Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images

Figure 1 Proposed HAF-DLac scheme along with a working example.

Math 10 Math(A1).

Figure 2 Curvelet-based decomposition of a wireless capsule endoscopy image. A: Decomposition of the Y channel of an ulcer image at scale three and eight angles; B: Mean energy of each angle depicted in (A).

Math 11 Math(A1).

Figure 3 Boxplot of local gradient of DLac curves vs analysis scale (parameter w) for efficient (black) and non-efficient (gray) sub-images at curvelet space based on[6]. +: Extreme value.

Figure 4 Six Wireless Capsule Endoscopy images of the adopted dataset and corresponding regions of interest: (from top to bottom) normal case, severe Crohn's disease lesion and mild Crohn’s disease lesion.

Figure 5 Classification ACC values using both fitness functions LFF (lacunarity curve gradient-based fitness function) and EFF (energy-based fitness function), for the R-individual channel case, all severity cases and all feature vector (FV) types [see (2) - (6)].

Figure 6 Classification ACC values using both fitness functions LFF (lacunarity curve gradient-based fitness function) and EFF (energy-based fitness function), for the NR-individual channel case, all severity cases and all feature vector (FV) types [see (2) - (6)].

Figure 7 Classification ACC values using both fitness functions LFF (lacunarity curve gradient-based fitness function) and EFF (energy-based fitness function), for the R-combined channel case, all severity cases and all feature vector (FV) types [see (2) - (6)].

Figure 8 Robustness study for HAF-DLac scheme. A: SENS, SPEC values when zero-mean Gaussian noise of various variances is added; B: Sensitivity of SENS, SPEC for (A); C: SENS, SPEC values for various sizes of the gliding box r of DLac analysis; D: Sensitivity of SENS, SPEC for (C).

Figure 9 Classification results for various texture feature extraction techniques.