An efficient neutrosophic-based approach to multiclass leukocyte segmentation
TL;DR
The objective of this work is to develop an automatic multi-class segmentation technique that achieves competitive performance while minimizing computational costs.
Abstract
Advances in image acquisition and computational processing technologies have significantly transformed histological and hematological analysis. However, to maximize the clinical impact of these technologies, it is essential to develop automatic or semi-automatic segmentation techniques. The objective of this work is to develop an automatic multi-class segmentation technique that achieves competitive performance while minimizing computational costs. The proposed technique consists of an unsupervised algorithm that models a neutrosophic image by combining only two features: the luminance channel L* from the CIELUV color space and the HH sub-band of the discrete wavelet transform obtained from the grayscale image. The neutrosophic components are enhanced to promote robust clustering and segmentation, especially in regions of uncertainty. Finally, morphological operations are applied as a post-processing stage. Experimental results demonstrate strong performance in multiclass segmentation, with average standard index values above 90% and an overall correct classification rate of 97.07%. The developed algorithm has publicly available open-source code, operates directly on color images without requiring any training and achieves competitive results with a low-cost implementation.