VLSI Adopted Larvae Image Segmentation Using Ensemble Clustering

Abstract

Larvae image segmentation is a crucial task in various biological and ecological studies, as it facilitates the monitoring and analysis of larval populations and their habitats. The global image analysis market is projected to reach approximately $28.8 billion by 2026, reflecting the increasing importance of image processing technologies across various fields. However, traditional threshold- based segmentation methods often face challenges such as sensitivity to lighting conditions and noise, leading to inaccurate segmentation results and difficulties in distinguishing between larvae and background noise. This work proposes a novel approach to larvae image segmentation using ensemble clustering techniques, which combine multiple clustering algorithms to improve segmentation accuracy and robustness. The proposed method effectively integrates various clustering strategies to adaptively identify and segment larvae images under varying conditions, thereby overcoming the limitations associated with threshold-based methods. By leveraging the strengths of different algorithms, the ensemble clustering approach enhances the overall performance of the segmentation process, providing more accurate and reliable results. Advantages of this method include improved segmentation quality, robustness to noise and illumination variations, enhanced adaptability to different image types, and the potential for real-time processing in a VLSI implementation.