Micro-relief characterization of benign and malignant skin lesions by polarization speckle analysis in vivo.

BACKGROUND/PURPOSE: A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. METHODS: The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). RESULTS: The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 µm and 21 ± 3 µm, respectively. Normal skin has rms roughness of 31 ± 3 µm, other lesions have roughness of 35 ± 10 µm (AK), 35 ± 7 µm (SCC), 31 ± 4 µm (SK), and 30 ± 5 µm (BCC). CONCLUSION: An independent-samples Kruskal-Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection.

Polarization memory rate as a metric to differentiate benign and malignant tissues.

Non-invasive optical methods for cancer diagnostics, such as microscopy, spectroscopy, and polarimetry, are rapidly advancing. In this respect, finding new and powerful optical metrics is an indispensable task. Here we introduce polarization memory rate (PMR) as a sensitive metric for optical cancer diagnostics. PMR characterizes the preservation of circularly polarized light relative to linearly polarized light as light propagates in a medium. We hypothesize that because of well-known indicators associated with the morphological changes of cancer cells, like an enlarged nucleus size and higher chromatin density, PMR should be greater for cancerous than for the non-cancerous tissues. A thorough literature review reveals how this difference arises from the anomalous depolarization behaviour of many biological tissues. In physical terms, though most biological tissue primarily exhibits Mie scattering, it typically exhibits Rayleigh depolarization. However, in cancerous tissue the Mie depolarization regime becomes more prominent than Rayleigh. Experimental evidence of this metric is found in a preliminary clinical study using a novel Stokes polarimetry probe. We conducted in vivo measurements of 20 benign, 28 malignant and 59 normal skin sites with a 660 nm laser diode. The median PMR values for cancer vs non-cancer are significantly higher for cancer which supports our hypothesis. The reported fundamental differences in depolarization may persist for other types of cancer and create a conceptual basis for further developments in polarimetry applications for cancer detection.

Constructing a portable optical polarimetry probe for in-vivo skin cancer detection.

SIGNIFICANCE: Management of skin cancer worldwide is often a challenge of scale, in that the number of potential cases presented outweighs the resources available to detect and treat skin cancer. AIM: This project aims to develop a polarimetry probe to create an accessible skin cancer detection tool. APPROACH: An optical probe was developed to perform bulk tissue Stokes polarimetry, a technique in which a laser of known polarization illuminates a target, and the altered polarization state of the backscattered light is measured. Typically, measuring a polarization state requires four sequential measurements with different orientations of polarization filters; however, this probe contains four spatially separated detectors to take four measurements in one shot. The probe was designed to perform at a lower cost and higher speed than conventional polarimetry methods. The probe uses photodiodes and linear and circular film polarizing filters as detectors, and a low-coherence laser diode as its illumination source. The probe design takes advantage of the statistical uniformity of the polarization speckle field formed at the detection area. RESULTS: Tests of each probe component, and the complete system put together, were performed to evaluate error and confirm the probe's performance despite its low-cost components. This probe's potential is demonstrated in a pilot clinical study on 71 skin lesions. The degree of polarization was found to be a factor by which malignant melanoma could be separated from other types of skin lesions.

Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection.

In recent years, vast developments in Computer-Aided Diagnosis (CAD) for skin diseases have generated much interest from clinicians and other eventual end-users of this technology. Introducing clinical domain knowledge to these machine learning strategies can help dispel the black box nature of these tools, strengthening clinician trust. Clinical domain knowledge also provides new information channels which can improve CAD diagnostic performance. In this paper, we propose a novel framework for malignant melanoma (MM) detection by fusing clinical images and dermoscopic images. The proposed method combines a multi-labeled deep feature extractor and clinically constrained classifier chain (CC). This allows the 7-point checklist, a clinician diagnostic algorithm, to be included in the decision level while maintaining the clinical importance of the major and minor criteria in the checklist. Our proposed framework achieved an average accuracy of 81.3% for detecting all criteria and melanoma when testing on a publicly available 7-point checklist dataset. This is the highest reported results, outperforming state-of-the-art methods in the literature by 6.4% or more. Analyses also show that the proposed system surpasses the single modality system of using either clinical images or dermoscopic images alone and the systems without adopting the approach of multi-label and clinically constrained classifier chain. Our carefully designed system demonstrates a substantial improvement over melanoma detection. By keeping the familiar major and minor criteria of the 7-point checklist and their corresponding weights, the proposed system may be more accepted by physicians as a human-interpretable CAD tool for automated melanoma detection.

Investigating the depolarization property of skin tissue by degree of polarization uniformity contrast using polarization-sensitive optical coherence tomography.

The depolarization property of skin has been found to be important for skin cancer detection. Previous techniques based on light polarization lack the capability of depth differentiation. Polarization-sensitive optical coherence tomography (PS-OCT) has the advantage of both depth-resolved 3D imaging and high sensitivity to polarization. In this study, we investigate the depolarization property of skin tissue using PS-OCT, especially with the degree of polarization uniformity (DOPU) contrast. Well designed skin phantoms with various surface roughness levels and optical properties mimicking skin are imaged by PS-OCT and the DOPU values are quantified. The result shows a correlation between DOPU and surface roughness, where a higher roughness corresponds to a lower DOPU value. An index matching experiment with a water layer confirms the impact of surface condition on light depolarization. Refraction of backscattered photons on the surface boundary is attributed to the broadening of backscattering angle and thus depolarization. To the best of our knowledge, this is the first time the impact of surface roughness on DOPU is reported and its mechanism explained. Furthermore, through preliminary in vivo skin imaging, the capability of DOPU in detecting depolarization in skin is demonstrated. By utilizing the 3D imaging from PS-OCT, DOPU can offer a high-resolution depth differentiation and quantification of depolarization in skin tissue.

Degree of optical polarization as a tool for detecting melanoma: proof of principle.

Determining the optical polarization properties of a skin lesion is a proposed method to differentiate melanoma from other skin lesions. We developed an in vivo Stokes polarimetry probe that fires a laser of known polarization at the skin and measures the Stokes parameters of the backscattered light in one shot. From these measured Stokes parameters, we can calculate the degree of polarization (DOP). Through testing on rough skin phantoms, a correlation between backscattered DOP and skin roughness was identified for both linear and circular input polarization, the latter of which was found to be more useful. In a pilot clinical trial of 69 skin lesions in vivo, it was found that the mean DOP for melanoma (linear input on melanoma: 0.46 ± 0.09) was greater than that of other lesions (linear input on all other lesions: 0.28 ± 0.01). This separation is greater for circular polarized input light, and it is likely that circular polarized light's greater sensitivity to surface roughness contributes to this result. In addition, all skin lesions demonstrated a stronger depolarizing effect on circular polarized light than linear polarized light. We have identified DOP as a potentially useful measurement to identify melanoma among other types of skin lesions.