In the first stage of this work, well-known and contrasted algorithms as logarithmic transformation, bilateral filter [12], Canny [17, 24, 32], and morphological filtering were used.
Morphological filtering was applied as a final image processing step in order to eliminate background elements around the primary coronary arteries.
Morphological filtering was chosen over other methods such as the temporal approach of the EEG signal and wavelet-based approach due to the fact that morphological filtering can precisely determine the spikes with a very high accuracy rate [14].
In [32], the total variation based image restoration and morphological filtering are applied to the hazy image, which can prevent producing unnecessary texture details from the estimated transmission map.
This is performed by a complete partitioning of image C (the original TFD) into n disjoint sub-regions, C1, C2,..., C n. Morphological filtering was used in the analysis of TFDs in several applications, for example, musical transcription of audio recording [47] and analysis of submarine and seismic signals [48, 49].
A morphological filter is used to sharpen these detected edges.
Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW.
Once the order of the morphological filter is calculated, the segmented image is denoised using (13).
