Paper Info
Title
Design and evaluation of a parallel and optimized light-tissue interaction-based method for fast skin lesion assessment.
Abstract
In recent years, image processing technics have attracted much attention as powerful tools in the assessment of skin lesions from multispectral images. The Kubelka–Munk Genetic Algorithm (KMGA) is a novel method which has been developed for this diagnostic purpose. It combines the Kubelka–Munk light–tissue interaction model with the Genetic Algorithm optimization process, and allows quantitative measure of cutaneous tissue by computing skin parameter maps such as melanin concentration, volume blood fraction, oxygen saturation or epidermis/dermis thickness. However, its efficiency is seriously reduced by the mass floating-point operations for each pixel of the multispectral image, and this prevents the algorithm from reaching industrial standards related to cost, power and speed for clinical applications. In this paper, our work focuses on the improvement of this theoretical achievement. Therefore, we repropose a new C-based Parallel and Optimized KMGA (PO-KMGA) technique designed and optimized using multiple ways: KM model optimized re-writing, operation massively parallelized using POSIX threads, memory use optimization and routine pipelining with Intel C++ Compiler, etc. Intensive experiments demonstrate that our introduced PO-KMGA framework spends less than 10 min to finish a job that the conventional KMGA spends around two days to do in the same hardware environment with a similar algorithm performance.
Year
DOI
Venue
2018
10.1007/s11554-015-0494-6
Journal of Real-time Image Processing
Keywords
DocType
Volume
Multispectral image processing, Kubelka–Munk model, Genetic algorithm, Function parallelization, High-performance computer, POSIX threads
Journal
15
Issue
ISSN
Citations 
2
1861-8219
8
PageRank 
References 
Authors
0.46
20
5
Name
Order
Citations
PageRank
Chao Li1183.98
Vincent Brost2456.75
Yannick Benezeth339926.11
Franck Marzani47313.41
Fan Yang513313.50