Memetic improved cuckoo search algorithm for automatic B-spline border approximation of cutaneous melanoma from macroscopic medical images. (January 2020)

• Record Type: Journal Article
• Authors: Gálvez, Akemi; Iglesias, Andrés
• Is Part Of: Advanced engineering informatics. Volume 43(2020)
• Publication Date: 2020-01
• Subjects: Swarm intelligence -- Cuckoo search algorithm -- Medical image segmentation -- Cutaneous melanoma -- Border approximation -- B-spline curves; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2019.101005
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: This work follows up a previous paper at conference Cyberworlds 2018 for automatic border approximation of cutaneous melanoma and other skin lesions from macroscopic medical images. Given a set of feature points on the boundary of the skin lesion obtained by a dermatologist, we introduce a new method for automatic least-squares B-spline curve fitting of such feature points. The method is based on the original cuckoo search algorithm used in the conference paper but with three major modifications: (1) we use an enhanced version of the algorithm in which the parameters change dynamically with the generations; (2) this improved method is coupled with the Luus-Jaakola local search heuristics for better performance; (3) the original Bézier curves are now replaced by the more powerful and more general B-spline curves, providing extra flexibility and lower polynomial degree. The new method (called memetic improved cuckoo search algorithm ) has been applied to a benchmark comprised of ten medical images of skin lesions. The computer results show that it performs very well and yields a border curve enclosing the lesion and fitting the feature points with good accuracy. Furthermore, a comparison with ten alternative methods in the literature (six standard mathematical methods for B-spline fitting, two state-of-the art methods in medical imaging, the method in our conference paper and the non-memetic version of our new method) shows that it outperforms all these methods in terms of numerical accuracy for the instances in our reference benchmark.