Exploration of Optimal Many-Core Models for Efficient Image Segmentation

Image segmentation plays a crucial role in numerous biomedical imaging applications, assisting clinicians or health care professionals with diagnosis of various diseases using scientific data. However, its high computational complexities require substantial amount of time and have limited their applicability. Research has thus focused on parallel processing models that support biomedical image segmentation. In this paper, we present analytical results of the design space exploration of many-core processors for efficient fuzzy c-means (FCM) clustering, which is widely used in many medical image segmentations. We quantitatively evaluate the impact of varying a number of processing elements (PEs) and an amount of local memory for a fixed image size on system performance and efficiency using architectural and workload simulations. Experimental results indicate that
${\rm PEs}={4},096$
provides the most efficient operation for the FCM algorithm with four clusters, while
${\rm PEs}={1},024$
and
${\rm PEs}={4},096$
yield the highest area efficiency and energy efficiency, respectively, for three clusters.