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Abstract
Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy.
Highlights
Microwave imaging technology is a potential imaging method that aims to provide inexpensive, portable and wearable imaging devices
We examine the potential gains in performance and scalability of delay multiply and sum (DMAS) using big data framework
To validate the results of the proposed Spark-based parallel DMAS algorithm, the results are compared in terms of processing speed with both sequential and Python multiprocessing library
Summary
Microwave imaging technology is a potential imaging method that aims to provide inexpensive, portable and wearable imaging devices. It was originally proposed for breast cancer detection [1] and was later extended for brain stroke or brain injuries [2]. The basic principle is the difference in the dielectric values (relative permittivity and conductivity) in healthy and malignant tissues [3,4]. An imaging algorithm uses these data to reconstruct images of the targeted object/tissue. Microwave head imaging is a biomedical imaging application, and a number of microwave head imaging systems have been proposed targeting brain tumours [5], stroke [6]
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