Design and evaluation of a parallel and optimized light–tissue interaction-based method for fast skin lesion assessment

• DOI: 10.1007/s11554-015-0494-6
• Publication Date: 2015-03-09T06:19:31Z
• Authors: Chao Li; Vincent Brost; Yannick Benezeth; Franck Marzani; Fan Yang
• Keywords: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]; [INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR]; POSIX threads; Function parallelization; Color; 02 engineering and technology; Kubelka-Munk model; Genetic algorithm; Diffuse-Reflectance; Implementation; Melanin; 0202 electrical engineering, electronic engineering, information engineering; Multispectral image processing; Hemoglobin; Model; High-performance computer

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.