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In brain tumor segmentation, the methods based on pixel classification are constrained to the use of supervised or unsupervised classifiers to cluster pixels in the feature space.
The superpixel-level classifier is directly affected by precision of pixel-level classification since the confidence of superpixel is calculated based on pixel classification results.
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In the first step, the pixels are grouped into components based on pixel connectivity.
The similarity indices were based on pixel by pixel (size: 1 ms × 86 Hz) comparisons between pairs of spectrograms.
Then, we classified the 8× 8 blocks based on the pixel classification results.
Similarly, points of interest (also called fiducial points) are located based on the pixel classification.
Based on manual classification, pixels were labeled as: fibrous, calcified or lipid-rich.
Based on the pixel-level classification result, we update the hand appearance model and optimize the upper layer classifier and detector.
Tarabalka [24] proposed a spectral-spatial classification scheme based on the pixel-wise SVM classification, followed by majority voting within the watershed regions.
A method to use different interpolation techniques based on classification of each missing pixel into two categories according to different local region gradient features is discussed in [5].
For pixel-level training, we select samples from the superpixels based on their classification confidence values.
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