2.1.

Differential Spectropolarimetric Imaging System Using Liquid Crystal Devices

The DOSI system comprises a halogen light source with collimation and imaging optics, using liquid crystal tunable filter (LCTF) with linearly polarized output and a wavelength independent liquid crystal (LC) polarization rotator (WILCPR). The LCTF¹⁶ and the WILCPR^{17, 18} were designed and fabricated in our clean room, especially for the DOSI system. The LCTF and the WILCPR are both controlled by sinusoidal voltages at 1 kHz to generate color and continuous rotation of the linearly polarized light illuminating the investigated lesion. A CCD camera with fixed polarizer captures a sequence of pictures (as uncompressed movie). After image capture, a MATLAB ^® program calculates the contrast between two adjacent pictures and averages a chosen section of picture series.

The DOSI system diagram is shown in Fig. 1, (A) is halogen light source with lens to collimate light to LCTF (B), light emerges from LCTF linearly polarized, entering WILCPR (C) and impinges on glass window (D), touching the examined skin lesion. Backreflected light emitted toward a fixed polarizer (E) objective with magnification 0.56, calibrated for a field of view (FOV) of 8 mm (F), Camera: Unibrain Fire-i™ Digital Board, CCD Sony Chip ICX098BL, unit cell size: 5.6 μm(H) × 5.6 μm(V), image size: diagonal 4.5 mm, Chip size: 4.60 mm (H) × 3.97 mm (V), capture resolution: 640×480, frames per second (fps) 30 (G), computer with CPU 2 GHz dual core, 4G RAM (H) to control DOSI scan with function generator NI6715-PCMCIA card (analog output voltage of 8 channels).¹⁹ Polarized light is obliquely incident while the read light is symmetric around the normal to the surface being inspected,²⁰ and the configuration in this work uses LC devices with new algorithms that will be explained later on.

Fig. 1

DOSI imager using LCDs. (A) is halogen light source with lens to collimate light to LCTF (B), light emerges from LCTF linearly polarized, entering WILCPR (C) and impinges on the glass window (D), touching the examined skin lesion. Backreflected light emitted toward fixed polarizer (E) objective (F) and CCD camera (G). A computer (H) controls DOSI scan with function generator NI6715-PCMCIA card.

In Fig. 1 the LCTF (B) and WILCPR (C) are controlled by the FG. The scanning performed by first choosing the wavelength with the LCTF and then rotating the linearly polarized light using the WILCPR. The incident light (on the lesion), linearly polarized and rotated three cycles. The angle velocity was 1 rotation for 2 s, 60 frames for 1 rotation. The scan lingered for 6 s, thus generating 180 pictures. For each angle of rotation α, the backscattered light is captured as frames on the 8-bit CCD camera with resolution of 640×480, IEEE type. In front of the camera we located a fixed wire-grid linear polarizer. The advantage of using a wire-grid polarizer over the polaroid polarizer is that it covers the visible (VIS) and the near-infrared (NIR) spectral ranges. The computer (H) in our case is a laptop with National instruments function generator, type: NI6715-PCMCIA card (analog output voltage of 8 channels) and a FireWire connection to a CCD camera. Figure 2 shows the whole pack — the DOSI system and the laptop, the whole system can be carried in one laptop bag. To control the image capture process we developed a LABVIEW program as shown in Fig. 3. This interface has two purposes: the first is for the calibration of LCTF and WILCPR, when necessary, and the second is for capturing a sequence of images in vivo.

Fig. 2

DOSI system including one laptop that can be portable in a school bag. Under operation the upper sealing is closed to eliminate background light.

Fig. 3

LABVIEW program interface for calibration of liquid crystal devices (LCTF, WILCPR) and capturing a sequence of images while rotating the polarization of light impinging on the tissue.

LCTF and WILCPR retarders were built using glass substrates of area 15 mm × 17 mm and thickness of 2 mm. The two pieces of flat glass coated with transparent electrically conducting electrodes made of indium tin oxide were cleaned and spin coated with Rolic photoalignment polymer linearly photo-polymerisation. A spinner was set to 4000 rpm for 60 s to form ∼50 nm of uniform polymer thickness. Then, polymer was baked in a vacuum oven at a temperature of 90°C for 30 min and then at a temperature of 250°C for 1 h. Next, the two polymer coated substrates were irradiated by a collimated linearly polarized UV light at normal incidence for photoalignment. After UV irradiation for photoalignment, glass spacers mixed with UV-glue Norland 68 were applied near the edges of the glass substrates. Assembling the two glass plates was then achieved with a specially designed mechanical jig to obtain a uniform gap by observing the interference colors reflected from the empty cell with a green light. The cell then was filled with the nematic LC 4-pentyl-4^′-cyanobiphenyl (E44) purchased from Merck, in vacuum, at a temperature above the clearing temperature (101°C in our case), and then cooled slowly to room temperature. The cell was sealed with UV-glue Norland 68 and metal wires connected to electrodes using sliver paste and epoxy glue. Characterization of the LC thickness was done by spectral measurement of the retardation between crossed polarizers and using fitting of measured output spectrum with theoretical spectrum. Our polarizers are limited to the wavelength regime between 450 and 850 nm. Retarders were aligned in a setup which enabled us to rotate them freely and modify their voltage. Output transmission spectrum was read by a spectrometer manufactured by StellarNet Inc-EPP2000. Each glass surface attenuates 4% (transmittance 0.96² for each cell) and the polarizer attenuates 30% (transmits 0.7), thus the total transmission was 17%. However for bioimaging applications where CCD cameras are very sensitive it is not a problem to compensate with a stronger light source. It is also possible to use higher throughput polarizers such as those made of metal wire grids with transmission above 90%, bringing the filter output to nearly 50%. All retarders are controlled by our LABVIEW program code using National Instrument PCICIA Card NI6715 with eight programmable output voltage channels that we synchronized with the LABVIEW video code of the IEEE camera.

2.2.

Functionality of the Liquid Crystal Devices

LCs are characterized by a large optical anisotropy, large electrooptic effect, fast tunability, and ease of fabrication.^{21, 22} These facts make them excellent candidates for tunable filtering and polarization control for biomedical optical applications.²³ LCTF is a birefringence filter that utilizes retardation between the ordinary and extraordinary light rays passing through the LC, creating constructive and destructive interferences, allowing a transmission of narrow spectral bands. By combining several electronically tunable LC retarders in series, a high spectral resolution can be achieved. LCTFs have the advantages of miniaturization of the designed system, as well as tuning the center wavelength with no physical movements with capability of rapid wavelength selection in microsecond to tens of milliseconds tuning speeds.

Our LCTF is comprised of three retarders, two in Lyot–Öhman composition,^{24, 25, 26} and an additional retarder between crossed polarizers, see Fig. 4b. We named this filter as Lyot elimination order, or LEO filter, due to the additional retarder eliminating one of orders of Lyot filter, and by that extending its free spectral range. This latter property is needed in our imaging process to avoid disturbance of unwanted spectral order (named also mode). Transmission (T _out) of the LEO filter was calculated by Jones matrices,²⁷ shown in Eq. 1. Equation 2 defines the retardation δ _i for the LCTF retarders and the filter transfer function is given in Eq. 1:

Fig. 4

Lyot eliminated order filter (LEO filter) comprised from two parts (a) liquid crystal Lyot filter composed of two LCTRs, d is the first retarder thickness, (b) an additional LCTR between crossed polarizers to improve the free spectral range.

Fig. 5

Normalized transmission of the LEO filter (dashed lines) and the theoretical calculation (solid lines), for the 2nd order peak of the Lyot filter at 838, 740, 634, and 560 nm when the sets of voltages for the first and second Lyot's retarders, as well as for the additional eliminator retarder, were: {V ₁, V ₂, V ₃} = {2.23 V, 2 V, 2.16 V}, {2.492 V, 2.28 V, 2.455 V}, {2.785 V, 2.61 V, 2.79 V}, {3.0625 V, 2.915 V, 3.105 V}, correspondingly.

Although the LCTF is capable of tuning over a wide spectral range, we concentrate in this study mainly on results obtained with two wavelengths: 520 and 700 nm based on two properties that can be inferred from the work of Zhang ³⁰ (Figs. 5, 6, 7): i. the large difference between the remittance at 520 and 700 nm and ii. the spectral dependence around each of these wavelengths is nearly flat. Zhang performed the research on an Asian male with dark red skin and a Caucasian male with light-pink skin. As a rule of thumb, these types of skin are typical among all kinds of skin and surely suitable for a spectral decision of our preferable wavelengths. A similar phenomenon in darker skin was reported by Anderson (Fig. 5).⁴ Another reason for choosing these wavelengths, 520 and 700 nm, is the fact that penetration depths of 0.1 and 0.25 mm, respectively, as was shown by Anderson ¹¹ allow us to detect skin cancer in the superficial layer of skin, before its penetration into the dermis.

Fig. 6

Schematic of the WILCPR, linearly polarized light impinges from the left side into the first retarder LCTR1 (oriented at 45 deg), then through the second retarder LCTR2 (oriented at 0 deg), tuned to behave as a tunable QWP. Rotation control for the output is executed by the voltage control of LCTR1 for any wavelength selected by the LCTF.

Fig. 7

(a) N frames are taken during continuous change in α_k, the rotation angle of the impinged light on the tissue. I _s is the image where a prominent specular-like light appears. I _d is obtained when most of the light is backscattered from internal layers of the tissue and no specular-like light is observed. (b) An example of BCC lesion for I _s and I _d situations.

WILCPR is a wavelength independent tunable liquid crystal polarization rotator that we developed to operate with tunable filters such as LEO. Two nematic LC retarders comprise the device; see Fig. 6, LC tunable retarder1—LCTR1 and LCTR2. Linearly polarized light impinges from the left side into the first retarder LCTR1 (oriented at 45 deg), then through the second retarder LCTR2 (oriented at 0 deg) that is tuned by voltage V ₅ to behave as a tunable quarter wave-plate (QWP). Rotation control for output is executed by voltage control V ₄ on LCTR1 and synchronized with the algorithm of CCD camera during image capture.

WILCPR is able to rotate, continuously, a linearly polarized light for a spectral range of 450 to 1000 nm while keeping it linear. Jones matrix for a stack of the two retarders is given by Eq. 3. We omitted the accumulated total phase term from the ordinary and extraordinary modes. δ ₄ and δ ₅ are retardations calculated by integration over LC thickness d ₄ and d ₅, Eq. 2. QWP condition is achieved for each wavelength by tuning voltage V ₅ so that δ₅ at each wavelength is tuned to be π/2. Jones matrix for such a system is:

For incident linear polarization along the x axis, Jones vector is [TeX:] $J_{{\rm in}} = ({\begin{array}{*{20}c} 1 & 0 \\\end{array}})^T$ $J_{\mathrm{in}}={\left(\begin{array}{cc}1& 0\end{array}\right)}^T$ and hence the output Jones vector will be:

From Eq. 4, the output polarization is linear and oriented at an angle equal to δ ₄/2 which is governed by voltage V ₄. The device is able to rotate the polarization by any degree, for a spectral range from 400 nm to more than 1000 nm in about 50 ms switching time, and between adjacent discrete angles the device is even faster. Due to that, it is possible to synchronize the capture by the CCD camera to the rotation of the impinged polarized light.

2.3.

Scanning Method and Post Processing

Boundaries and high scattering zones are more sensitive to changes of the incident light polarization angle α_k. Thus, rapid changes will be generated in the images between two or more angles α_k, namely adjacent angles, say α_{k + 2}, α_{k + 1}, α_k as shown in Fig. 7, when I _k is the image intensity. On the contrary, areas that already experienced denaturization of the dermis or epidermis layers will exhibit weak differences between images at different angles of the impinged polarization. While rotating the incident polarization, DOSI produces the sequence of images I _k, as shown in Fig. 7, k = 1 to N. N is the number of the total frames and k is the number of the kth frame in the sequence. Sequence is captured while the WILCPR rotates the polarization (α_k). The reason for that is to improve the signal-to-noise ratio, particularly important for regions with small contrast. Equation 5 demonstrates calculated contrast between two adjacent angles. High scattering areas in C _λ,k will be averaged in the final post-processed image of C _λ,A defined in Eq. 6. This can be done online during the scan or right after the capture process is over.

Returning to Eq. 7, SD will take the form of:

From Eq. 10 it is clear that the contrast originates mainly in B _P and B _S, because the background of BP and BS will always be different. P _P and P _S also increase the contrast since the light that keeps some DOP will also be different depending on the polarization of the incident light. Hence, SD helps to emphasize the lesion area and lesion boundaries by two mechanisms—polarization change in the first layer (P _P − P _S) and different attenuations (B _P − B _S), scattering and absorption of the backscattered light originates in deeper layers. Note, that the SD image resembles the normalized Stokes image S ₁/S ₀. Nevertheless, I _s and I _d are not orthogonal in the mathematical sense because the analyzer is fixed and subtraction I _s − I _d is performed at different backscattered backgrounds B _P and B _S as explained before.

Figure 8 shows an example of the SD image which shows features not seen in I _s and I _d. Well-defined boundaries are important information before executing the MMS. The SD parameter gives additional information to compare with our main approach [Eqs. 6, 11].

Fig. 8

SD image for the contrast between the two images in Fig. 7b illustrating an emergence of well-defined boundaries of the BCC.

We are interested in frames excluding those with the specular-like reflection, so that the polarization changes of the backscattered light from underneath the stratum corneum can be collected and emphasized. C _λ,k in Eq. 5 is the basic differential contrast and C _λ,A in Eq. 6 is a result of collecting and averaging a range of pictures at different α_k angles. In addition, we define an image that represents the curvature D _λ of the intensity on the CCD readout during changes in α_k of the incident polarization. Let us take three points of intensity (arbitrary pixel) I _k, I _{k + 1}, and I _{k + 2}. The numerical second order derivation, as can be found in any mathematical handbooks, is: Δ² I/Δα² = [(I _{k + 2} + I _k)/2 − I _{k + 1}]/Δα². Δα² = (α_{k + 1} − α_k)² is the numerical constant. Let us follow the sampled signal on one of the CCD pixels as shown in Figs. 9a, 9b, where the axes are the intensity on a particular pixel versus discrete polarization angle, α_k, of the incident light. To express the image D _λ we use the magnitude of the distance between the sampled intensity at point (α_k+1,I _k+1), and the average value between two adjacent points (I _k+2 + I _k)/2.

Fig. 9

(a) The variation of the intensity in an arbitrary pixel at the CCD camera while α_k changes. The solid line represents fitted continuous intensity variation while the black circles are its numerical samples. (b) Demonstration of a case where practically there is no change between I _k and I _k+2 but the image I _k+1 exhibits some noise and should not be taken in account in Eq. 11.

To avoid the cases when the change between two points I _k+2 and I _k is not prominent or originating from noise, we multiply by the difference |I _k+2 - I _k|. During the scan the angle of impinged polarization on a lesion changes continuously. Hence, we expect a continuous change on the CCD as well, so if the image I _k+2 does not change much compared to I _k, we interpret the jump in I _k+1 as a noise which can be ignored. This is demonstrated in Fig. 9b, taking into account multiplication by |I _k+2 - I _k| in Eq. 11.

In other words, Eq. 11 collects cases similar to Fig. 9a when there is a prominent difference between I _k, and I _k+2 [in a similar manner to Eq. 5 which is enforced with the magnitude of the intensity difference between points I _k, and I _k+2]. The differential polarization scanning method of DOSI is considering the fact that deep layers will cause the impinged polarized light to loose its polarization and emerge as a nonpolarized background light. So Eqs. 6, 1 will mainly emphasize the changes of the superficial layer of the lesion, while still some DOP is preserved.

In Fig. 10 we illustrate qualitatively the penetration depth and marked the area where the changes in the impinged polarization can be manifested in differential changes in the polarization of the backscattered light [Fig. 10d]. On the contrary to deep penetration [Fig. 10e], the backscattered light will produce a background reflection almost constant while rotating the linearly polarized impinged light (nearly completely depolarized light). So the main information produced by Eqs. 6, 11 in the post-processing will be due to scattered light in-between the stratum corneum and the subcutaneous layer or dermis layer. Post-processing of the DOSI system will present images C _λ,A versus D _λ as images with gray scale colormap. The code is written using MATLAB with the flexibility to emphasize layers of the output images. This virtue is useful to detect patterns among noisy images, and will be used in Sec. 3.

Fig. 10

Schematic description of penetration of light through tissue. For deep penetration, such as in area (e), the backscattered light will produce a background reflection almost constant with the rotating linear polarization due to strong depolarization. (a) Stratum corneum and epidermis layers, (b) dermis layer, (c) subcutaneous layer. In regions (d) and (e), polarized photons and random photons are obtained, respectively.

The number of patients that participated in this study was 44, ages 18 to 101, who where scheduled for removal of 77 skin lesions. In vivo images were obtained in the Department of Plastic and Reconstructive Surgery and Burn Unit at Soroka University Hospital. Images of skin tumors were been captured before the patients entered into the operation room for their surgery. The ABCD method was used by the dermatologist and the MMS surgeon in the clinic before the patient had the surgery. We used their diagnosis in the clinic and compared it with the histology findings and attached it to DOSI images for categorization. Images were sorted into different types of skin lesions: 10-Seborheic Keratoses (SK), 13-Compound nevus (CN), 6-Junctional nevus (JN), 11-Intradermal nevus (IDN), 5-Basal cell carcinoma (BCC), 2-Pigmented basal cell carcinoma (PIG BCC), 4-Squamous cell carcinoma (SCC), 1-Hemangioma, 4-Lentigo, 16-Dysplastic nevus, and 5-Melanoma. We looked for repeating features of images for each tumor (which was histologically diagnosed) and built a database.

3.1.

Benign Lesion as Appearing in DOSI Images

With the assistance of the pathologists from the Soroka University Hospital, we could interpret the DOSI images according to the histological findings. The histological pictures were shown after figures S, C, and D, as in Fig. 12. Note: As mentioned before, the FOV of the camera is 8 mm, hence the diagonal of pictures S, C, and D is 8 mm. Histological pictures were grabbed by a digital camera of 10 megapixels. Arrows within the histological pictures are pointing to explanatory areas, teaching us about feasible projection between microscopic and macroscopic images. Figure 11 shows a benign lesion of CN-type. DOSI could produce a different texture especially in CG and CR images, emphasizing the boundaries of high scattering areas, when DG and DR do not show any prominent patterns. The lack of strong inner patterns might allude about the degree of dysplasia, which in this case is very weak. In Fig. 12 (magnification of the doted square in Fig. 13), a histological picture shows some features of melanocytic cytologic atypia, enlarged cells with increased nuclear size variation and enlarged nucleoli, which may produce this type of high scattering areas when equally distributed. In a wider view, as Fig. 13 shows, one can see a homogeneous distribution of a benign lesion probably due to ordered collagen fiber structures. One of the origins of the disordered collagen stems from new collagen formation of malignant lesions. In this case, neat order indicates benign lesion.

Fig. 12

The lesion shows some features of melanocytic cytologic atypia (enlarged cells with increased nuclear size variation and enlarged nucleoli).

Fig. 13

Algorithm D shows homogeneous distribution of the benign lesion probably due to a relatively ordered collagen. The white vacancies are tears in the tissue when the lesion was cut from its wax holder cube.

We found that as long as dysplasia was low, the CORV also was low, a fact observed in many types of the investigated lesions. For example, Figs. 14, 15 is a case of JN, where CG and CR are showing texture of scattering areas in patterns slightly different from what is seen in the S-picture, probably due to irregularly distributed and elongated epithelial buds producing a “dusty” irregular margin as shown in Fig. 15(1), when algorithm D shows no prominent inner pattern because of a regular characteristic of collagen, see Fig. 15(2).

Fig. 14

Lesion type, JN, localized at the left back.

Fig. 15

(1) Irregularly distributed and elongated epithelial buds producing a dusty irregular margin. (2) Histological picture showing a regular characteristic of the collagen, which may be the reason why in algorithm D in Fig. 14, no prominent boundaries were seen.

There were cases when a lesion was starting to show some degree of correlation between images of C and D Medium-CORV (Med-CORV). In these cases it is suspected that dysplasia starts to emerge as shown in CR-DR in Fig. 16 where a superficial scan (G-images) presents a hazy image, but the deeper scan (R-images) shows a pattern with a Med-CORV between CR and DR indicating upon moderate dysplasia. We suspect that dysplasia already took place over the first layer of the lesion and started to penetrate the deeper layers. Sometimes when the dysplasm is strong and old it covers the upper surface first and then penetrates. That is why the G images look hazy and the R images show some patterns. Longer wavelengths like in R images target the deeper layers when the thin first layer can be almost transparent, mainly if the dysplasia finished to form patterns in the first layer and already covers equally the first layer of the lesion. This correlates to a histological picture (Fig. 17), where the collagen is bundled within the lesion and there are gaps between the bundles, that is why R-images show strong scattering with defined boundaries, when superficial melanocytes are located in the junctional region of the epidermis, and the collagen is distributed equally upon the upper part of the lesion. Thus, a smeared pattern is seen in the G-images.

Fig. 16

Lesion type, JN, localized at the upper back.

Fig. 17

(1) Superficial melanocytes located in the junctional region of epidermis might cause G-images to be smeared or hazy. Another reason for this hazy look is that (2) the density of the collagen is equally distributed within the lesion. (3) Strong density of collagen as bundles in the deeper layers where its distribution is versatile in different areas of the lesion. Thus, DR shows distinguished patterns due to better penetration at wavelength of 700 nm.

A strong density of collagen can be realized also in the deeper layers as a bundled collagen increasing the scattering for longer wavelength such as in the R-images. DR shows patterns that lead us to the idea of JN with some inclination to be also dysplastic. Since dysplasia can lead to neoplasia we should be alerted when this type of sign emerges in DOSI images.

The next lesions (Figs. 18, 19) were diagnosed in the clinic as a CN-type lesion. The DOSI scan showed clear CORV, raising our suspicion that this lesion is not a simple CN but also has dysplastic features. Indeed, after histological investigation, the lesion was found to have dysplastic characteristics, some dermatologists mark it as CN(DN). Again, dysplasm, can lead to malignancy, so following the evolution of the dysplasia over time with the DOSI system may be very crucial for the patients life. Histology image show mild dysplasia which manifests itself in algorithm D of Fig. 18, when CG and DG have higher CORV than CR and DR. In later examples we shall see that in malignant tissues there is very strong CORV as a strong similarity between patterns of C and D. Histology in Fig. 19 shows that collagen is denser in some areas than in normal tissue, which increases the difference in index of refraction between areas within the lesion. By contrast, in deep layers the collagen is equally distributed with intact gaps between collagen bundles, presented as a more smeared image of CR and DR with Med-CORV. Figure 18 is representing the most important goal of the DOSI method due to its ability to give some indication about the dysplastic nature of a lesion that could not be seen with a standard dermatological inspection tool (dermatoscope). When using DOSI over time, patients and physicians will have a notion about the evolution of the dysplasia and tissue distortion.

Fig. 18

Lesion type-CN, localized at the lower back. After histological inspection it was found to have dysplastic characteristics, CN(DN). The high CORV shows similarity between algorithms C and D, thus supporting the idea of existence of dysplasm.

Fig. 19

(1) Histological picture showing the intradermal component of a lesion which is located deep in the epidermis, thus C images show boundaries inside a lesion, probably representing the subepitelial collagen. (2) The degree of collagen distortion is above normal, this may lead to some patterns in the DG image in Fig. 18. This pattern is close to the dermis due to the green light used for G images.

3.2.

Dysplasia and Neoplasia as Appearing in DOSI Images

Cases of dysplasm appeared in DOSI images with good correlation between the degree of dysplasm and the degree of CORV between C and D. The higher the grade of dysplasia (or neoplasia) is, the higher the CORV. Let us review one of the scanned lesions of the DN type and its histological diagnosis that proved to be with mild dysplasia. Images in Fig. 20 enforce this idea that dysplasia can be spotted according to the degree of the CORV. C and D are showing low-CORV between patterns CG – DG and CR – DR which have a little higher CORV, see the marked areas in dashed line. In Fig. 21 we see the existence of a low degree of dysplasia in the upper part, where the deeper part preserves the natural structure of dermal collagen. Although the change in collagen density is slightly increased in the superficial (papillary) dermis it is equally distributed, thus causing the post-processing of D-images to look a bit hazy. But it still resembles C-images, consequently have some CORV.

Fig. 20

DN lesion, localized on the chest. The dashed lines in D-images are marking the similar patterns resembling patterns in C-images that reveal some CORV.

Fig. 21

Histological picture showing weak dysplasia. (1) A low degree of dysplasia in the upper part, (2) preserved structure of dermal (deep) collagen, and (3) slightly increased density of collagen in superficial (papillary) dermis.

Progression of dysplasia to neoplasia as in the case of CMM, is a relatively slow process. In principle, we would like to be able to diagnose the premature stages of the so called innocent lesion and let the imaging system help the physician or the patient follow the lesion over time and realize whether the lesion progresses to neoplasm. We believe that pattern-sorting and the degree of the CORV are posing a feasible way to do so. To understand the worst case in which a dysplasm can be mutant to neoplasm or cancer, several cancer lesions were scanned with DOSI that are known to have a high degree of dysplasm. Strong CORV was found between C and D-images as presented in Figs. 22, 23, 24, 25, 26. The lesions margins in S-pictures cannot be seen because the lesion is much larger (diameter more than 1 cm) than the FOV of the CCD, which in this cases 8 mm.

Fig. 22

Malignant melanoma (MM) lesion, localized at the lower thigh. The lesion has very High-CORV.

Fig. 23

MM lesion, localized at the mid-thigh. The lesion has very high-CORV.

Fig. 24

MM lesion, localized at the hip. The lesion has very high-CORV.

Fig. 25

MM lesion, localized at the lower thigh. The lesion has very high-CORV.

Fig. 26

MM lesion, localized at the lower back. The lesion has very high-CORV.

This indicates a high dysplasm or distorted tissue which is expected in this type of neoplasia. These figures were chosen due to the different patterns emerging from them. Cancer cells first spread horizontally, then in later stages they start to metastasis to deeper layers toward the dermis. In this process the cell proliferation passes transient stages. One can identify patterns which are similar to those in the work of Vicsek ³² where positional and spatial trajectories of particles were shown to be very similar to the trajectories the cancer cells plough within the healthy tissue.

Remembering the main goal to diagnose the evolving dysplasia over time, the following case of Fig. 27 is for a Caucasian woman with a seemingly innocent mole that most people would not bother themselves to check in the clinic. The DOSI scan shows very high-CORV, something that can be developed to a cancerous case. After histological inspection, the pathological diagnosis for this lesion was dysplastic compound (DC)-type nevus, with very high dysplasm. It is a classical case of using DOSI to warn the patient.

Fig. 27

DC lesion, localized at the right breast of a female patient. In the S-picture: a clear mole can be seen with uniform gray scale. But DOSI could show distortion of tissue by CORV indicating dysplasm.

In other cases similar to the lesion shown in Fig. 28, a male patient came to have his MMS after the diagnosis of three dermatologists telling him he has skin cancer. The inner area of the lesion has few degrees of colors, nonregular boundaries which can be very suspicious, etc. As can be seen in Fig. 28 there is low-CORV between C versus D-images and most important the hazy look at the D-images, which does not show circular patterns as in C images. Consequently, we suspect it is not skin cancer and maybe has some degree of low dysplasm. The patient finally had his MMS but when the histological results returned from the pathology, the histologists report was DC-type nevus, but no cancer like CMM as previously diagnosed. DOSI diagnosis was aligned with the histology.

Fig. 28

Lesion that was thought to be cancerous (CMM) while the DOSI scan showed that it is not. Histological pictures approved a low level of dysplasm but no cancer.

Another area to which DOSI may contribute is to help pathologists understand, in the macroscopic point of view, the distribution of the damaged collagen and dysplasm inside the lesion. In order to have a side slice of the lesion for having its histological diagnosis, the lab technicians are using wax cubes immersion to keep the lesion after the MMS removal. This cube of wax fixes the lesion in a way so that it becomes easier for the pathologist to remove a side cut and hold it between two glasses for microscopic inspection, similar to the figures presented above. Figure 29 shows the same lesion as in Fig. 28, but now we used the DOSI scanner to reveal differences in scattering areas over the side-cut. It was very intriguing to see that DOSI can open a whole new research about the collagen quality and distribution inside a lesion only by following the scattering characteristics in the side-cut. Modifying the gray scale of the algorithm we could emphasize the small differences in texture even more Fig. 30 shows the finest contrast C-images of Fig. 29.

Fig. 29

Same as Fig. 28, but a side-cut of the lesion situated in a block of wax. A brighter white can be spotted where collagen formation zone is expected.

Fig. 30

Contrast improved to emphasize the differences in the inner texture of collagen areas.