Automatic pseudo-coloring approaches to improve visual perception and contrast in polarimetric images of biological tissues

Abstract

Imaging polarimetry methods have proved their suitability to enhance the image contrast between tissues and structures in organic samples, or even to reveal structures hidden in regular intensity images. These methods are nowadays used in a wide range of biological applications, as for the early diagnosis of different pathologies. To include the discriminatory potential of different polarimetric observables in a single image, a suitable strategy reported in literature consists in associating different observables to different color channels, giving rise to pseudo-colored images helping the visualization of different tissues in samples. However, previous reported polarimetric based pseudo-colored images of tissues are mostly based on simple linear combinations of polarimetric observables whose weights are set ad-hoc, and thus, far from optimal approaches. In this framework, we propose the implementation of two pseudo-colored methods. One is based on the Euclidean distances of actual values of pixels and an average value taken over a given region of interest in the considered image. The second method is based on the likelihood for each pixel to belong to a given class. Such classes being defined on the basis of a statistical model that describes the statistical distribution of values of the pixels in the considered image. The methods are experimentally validated on four different biological samples, two of animal origin and two of vegetal origin. Results provide the potential of the methods to be applied in biomedical and botanical applications.

Biological tissue-enhanced visualization through polarimetric observables

“…The Gaussian approach is provided in Ref. 16 , and as an alternative approach, the application of the Cauchy function is proposed in this work.…”

“…This is because, on the one hand, they provide physical information of samples not provided by standard non-polarimetric images, as dichroism, retardance or depolarization associated to samples, 6,7 and on the other hand, because they may visualize structures that are invisible when using non-polarimetric imaging systems. 8 Under this scenario, different authors have provided the usefulness of polarimetric observables derived from M for biomedical 7, 9-13 and botanical 5,6,[14][15][16] applications. For instance, in the study of biological structures such as the visualization of brain fiber tracks, 13 the study of heart capillary structures 8 and cancerous tissue inspection.…”

Enhancing biological tissue structures visualization through polarimetric parameters

“…In the following, we briefly describe a IPPs based pseudo-colored approach proposed by C. Rodriguez et al in Ref. 21, in order to apply it to the samples inspected in this work. The approach requires to identify two different biological classes (for instance, healthy or pathological tissues), which are associated to a particular color.…”

“…To improve this situation, in Ref. 21 we proposed an automatic pseudo-colored technique based on IPPs images. In this work we focus on the pseudo-colored method proposed in Ref.…”

“…In this work we focus on the pseudo-colored method proposed in Ref. 21: the Euclidean distances between mean values of IPPs associated to different tissue classes; and we use it for the visual enhancement of different biologic scenarios.…”