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Figure 7 | BMC Medical Imaging

Figure 7

From: Histological image classification using biologically interpretable shape-based features

Figure 7

The data flow for extraction of 900 shape-based features from a histological image. First, we segment the RGB histological image based on stains: blue (nuclei), pink (cytoplasm), and white (no-stain/gland). Then based on segmented results, we generate three binary masks corresponding to three stains (blue:b, white:w, pink:p). For each mask, we obtain the contour for all shapes after noise filtering using connected component analysis. Nm is number of shapes in m mask, where m {b, w, p}. We then extract shape axes descriptors (2 axes*10 harmonics) for each shape contour and bin them to produce 2*10 histograms for each mask (3 masks*10*2 histograms in an image). Due to the variation in dynamic range of the two axes and harmonics, we use data-dependent histogram ranges with 15 bins per histogram. We use the histogram frequencies as features for our image classification.

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