Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Figures & data

Figure 1. Distribution of melanoma thickness comparing to Breslow’s depth stage and 5 years survival rate.

Figure 2. Ultrasonic B-scan images (raw and processed) of skin melanoma, axes are in millimetres: a – ultrasonic raw B-scan image, b – binary B-scan image, c – detected informative region.

Figure 3. Digital Dermatoscopy Images (raw and processed) of skin melanoma, axes are in millimetres: a – raw optical image, b – binary optical image, c – detected informative region.

Figure 4. Ultrasonic B-scan images (raw and processed) of benign nevus, axes are in millimetres: a – ultrasonic raw B-scan image, b – binary B-scan image, c – detected informative region.

Figure 5. Digital dermatoscopy images (raw and processed) of benign nevus, axes are in millimetres: a – raw optical image, b – binary optical image, c – detected informative region.

Table 1. Significant parameters evaluated from detected skin lesion region of ultrasonic and dermatoscopic images.

Discriminant analysis aodel (Fisher’s test)
Significant parameters of ultrasonic images: (n = 5/19)	Significant parameters of digital dermatoscopy images: (n = 17/27)
Maximum length, area, perimeter, the ratio of average kurtosis (1000 directions) and maximum excess, the ratio of perimeter and area.	Average diameter, minimum diameter, area, average excess (1000 directions), maximum kurtosis, the ratio of maximum diameter and average diameter, the ratio of minimum diameter and average diameter, the ratio of maximum diameter and minimum diameter, 9 of 10 deciles.
Logistic regression model (Chi-squared test)
Significant parameters of ultrasonic images: (n = 2/19)	Significant parameters of digital dermatoscopy images: (n = 2/27)
The ratio of average kurtosis (1000 directions) and maximum excess, the ratio of perimeter and area.	The ratio of minimum diameter and average diameter, 2^nd decile.

Figure 6. ROC curve of the classification of melanoma and benign melanocytic nevi analysing ultrasonic B-scan images only by using discriminant analysis.

Figure 7. ROC curve of indirectly combined digital dermatoscopy and ultrasonic B-scan images classification by using discriminant analysis.

Figure 8. ROC curve of the classification of melanoma and benign melanocytic nevi analysing ultrasonic B-scan images only by using logistic regression.

Figure 9. ROC curve of indirectly combined digital dermatoscopy and ultrasonic medical images classification by using logistic regression.

Figure 10. The results of classification of melanoma and benign melanocytic nevi analysing ultrasonic B-scan images and in combination with analysis of digital dermatoscopy images.