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Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non‐uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region‐based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel‐Based Fuzzy C‐Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U‐Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state‐of‐the‐art methods in brain tumor segmentation. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates a compact Signed-Digit (SD) encoding algorithm which is an efficient class of the large integer encoding algorithms. The compact SD algorithm results in a series of digits which show the number of consecutive zero bits. Using the proof-of-concept code, we proved that the optimal range for the limit on the number of consecutive zero bits in each digit and average Hamming weight ofn-bit integers in the compact SD representation aretoandn/3, respectively. In the modular multiplication algorithm, the compact SD is applied to the multiplier. The compact SD modular multiplication processes consecutive zero bits and followed nonzero digits in one clock cycle. Implementation results on Xilinx Virtex 5 FPGA show that the compact SD modular multiplication outperforms previous modified modular multiplication algorithms in terms of throughput and area × time complexity. [ABSTRACT FROM AUTHOR]