An Enhancement Neighborhood connected Segmentation for 2D-Cellular Image

TL;DR

This paper introduces an automatic seeded region growing algorithm for cellular image segmentation that enhances seed selection using machine learning, resulting in more accurate and less noisy segmentation outcomes.

Contribution

It proposes a novel method combining ROI extraction and machine learning for automatic seed selection in cellular image segmentation.

Findings

Segmentation results are less noisy compared to existing algorithms.

The method effectively overcomes over-segmentation and under-segmentation issues.

Experimental results demonstrate improved segmentation accuracy.

Abstract

A good segmentation result depends on a set of "correct" choice for the seeds. When the input images are noisy, the seeds may fall on atypical pixels that are not representative of the region statistics. This can lead to erroneous segmentation results. 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 in 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.