An Automatic Seeded Region Growing for 2D Biomedical Image Segmentation
This work addresses segmentation challenges in biomedical imaging, but it appears incremental as it builds on existing seeded region growing methods with automation and machine learning enhancements.
The paper tackles the problem of cellular image segmentation by proposing an automatic seeded region growing algorithm to overcome issues like explosion, under-segmentation, and over-segmentation, with experimental results showing improved and less noisy segmented images compared to existing algorithms.
In this paper, an automatic seeded region growing algorithm is proposed for cellular image segmentation. First, the regions of interest (ROIs) extracted from the preprocessed image. Second, the initial seeds are automatically selected based on ROIs extracted from the image. Third, the most reprehensive seeds are selected using a machine learning algorithm. Finally, the cellular image is segmented into regions where each region corresponds to a seed. The aim of the proposed is to automatically extract the Region of Interests (ROI) from the cellular images in terms of overcoming the explosion, under segmentation and over segmentation problems. Experimental results show that the proposed algorithm can improve the segmented image and the segmented results are less noisy as compared to some existing algorithms.