Artificial Life for Segmentation of Fusion Ultrasound Images of Breast Abnormalities
Nalan Karunanayake, Stanislav S. Makhanov and Wanrudee Lohitvisate
(Published in Pattern Recognition, Elsevier for review and possible publication)
Additional References
A new segmentation method that combines AI, fusion, and artificial life has been proposed for the segmentation of breast cancer in Ultrasound images. The algorithm outperforms 16 of the most popular, recent segmentation methods. Most of the codes have been downloaded from the developers' websites. In turn, the 16 algorithms outperform 69 preceding routines (5 algorithms have been outperformed several times). Therefore, the conjecture is that the proposed artificial life outperforms 69+16=85 algorithms.
This claim requires appropriate references. However, the journal does not allow more than 40 citations per paper. Therefore, the authors decided to post the 16 and the additional 69 references online.
The reference methods have been divided into 1) Active contours, 2) Level set methods, 3) Machine learning methods, 4) Super-pixels, 5) Edge linking, 6) Selected hybrid methods.
Below is the list of the corresponding 16 references. The dropdown menus show the outperformed methods.