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The tracks are then segmented and characterized by multi-parametric, morphometric analysis, resolving a variety of morphological and kinetic features.
Then, the region-based morphological and kinetic features are utilized to identify the malignancy score of lesion candidates.
A recent study for mass-like-enhancing lesions[5] has shown that both morphological and kinetic features outperform spatiotemporal features.
Eventually, a discrimination step is used by relying on the morphological and kinetic features as inputs to the support vector machine (SVM) classifier to reduce false positives detections.
In an automated step, we will extract both morphological and kinetic features and use them as potential cues for non-standard lesion detection.
For classifying the potential lesion regions, morphological and kinetic features are calculated after applying the 3D-connected component algorithm [27, 28] on the potential lesion regions.
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Finally, the spurious candidate regions are removed by 3D morphological characteristics and kinetic features.
Moreover, 3D morphological characteristics and kinetic features are utilized to eliminate the spurious candidate regions.
The two values were increased by 12.1% and 2.2% more than by using morphological, texture and kinetic features together.
For example, the maximum specificity and sensitivity values were 0.722 and 0.924, which were increased by 30%and4.8%8% more than by using morphological, texture and kinetic features together, when using SVM on entire feature groups.
We have substantially expanded description and analysis of filopodia initiation from dendritic nodes, showing now in detail morphological rearrangements and kinetic behavior of ArhGAP44 and actin at such sites.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com