Early detection of cancer remains the best way to ensure patient survival and quality of life. Squamous cell carcinoma is usually preceded by dysplasia presenting as white, red, or mixed red and white epithelial lesions on the oral mucosa (leukoplakia, erythroplakia). Dysplastic lesions in the form of erythroplakia can carry a risk for malignant conversion of 90%. A noninvasive diagnostic modality would enable monitoring of these lesions at regular intervals and detection of treatment needs at a very early, relatively harmless stage. The specific aim of this work was to test a multimodality approach [three-dimensional optical coherence tomography (OCT) and polarimetry] to noninvasive diagnosis of oral premalignancy and malignancy using the hamster cheek pouch model (nine hamsters). The results were compared to tissue histopathology. During carcinogenesis, epithelial down grow, eventual loss of basement membrane integrity, and subepithelial invasion were clearly visible with OCT. Polarimetry techniques identified a four to five times increased retardance in sites with squamous cell carcinoma, and two to three times greater retardance in dysplastic sites than in normal tissues. These techniques were particularly useful for mapping areas of field cancerization with multiple lesions, as well as lesion margins.
The Mueller matrix describes all the polarizing properties of a sample, and therefore the optical differences between non-cancerous and pre-cancerous tissue should be present within the matrix elements. We present in this paper that a high speedpolarimetry system generates 16 full Mueller matrices to characterize tissues. Feature extraction is done on the Mueller matrix elements resulting in the depolarizance and retardance images by polar decomposition to detect and classify of early oral cancers and pre-cancerous changes in epithelium, such as dysplasia. These images are compared with
orthogonal polarization image and analyzed in an attempt to determine the important factors for the identification of cancerous lesions from their benign counterparts. Our results indicate that polarimetry has potential as a method for the in vivo early detection and diagnosis of oral premalignancy and malignancy.
The Mueller matrix describes all the polarizing properties of a sample, and therefore the optical differences between cancerous and non-cancerous tissue should be present within the matrix elements. We present in this paper the Mueller matrices of three types of tissue; normal, benign mole, and malignant melanoma on a Sinclair swine model. Feature extraction is done on the Mueller matrix elements resulting in the retardance images, diattenuation images, and depolarization images. These images are analyzed in an attempt to determine the important factors for the identification of cancerous lesions from their benign counterparts. In addition, the extracted features are analyzed using statistical processing to develop an accurate classification scheme and to identify the importance of each parameter in the determination of cancerous versus non-cancerous tissue.
KEYWORDS: Polarization, Polarizers, Polarimetry, Signal detection, Sensors, Signal to noise ratio, Semiconductor lasers, Laser applications, Telecommunications, Linear polarizers
The ability to detect skin cancer accurately, quickly, and non-invasively has been the object of researchers for many years. This paper describes a novel Automated Mueller Matrix Polarimetric Imaging System that has the potential for non- invasive determination of cancerous lesions from their benign counterparts. Our system collects the 16 images used to calculate the 16 Mueller matrix elements in less than 70 seconds. To validate the system, we used known samples that show a maximum error of 1.41 percent in the Mueller matrix. Tissue-phantoms with varying concentrations of scatterers were used to determine the effects of changes in the sample scattering coefficient on the Mueller matrix. The system was also used to image a benign lesion on a human subject to show the ability to collect of polarization information from the skin.
In this paper, we describe the use of the full sixteen element Mueller matrix to differentiate between surface lesions on the skin, as an eventual noninvasive diagnostic technique for detection of certain types of skin cancers. A semi-automated system using a polarized light beam and a computer controlled CCD camera was developed to collect 16 polarization images of a sample and to calculate the complete Mueller matrix in near real time. The system was initially calibrated and the accuracy and precision were evaluated with a mirror and polarizer. This study also includes in vivo results from normal skin, a benign nevus, and a known cancerous lesion acquired from a single Sinclair swine. Differences were observed between the Mueller matrices of all three in vivo cases. These preliminary results demonstrate the potential for using an automated polarization imaging apparatus for eventual clinical cancer diagnostics.
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