Skin biomedical optical imaging system using dual-wavelength polarimetric control with liquid crystals

Safrani, Avner; Aharon, Ofir; Mor, Shahar; Arnon, Ofer; Rosenberg, Lior; Abdulhalim, Ibrahim

doi:10.1117/1.3394338

Cited by 20 publications

(14 citation statements)

References 17 publications

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“…The same is true when using polarimetric imaging alone, while the combination certainly enhances the information on the pictured lesion. 8,9 Hence, there is still a need for novel approaches for skin cancer imaging.…”

Section: Tomentioning

confidence: 99%

Modeling of reflectometric and ellipsometric spectra from the skin in the terahertz and submillimeter waves region

Ney

Abdulhalim

2011

J. Biomed. Opt.

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The human skin is modeled as a stack of homogeneous layers in the terahertz and submillimeter waves regions with some anisotropy due to the helical sweat glands and other elongated entities. A dielectric model for the skin is presented, valid for a wider frequency range (up to the terahertz region) taking into account the dispersive nature of the effective conductivity. Polarized reflectivity and generalized ellipsometric parameters are calculated versus angle and wavelength. Recent studies have claimed that the helical sweat ducts act as an array of low-Q helical antennae and are dominant in shaping the spectral response in the sub-terahertz region. We found that water absorption, dispersion and multiple interference effects play the major role in shaping the spectrum without the need for the assumption of the sweat ducts acting as low-Q helical antennae. High sensitivities to the water content are found particularly in the ellipsometric parameters at large incidence angles. Hence a new methodology is proposed to detect skin cancer using variable angle ellipsometry or polarized reflectometry. The parameter found with the highest sensitivity to water content is cos Δ(pp) with Δ(pp) being the phase of the on-diagonal reflection matrix ratio between p-to-p polarization.

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Section: Tomentioning

confidence: 99%

Modeling of reflectometric and ellipsometric spectra from the skin in the terahertz and submillimeter waves region

Ney

Abdulhalim

2011

J. Biomed. Opt.

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“…In the last decade, several non-invasive methods were introduced for the diagnosis of melanoma including optical imaging system (OIS) [ 13 ], optical coherence tomography (OCT) [ 14 ], light scattering (LS) [ 15 ], spectropolarimetric imaging system (SIM) [ 16 , 17 ], fourier polarimetry (FP) [ 18 ], polarimetric imaging [ 19 ], reectance confocal microscopy (RCM) [ 20 , 21 ], photo-acoustic microscopy [ 22 ], optical transfer diagnosis (OTD) [ 23 ], etc. All these above mentioned methods have enough potential to diagnose the skin lesions and also accurate enough to distinguish the melanoma and benign.…”

Section: Introductionmentioning

confidence: 99%

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

et al. 2018

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BackgroundMelanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency.MethodsIn this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier.ResultsThe proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively.ConclusionThe base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.

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“…Recently, simple methods for developing a clear image of the nevus using spectro and polarimetric techniques were presented [34][35][36]. However, the method presented in this paper suggests a new and unique technique in which the physical depth of the nevus tissue is directly analyzed by the vibrational responsivity of the tissue.…”

Section: Introductionmentioning

confidence: 99%

Optical configuration of pigmented lesion detection by frequency analysis of skin speckle patterns

Bishitz¹,

Ozana²,

Schwarz³

et al. 2016

Biomed. Opt. Express

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Abstract:In this paper we present a novel approach of realizing a safe, simple, and inexpensive sensor applicable to pigmented lesions detection. The approach is based on temporal tracking of back-reflected secondary speckle patterns generated while illuminating the affected area with a laser and applying periodic pressure to the surface via a controlled vibration source. When applied to pigmented lesions, the technique is superior to visual examination in avoiding many false positives and resultant unnecessary biopsies. Applying a series of different vibration frequencies at the examined tissue and analyzing the 2-D time varying speckle patterns in response to the applied periodic pressure creates a unique signature for each and different pigmented lesion. Analyzing these signatures is the first step toward detection of malignant melanoma. In this paper we present preliminary experiments that show the validity of the developed sensor for the classification of pigmented lesions.

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Skin biomedical optical imaging system using dual-wavelength polarimetric control with liquid crystals

Cited by 20 publications

References 17 publications

Modeling of reflectometric and ellipsometric spectra from the skin in the terahertz and submillimeter waves region

Modeling of reflectometric and ellipsometric spectra from the skin in the terahertz and submillimeter waves region

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

Optical configuration of pigmented lesion detection by frequency analysis of skin speckle patterns

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