Using equalization in YIQ color model and curve adjust by splines for morphometric evaluation of histological slides in mice Manuel G. Forero-Vargasa , Eduard L. Sierra-Ball´ena , Wilman H. S´anchez-Rodrigueza , Fredy Tejedor-Orduza , Karen Hern´andezb , Ana Vinascob , Eduardo Lowb and Angel Bernalb a Grupo
OHWAHA, Univ. Nacional de Colombia, Ciudad Universitaria, Bogot´a, Colombia b Facultad de Odontolog´ ıa, Universidad Nacional de Colombia, Ciudad Universitaria, Bogot´a, Colombia ABSTRACT
This paper presents two image techniques for morphometric evaluation. The first one improve the color contrast employing color equalization and borders are identified by using splines. The second one is a semiautomatic method that use fuzzy color thresholding. The second technique will provide the basis of a future automatic method. These techniques are experimentally validated measuring neoformed vessels on histological sections of mice’s thigh. Keywords: Color model, segmentation, color equalization, spline, fuzzy logic thresholding, histological slides, AINEs, scaring, angiogenesis
1. INTRODUCTION The angiogenesis (vessel formation), is a event in the complex reparative process scaring, it is composed by several events. These events appear simultaneously or sequentially and without them it is impossible to repair the tissues. The identification of vessels on histological slides is a difficult work and requires specialized personal. However, the classical process to measure the vessels is totally manual, it is time consuming, not precise and does not allow obtaining a measure of the area and perimeter of the vessel. Furthermore, in some cases the borders of the vessels are not visible and the measure can not be made. In order to measure the vessels using image processing is necessary to segment them. Few techniques have been developed to segment color images. Forero,1, 2 proposed a color segmentation method by using fuzzy logic thresholding on the RGB channels of Koch Bacilli images. Two solutions are developed. The first one improved the contrast employing a equalization in the component Y of the YIQ model. Then, a manual segmentation is made to adjust spline curves. The second one employs a fuzzy thresholding technique to segment the color images. To evaluate the methods a study to compare the effects produced by the anti-inflammatory nonsteroidal drugs (AINEs) was developed. AINEs have an analgesic and anti-inflammatory effect. To date, there are not in-vivo studies to measure the angiogenesis process. The responses presented in an in-vivo model are studied by analyzing the measures of neoformed vessels. The goal is to have a parameter to compare the angiogenesis with the different drugs in the mouse. Further author information: (Send correspondence to M.G.Forero) M.G.Forero: E-mail:
[email protected], http://dis.unal.edu.co/∼ohwaha
(a) Original image.
(b) Sobel.
(c) Prewitt.
Figure 1. Edge segmentation of the vessels.
2. MATERIALS To develop the approach, forty mice of the Balb-C stump certified, endogenics with twenty year breeding. The animals were treated following the international regulation for the animal treatment.3, 4 A circular incision with a diameter of 7 mm was made in the right thigh of the animals that involved skin and muscular tissues. Then, they were divided in four groups: Acetaminophen, Ibuprophen and Rofecoxib, according to the applied drug, and Control. Animals were dosed according to their weight and the pharmaceutical recommendations; every 6 hours for the Acetaminophen and the Ibuprophen groups and every 12 hours for the Rofecoxib one. Mice were sacrificed the third, fifth, sixth and eighth days after they were injured. Sections of the scar tissues were cut and put in 10% formol. The samples were stained with hematoxylineosin. Each section were examined through a 40X electronic microscope and pictures from the neoformed vessels were taken using a color digital camera attached to the microscope. The whole process was performed independently for each section. Then the new techniques were applied to evaluate the results.
3. IMAGE IMPROVEMENT AND CURVE ADJUST BY SPLINES To segment the neoformed vessels in the images, Sobel and Prewitt gradients masks were applied separately to each color component and a cumulative edge image was constructed. However, as it is shown in Figure 1 it is not possible to obtain a good approach to the borders of the vessels. The previous technique does not allow to separate the region of interest. In addition, the shape of the structures to be analyzed is not clearly delimited. Frequently, the vessel borders are transparent or diffused due to the images are taken against the light so they are very clear. It is also important the vessels maturity and the quality of the image to distinguish them. Therefore a continuous vessel border are not always visible. Because the difficulty in the identification of the borders, a manual delimitation must be employed. Therefore, it is not possible to design an automatic technique for the determination of the borders. However, the contrast can be improved to facilitate the borders recognition. It is important to maintain the hue to allow a better identification of the neoformed vessels, because the user is adapted to specific colors to distinguish the region of interest. To enhance the image contrast several equalization techniques are used. The equalization are applied in the RGB, CMY, CMYK, YIQ and HSV color models.
Table 1. Color equalization results.
Statistic
RGB
Mean Median Mode Variance Standard deviation
1,975 2 1 1,256 1,121
CMY 1,975 2 1 0,845 0,919
CMYK 2,200 2 1 1,754 1,324
YIQ Y 4,100 5 5 1,631 1,277
SV 2,650 3 4 1,669 1,292
HSV S 2,550 2 2 1,433 1,197
V 2,075 2 2 0,994 0,997
Equalization is a process employed in achromatic images to redistribute the grey level values of the pixels. It produces an image whose contrast has a much more balanced appearance.5 Based on the equalization principle, we have developed an equalization technique for different color models. The resulting image of the equalization process must maintain the hue while the saturation and bright change. In RGB, CMY and CMYK models the luminance information appear implicit in every component, so the equalization process is applied separately to each color component. In the HSV model the H(hue) component represent the tonalities of the image. So the equalization method can be applied to the S (saturation) and/or V (value or brightness) components. In the YIQ color space model, the Y (luminance) component contain the information about the image brightness, hence the equalization is only applied to this component. Figure 2 shows the histograms: from top to bottom and the left to right, original, equalized, original cumulative and equalized cumulative histograms of the Y component of Figure 1(a). Once the borders of the vessels have been contrasted by equalization, a closed cubic B-spline curve is employed in order to fit the border of each neoformed vessel. This step is made, on the computer, simply drawing on each image some control points around the vessel contour with the mouse. Because of the flexibility of the cubic B-spline and its low computational complexity, they can easily adapt to the vessels shape. The shape of the B-spline is modified changing the position of the control points with the mouse. The vessel area counting the number of pixels inside the B-spline and the perimeter is given by the B-spline length. The results are shown in Figure 3.
4. EQUALIZATION RESULTS An objective measure of the equalization technique cannot be applied since the vessel borders are not always visible and its delimitation is defined subjectively by the expert. In order to evaluate the technique a scale 1-5 was established. The low value (1) is given when is not possible to distinguish the border and the upper value (5) when the border is well defined. To avoid a bias the experts did not know the equalization model employed for each image. The neoformed vessels borders were qualified by two experts. The statistical results of the evaluation of 4o images are shown in Table 1. Figure 4 shows the obtained results of the equalization technique for the different color models. As can be seen in the table, the YIQ equalization gets the best qualification, presenting a big difference with respect to the other models. A good distinction of the vessels in neoformation borders is observed when the YIQ equalization is employed.
5. COLOR SEGMENTATION In order to developed an automatic technique, a fuzzy thresholding technique proposed by Forero,1, 2 segment Koch Bacilli images is been explored to segment vessels. The color method described apply the gray level thresholding to different color channels and the knowledge about the regions on the image to establish fuzzy rules that are employed in the segmentation. However, because colors are not uniform and there is not reproducibility
(a) Original histogram.
(c) Original cumulative histogram.
(b) Equalized histogram.
(d) cualized cumulative histogram.
Figure 2. Y component histograms of the image 1(a).
in the images it is not easy to fix an adequate inference rule to determine the region of interest. Figure 5 shows an example of the obtained results applying an AND inference function in the RGB model. The fuzzy thresholding is applied separately to each color component. The same color models mentioned above have been employed. The fuzzy technique employs the concept of measure of fuzziness associated with the histogram for the optimal selection of the threshold value. Four measures of fuzziness were tested: Shannon, Yager, Kaufmann and Gauss.2 A generalized bell membership function based on the Cauchy distribution was employed. The algorithm applied to test the technique consists of six stages: • Obtain the color components according to the color space employed.
(a) Ecualized Y channel image in the YIQ model.
(b) Control points.
(c) B-spline curve fit.
(d) Resultant area.
Figure 3. Vessel contour determination in Figure 1(a).
• Segment each component image by fuzzy thresholding. • Make a morphological opening to isolate the regions that could appear connected after segmentation. • Make a region labeling for identifying the various objects in the binary image. • Select the resulting image that presents the better segmentation of the vessels. • Measure the areas and perimeters of the labeled vessels. The results obtained with this algorithm for M component using the CMY color space and the Yager’s measure of fuzziness are shown in Figure 6.
(a) Original image.
(e) SV HSV.
channels
(b) RGB.
(c) CMY.
(d) CMYK.
(f) S channel HSV.
(g) V channel HSV.
(h) Y channel YIQ.
Figure 4. Color equalization. The arrows point to the vessel.
Figures 7.b-7.f show the results obtained applying the fuzzy segmentation in each color model. In order to compare the results, Figure 7.h present the image obtained by the experts using the YIQ equalization and spline method.
6. EXPERIMENTAL RESULTS In 40 images a total of 144 vessels were found. Table 2 shows the obtained results of the study. The Rofecoxib group presented the smallest mean of neoformed vessels: 1.7. The Acetaminophen group presented a mean of 3 and the Ibuprophen one a mean of 3.25. However, the Control group presented the highest mean: 6.9. The characteristics of the neoformation vessels were different in each one of the groups. The neoformed vessels in the Control and Acetaminophen groups had the biggest size and light. They had well
(a) Original image.
(b) Segmented vessel by fuzzy logic Cauchy b = 5.0 Yager d = 2.0. For all RGB channels.
Figure 5. Results of fuzzy logic segmentation. Table 2. Perimeters and areas of the neoformed vessels in the different groups of mice
Group Control Acetaminophen Ibuprophen Rofecoxib
Area (microns) 185.3 206.62 151.76 130.21
Perimeter (microns) 13.82 13.73 13.64 12.51
defined walls and most of them had a circular shape.The Ibuprophen’s vessels showed different shapes and the walls were a little thinner. The shape of the Rofecoxib’s vessels presented deep variations, their shapes were completely irregular, The walls were the thinnest and smallest.
7. DISCUSSION The application of simultaneous equalization on the RGB, CMY,CMYK color models, does not contribute with big variations in the contrast among the vessels borders. However, it modifies notably the original tonalities. In the HSV space, the equalization of the saturation component (Figure 4.f) improves the contrast among the borders slightly, hence there is not a big change in the definition of the borders. When the V channel is equalized (Figure 4.g) or the S and V channels are equalized simultaneously (Figure 4.e) the image becomes darker, and the visualization of the borders is improved notably. The image equalization using the YIQ model, on the Y component, maintains the image chroma. This is obvious since the color components are not modified. The experts found that this equalization allows to determine more easily the borders of the neoformed vessels. The fuzzy segmentation technique applied in the studied models allows to obtain good results when the vessels have a good definition. In the CMYK model this technique cannot define the region of interest (Figure 7. The murino model used in this study was sensitive to evaluate the angiogenesis, since neoformed vessels could be observed. The obtained results also show that the Ibuprophen, Acetaminophen and Rofecoxib affect the quantity, area and perimeter of the neoformed vessels, that is, the angiogenesis changes with the AINEs. It was observed that the Acetaminophen does not have a deep influence in the shape of the vessels. However, the
(a) Original image.
(b) C channel.
(c) M channel.
(d) Y channel.
(e) Thresholding of M channel by fuzzy logic, Cauchy b = 5.0, Yager d = 2.0.
(f) Morphological opening.
(g) Labeling.
(h) Resulting image.
Figure 6. M channel processing in CMY color model.
AINEs have an important influence in the angiogenesis inhibition and could affect the appropriate development of the tissues scaring.
8. CONCLUSIONS In this paper, we have presented a new approach to the study of vessel images. Advantages are simplicity and speed. In clinical practice, this technique reveals as a very useful tool. The technique allows measuring the area and perimeter of the vessels. The use of the YIQ model and its equalization technique highlight the definition of the regions of interest, without changing the coloration, which facilitates the the determination of the borders to be measured. The use of B-splines allows to determine the shape of the neoformed vessels, hence a better precision in the measurement of the vessel perimeter and area. Fuzzy thresholding techniques had been tested. They are useful when the vessels are well defined. This study must be complete to obtain an automatic technique. An experiment had been developed in order to test the technique showing the potential of the application. The set of procedures described had been applied to to compare the effects produced by the AINEs in the
(a) Fuzzy segmentation in RGB.
(b) Fuzzy segmentation in CMY.
(c) Fuzzy segmentation in CMYK.
(d) Fuzzy segmentation in HSV.
(e) Fuzzy segmentation in YIQ.
(f) Image obtained by experts.
Figure 7. Resulting images employing the fuzzy segmentation.
angiogenesis in a murino model. Based on these results we can infer that the indiscriminate use of the AINEs, could produced physiologic alterations and complications in postoperative periods that involve scaring processes.
REFERENCES 1. M. G. Forero, E. Sierra, J. Alvarez, J. Pech, G. Crist´ obal, L. Alcal´ a, and M. Desco, “Automatic sputum color image segmentation for tuberculosis diagnosis,” Proc. SPIE 4471, pp. 251–261, 2001. 2. M. Forero, “Fuzzy thresholding and histogram analysis,” in Fuzzy filters for image processing. Series Studies in Fuzziness and Soft Computing, M. Nachtegael, D. V. der Weken, D. V. D. Ville, and E. Kerre, eds., Springer Verlang, 2002. 3. A. Mrad and C. Cardozo, “El uso de los animales de laboratorio en la investigaci´ on biol´ ogica y m´edica: credibilidad y competitividad,” tech. rep., Universidad Nacional de Colombia, September. 4. A. Mrad and C. Cardozo, “Utilizaci´ on de animales de laboratorio en la experimentaci´ on biol´ ogica,” tech. rep., Universidad Nacional de Colombia, 2000. 5. R. Gonz´ alez and R. Woods, Digital image processing, Addison-Wesley Publishing Company, Inc., Reading, Massachusetts, 1992.
