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Table 2 The list of operations in the segmentation and feature computation stages and the sources of the CPU and GPU versions

From: Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

Feature Computations
Class Operations Computed Features CPU and GPU Implementation
Pixel Statistics Histogram Calculation Mean, Median, Min, Max, 25 %, 50 %, and 75 % quartile Implemented
Gradient Statistics Gradient and Histogram Calculation Mean, Median, Min, Max, 25 %, 50 %, and 75 % quartile Implemented
Haralick Normalization pixel values and Co-occurrence matrix Inertia, Energy, Entropy, Homogeneity, Max prob, Cluster shade, Prominence Implemented
Edge Canny and Sobel Canny area, Sobel area OpenCV (Canny), Implemented (Sobel)
Morphometry Pixel counting, Dist. among points, Area and Perimeter, Fitting ellipse, Bounding box, Convex hull, Connected components, Area, Perimeter, Equivalent diameter, Compactness, Major/Minor axis length, Orientation, Eccentricity, Aspect ratio, Convex area, Euler number Implemented
  1. We used the implementation of the morphological reconstruction operation by Vincent for the implementation of several segmentation operations. Implemented indicates our implementation of the respective operations