 85% accuracy on our model comparing with others which is slightly more efficient in terms of classification and detection.

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Section: Median Filtermentioning

confidence: 99%