Recently, backscattering polarization images have been used to explore the microstructures of biological tissues. A proposed study is presented for classifying different samples including a set of 7.4 pH Phosphate-buffered saline (PBS), plasma fibronectin (FN), fibronectin fibril assembly at 0.25 ml/h (FFN 25), and fibronectin fibril assembly at 0.48 ml/h (FFN 48) based on the Mueller matrix backscattering images. The research showed that the diagonal components values m22, m33, and m44 of PBS are considerably higher than those of the fibrillated fibronectin samples (i.e. FN, FFN 25 and FFN 48). In other words, PBS samples are more isotropic than the others whereas FFN 25 and FFN 48 are anisotropic. Furthermore, the frequency distribution histograms (FDHs) of all Mueller matrix elements are evaluated for yielding critical explicit structural information in the form of distinct values that may be used to distinguish four samples. The results also indicated that FFN 48 has the most noticeable depolarization properties. As a consequence, this approach has shown to be an effective method of assessing microstructural research.
Significance: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine.Aim: An approach is proposed for the detection of hepatitis B (HB) virus using a combined Mueller matrix imaging technique and deep learning method.Approach: In the proposed approach, Mueller matrix imaging polarimetry is applied to obtain 4 × 4 Mueller matrix images of 138 HBsAg-containing (positive) serum samples and 136 HBsAg-free (negative) serum samples. The kernel estimation density results show that, of the 16 Mueller matrix elements, elements M22 and M33 provide the best discriminatory power between the positive and negative samples.Results: As a result, M22 and M33 are taken as the inputs to five different deep learning models: Xception, VGG16, VGG19, ResNet 50, and ResNet150. It is shown that the optimal classification accuracy (94.5%) is obtained using the VGG19 model with element M22 as the input.Conclusions: Overall, the results confirm that the proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HB virus detection.
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 m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 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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