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Colposcopy is the commonest method employed to visualize human uterine cervix, and to diagnose uterine cervical cancer. When this structure is altered, colposcopy identifies and grades a number of characteristics of the lesions such as sizes, borders, shapes and vascular patterns. However, some drawbacks of the findings obtained by the classical colposcopic method are still unanswered. To improve the quality of the data obtained with colposcopy, selected images of human cervix were studied; further, a novel processing analysis was done using the top-hat and direct threshold methods.

The analysis tree included selection of the image, elimination of frequency of oxyhemoglobin absorption, unsharped mask, treatment of specular highlights, filter approach and adjustment of intensity levels. These findings were unrelated to the filter used.

These results may help to increase the likelihood of detecting uterine cervical cancer of humans at earlier stages of the disease than known to date. Studies also have shown that HPV infection produces inflammatory changes in the vascular pattern of the cervix Clifford et al. The changes in the vascular pattern are believed to be present at early stages of cervical cancer Guidi et al. Such changes are often diagnosed by using colposcopy.

Colposcopy developed by Hinselmann, inis commonly done by direct observation of the uterine cervix Fusco et al. Acetic acid topical application, or acetowhitening, on the suspicious abnormal area is used to improve the visualization of cervical tissue Schiller, The application of the acetic acid induces changes of the colour of the area under study.

The new colour of the anatomical area under study ranges from white to gray; such colour change appears when premalignant cells are present. The described chemical reaction is due to a malfunctioning of epithelial cells of the cervix Reid et al. This infection disorientates keratin filaments in the superficial layers of the squamous epithelium Zijlstra et al. Hinselmann created an optical stereoscopic microscope to visualize the cervix using the aforementioned technique.

Such microscope used incandescent light or white light-emitting diode LED type. It allowed visualizing the morphology of the blood vessels of the cervix Fusco et al. Kraatz used a green filter on the light beam to highlight the vascular pattern, which this author contrasted with the tissue background. Limitations of current optic methods or disturbed visual acuity preclude the detection of critical fine vascular changes that are key elements of lesioned tissue to make an accurate and timely diagnosis Vlachokosta et al.

Thus, state-of-the-art imaging techniques such as the ones we demonstrate in this research may be of value to improve early diagnosis of cervical cancer Durdi et al. Germane to this, segmentation techniques have allowed differentiating vascular anomalies of the cervix including vascular loops and webs Srinivasan et al.

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However, at present, none of the above mentioned biotechnological improvements have properly made possible to highlight the vascular pattern of the uterine cervix. This research aims to fill this gap. Thus, we developed a novel method using digital image processing. This method looks for enhancing the vascular patterns of uterine cervical tissue in images obtained by digital colposcopy; the results of this investigation will help to detect lesions of human cervix at earlier times than done at present.

Methods Selection of the picture step Io, figure 4 For this research, which was approved by the Institutional Review Board of Mediciencias Research Group, we selected 64 colposcopic images from our databank.

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The digital camera that took the photographs had a resolution of four mega-pixels. Then, the pictures were pseudorandomly selected according to defined criteria.

The inclusion criteria of the samples we selected took into account any injury observable during the colposcopic procedure, including inflammation or regions with suspected noninvasive cancer. An exclusion criterion was that the images had less than 40 kB; we also excluded images found more than once in the list searched at the data bank Figure 1. The final sample was composed of four images. Selection of the wavelength of interest WOI step I1, figure 4 The electromagnetic band between nanometers nm and nm Figure 2 has two hemoglobin related peaks above the baseline Figure 3.

The first peak appears at nm, and the second peak at nm Zijlstra et al. Accordingly, we selected WOIs of the image localized at the green band of the electromagnetic spectrum, which has between nanometers nm and nm Figure 2 to properly detect oxyhemoglobin from the colposcopic images, in a red-green-blue RGB model. Unsharped mask step I2, figure 4 To better analyze the blood vessels of the cervix, unsharped mask was added.

To accomplish this, a Laplacian filter was used. This filter enhances image regions having that display high frequency levels; in making so, it is possible to achieve a much better contrast of the image. The Laplacian filter was constructed as follows: First, a negative Laplacian matrix He was defined [ Equation 1 ]. The Unsharped image I2 obtained was the result of the convolution of He and the original image I1 [ Equation 2 ].

Treatment of specular highlights step I3, figure 4 A specular highlight is the spot of light that appears on objects when they are lighted up.

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In our case, the fluids of the cervical mucosa produced such specular highlights Dvir et al. In image processing techniques, methods that improve the quality of the image such as lighting, saturation and OTSU threshold method Srinivasan et al. However, such methods lack specificity to correct non uniform illumination of the image under study. Therefore, better methods have to be considered. In this line, top-hat methodology allows extracting the information needed from pictures in a better way than the mentioned above.

Thus, we used it. The equation 3 shows the difference between the images obtained in the step I1 minus the opening operation using a circular structure element EETH ; it was done assuming that the brightness approximated to spotted shapes.

The opening operation produced mathematical erosion; for this reason, the image was expanded. Specular highlights are those ITH that have values higher than the threshold named to as Uo. Thus, the threshold value was calculated taking the minimal intra-class variance Otsu, The obtained data were then masked by M [ Equation 4 ].

This manipulation ensured that the M1 mask covered the highlights, before to use the Laplace interpolation. After this, we solved the equation 7 for each unknown points. Low-pass filtering step I4, figure 4 Following the technique described by Tomazevic et alwe created an image called IPB, which was obtained after convoluting it.

Such convolution was the result of multiplying I1i times H as shown in equation 8. Hm in equation 9corresponded to the average low-pass filter, and Hg in equation 10corresponded to the Gaussian filter. This conserved both, the smooth changes coming from the illumination procedure, and the changes produced by variations of the intensity in the tissue.

Adjustment of intensity levels of pixels step I5, figure 4 In this step, we used a linear transformation function T l [ equation 12 ]. The equation 12 represents the light intensity of each pixel. These data were converted in two new values of intensity named to as ao and bo they corresponded to the values obtained among those that desired redistribute the new histogram. Such conversion was done for redistributing the intensity of the pixels within the range of available intensities.

Li showed a new intensity distribution, which was obtained from Lo levels as shown in Equation The histogram of the resulting image from the illumination correction I3, figure 4 and figure 5 was concentrated in a particular area; it did not occupy the entire range of intensity levels.

Using the transformation function T Lan image with higher contrast was obtained I4, figure 4. As a result, the levels of the I3 image are distributed in a wider range of intensities currents as shown in Figure 5. Lastly, the values obtained by top-hat method were statistically compared with the values obtained by direct threshold method.

Chi-square was used to compare proportions. P value was setup at 0. Results A full description of the results is presented in figure 6. In a a green layer of the original image is shown; b shows the Interpolation of specular highlights; c presents the filtering process; d allows knowing Correction of illumination, and e reports adjustment of intensity levels. In figure 7two original colposcopic images were included.

The first image 1a showed highlights whereas the second image 2a did not. In figure 7 b, it was observed the data obtained after treating specular highlights using the direct method. In figure 6 c, the image was analyzed using the top-hat method. It was found that with the direct method, highlights were not detected in the darkest region figure 71b.

In absence of highlights, artifacts were detected figure 72b. Using the top-hat method the opposite occurred. Note that when applying direct threshold method, no highlights were detected in the darkest region arrow in figure b.

When highlights decreased, artifacts were detected star in figure c. Using the top-hat method, the opposite happened b, 2c. In figure 8sections of vascular images obtained from colposcopy showed hairpined aclothed b and dotted patterns c, d. After performing the enhancement process in the three layers of the RGB model, a color image was obtained. In c the vascularization pattern was highlighted in a dotty-shape like form that was not evident prior to processing.

In da lesion on the squamous epithelium was also observed. Of note, this lesion was unseen in the original image obtained, due to the lack of observation of internal vascularization. It must be emphasized that once the enhancement was made, the vascular structures on the affected region were better observed; therefore, the contour of the injury delineates better.

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