Characterization of healthy and cancerous human skin tissue utilizing Stokes–Mueller​ polarimetry technique

Le, Dan Linh; Nguyễn, Duc Tung; Le, Thanh-Hai; Phan, Quoc-Hung; Pham, Thi-Thu-Hien

doi:10.1016/j.optcom.2020.126460

Cited by 26 publications

(25 citation statements)

References 33 publications

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“…Among others, polarimetry allows the imaging of nerve fiber bundles in the human brain 9 and the inspection of Alzheimer disease 10 . Polarization-related techniques also demonstrate high accuracy in the early detection of some cancers such as skin cancer 11 , colon cancer 12 , breast cancer 13 , 14 and brain cancer 15 . Moreover, recent studies reveal the accurate performance of some machine learning algorithms built-in the polarimetric analysis of some biological tissues 16 – 20 .…”

Section: Introductionmentioning

confidence: 99%

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

Rodríguez

Eeckhout²,

García-Caurel³

et al. 2022

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

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

Rodríguez

Eeckhout²,

García-Caurel³

et al. 2022

Sci Rep

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“…In the case of applications in biophotonics, polarimetric methods have proved to be very useful tools to enhance the image contrast of some organic structures, and/or providing information of certain structures invisible by using regular (non-polarimetric) images. This situation is useful, for instance, for the early detection of some diseases, such as breast cancer 7 , 8 , colon cancer 9 , skin cancer 10 , or brain cancer 11 , among others.…”

Section: Introductionmentioning

confidence: 99%

“…The above-stated use of polarimetric methods for the study of animal 12 or even human 8 – 10 tissues is a well-established field of work 13 and nowadays continues in constant development. However, the application of polarimetric methods for the study of plant diseases is less common and, in the last decade, there is a growing interest of exploring more complex (and rich in terms of information) polarimetric solutions for applications in plant science.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric observables for the enhanced visualization of plant diseases

Rodríguez

García-Caurel

Garnatje

et al. 2022

Sci Rep

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This paper highlights the potential of using polarimetric methods for the inspection of plant diseased tissues. We show how depolarizing observables are a suitable tool for the accurate discrimination between healthy and diseased tissues due to the pathogen infection of plant samples. The analysis is conducted on a set of different plant specimens showing various disease symptoms and infection stages. By means of a complete image Mueller polarimeter, we measure the experimental Mueller matrices of the samples, from which we calculate a set of metrics analyzing the depolarization content of the inspected leaves. From calculated metrics, we demonstrate, in a qualitative and quantitative way, how depolarizing information of vegetal tissues leads to the enhancement of image contrast between healthy and diseased tissues, as well as to the revelation of wounded regions which cannot be detected by means of regular visual inspections. Moreover, we also propose a pseudo-colored image method, based on the depolarizing metrics, capable to further enhance the visual image contrast between healthy and diseased regions in plants. The ability of proposed methods to characterize plant diseases (even at early stages of infection) may be of interest for preventing yield losses due to different plant pathogens.

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“…Pham et al. 18 – 22 employed a Stokes–Mueller method to examine the polarization properties of skin cancer, liver cancer tissues, neuroblastoma, collagen-rich tendons, and cartilage. It was shown that the proposed method yielded nine effective parameters for distinguishing between normal skin tissue and various skin cancer tissues, including BCC, squamous cell carcinoma (SCC), and malignant melanoma.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the fruitful achievement of Mueller matrix in Refs. 13 – 22 and machine learning techniques for skin cancer detection in Refs. 24 – 30 , furthermore, the RF classifier is adopted for this study because of its advantage for overcoming the overfitting and suitable for classifying untrained data.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

Luu

Phan

et al. 2021

J. Biomed. Opt.

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. Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements , , , and of the Mueller matrix) dominate the linear polarization properties (i.e., elements , , , and of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

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Characterization of healthy and cancerous human skin tissue utilizing Stokes–Mueller polarimetry technique

Cited by 26 publications

References 33 publications

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

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

Polarimetric observables for the enhanced visualization of plant diseases

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

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Characterization of healthy and cancerous human skin tissue utilizing Stokes–Mueller​ polarimetry technique

Cited by 26 publications

References 33 publications

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

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

Polarimetric observables for the enhanced visualization of plant diseases

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

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Characterization of healthy and cancerous human skin tissue utilizing Stokes–Mueller polarimetry technique