ABSTRACT
A microscope is an essential tool in biosciences and production quality laboratories for unveiling the secrets of microworlds. This paper describes the development of MicroHikari3D, an affordable DIY optical microscopy platform with automated sample positioning, autofocus and several illumination modalities to provide a high-quality flexible microscopy tool for labs with a short budget. This proposed optical microscope design aims to achieve high customization capabilities to allow whole 2D slide imaging and observation of 3D live specimens. The MicroHikari3D motion control system is based on the entry level 3D printer kit Tronxy X1 controlled from a server running in a Raspberry Pi 4. The server provides services to a client mobile app for video/image acquisition, processing, and a high level classification task by applying deep learning models.
ABSTRACT
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.
Subject(s)
Pathology , AlgorithmsABSTRACT
PURPOSE: Breast parenchymal density is an important risk factor for breast cancer. It is known that mammogram interpretation is more difficult where dense tissue is involved. Therefore, automated breast density classification may aid in breast lesion detection and analysis. METHODS: Several image pattern classification techniques for screen-film (SFM) mammography datasets were tested and classified according to BIRADS categories using known cases. A hierarchical classification procedure based on k-NN, SVM and LBN combined with principal component analysis on texture features uses the breast density features. The classification techniques have been incorporated into a CADe system to drive the detection algorithms. RESULTS: The results obtained on 322 mammograms demonstrate that up to 84% of samples were correctly classified. The results of the lesion detection algorithms were obtained from modules integrated within the CADe system developed by the authors. CONCLUSIONS: The ability to detect suspicious lesions on dense and heterogeneous tissue has been tested. The tools enhance the detectability of lesions and they are able to distinguish their local attenuation without local tissue density constraints.
