A parallel solution for high resolution histological image analysis

• Published in: Computer methods and programs in biomedicine Vol. 108; no. 1; pp. 388 - 401
• Main Authors: Bueno, G., González, R., Déniz, O., García-Rojo, M., González-García, J., Fernández-Carrobles, M.M, Vállez, N., Salido, J.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.10.2012; Elsevier
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Biological and medical sciences; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; High resolution histological image processing; Internal Medicine; Investigative techniques, diagnostic techniques (general aspects); Low and high-level parallel image processing; Medical sciences; Microscopy; MPI; Other; Pathology; Pattern recognition. Digital image processing. Computational geometry; Software; Whole slide imaging; High resolution histological image processing; Low and high-level parallel image processing; Microscopy; Whole slide imaging; MPI; Parallel algorithm; High resolution; Image resolution; Processor; Image processing; Memory architecture; Electronic document; Reuse; Data processing; Histology; Portability; Distributed system; Digital information; Image analysis; Message passing; Massive parallelism; Parallel processing; Diagnosis; Biomedical engineering
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2012.03.007

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.