Area–time efficient between-class variance module for adaptive segmentation process

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Area–time efficient between-class variance module for adaptive segmentation process

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Adaptive progressive thresholding (APT) has been shown to be an efficient method to segment the lumen region of endoscopic images. A pipelined architecture was previously proposed in an attempt to accelerate the conventional APT in hardware. In the paper, a novel architecture for the between-class variance computations of APT is presented to minimise the severe bottleneck of the iterative loop in the APT process. The technique employs binary logarithm conversion to eliminate the computationally intensive dividers and reduce the complexity of the multipliers of the previous architecture. The proposed method employs a re-configurable logarithmic computing unit, which can be configured to achieve a highly accurate between-class variance unit. It has been shown that the proposed approach leads to area–time efficient FPGA implementation which is capable of a computation speed-up of ∼2.75 times while occupying only one-sixth of the number of slices required by the previous approach.

Inspec keywords: biomedical optical imaging; feature extraction; minimisation; logic design; reconfigurable architectures; adaptive signal processing; field programmable gate arrays; pipeline processing; integrated circuit design; medical image processing; iterative methods; image segmentation; endoscopes; digital arithmetic

Other keywords: between-class variance computation; lumen region; adaptive segmentation; endoscopic images; reconfigurable logarithmic computing unit; binary logarithm conversion; area-time efficient FPGA implementation; multipliers; pipelined architecture; dividers; adaptive progressive thresholding; iterative loop; lumen region detection

Subjects: Interpolation and function approximation (numerical analysis); Optical and laser radiation (biomedical imaging/measurement); Image recognition; Interpolation and function approximation (numerical analysis); Logic design methods; Optimisation techniques; Optical and laser radiation (medical uses); Biology and medical computing; Digital circuit design, modelling and testing; Optimisation techniques; Image processing and restoration; Optical, image and video signal processing; Patient diagnostic methods and instrumentation; Computer vision and image processing techniques; Parallel architecture

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