Unsupervised learning segmentation for dynamic speckle activity images

TL;DR

This paper introduces unsupervised decision models using computational intelligence to segment dynamic speckle images, enhancing biological tissue analysis by combining multiple descriptors and self-organizing maps.

Contribution

It presents a novel unsupervised segmentation approach for dynamic speckle images using descriptors and self-organizing maps, improving accuracy over single-descriptor methods.

Findings

Significant improvement over single-descriptor methods

Effective identification of biological tissue regions

Encouraging results in tissue assessment applications

Abstract

This paper proposes the design of decision models based on Computational Intelligence techniques applied to image sequences of dynamic laser speckle. These models aim to identify image regions of biological specimens illuminated by a coherent beam coming from a laser. The field image is pseudo colored using a Self Organizing Map projection. This process is carried out using a set of descriptors applied to the intensity variations along time in every pixel of an image sequence. The models use descriptors selected to improve effectiveness, depending on the specific application. We present two examples of the application of the proposed techniques to assess biological tissues. The results obtained are encouraging and significantly improve those obtained using a single descriptor.