Automated visual scoring of psoriasis

Transcription

1 Automated visual scoring of psoriasis David Delgado Gomez, Toke Koldborg Jensen, Sune Darkner Jens Michael Carstensen Informatics and Mathematical Modelling Technical University of Denmark,Lyngby,Denmark May 27, 2 Abstract One of the most important tasks in the treatment of psoriasis is to evaluate the degree of the illness. Each doctor usually treats a high number of patients. This together with the fact that the time between visits can be weeks or even months makes the monitoring of the treatment difficult. Each time that a patient goes to the hospital, doctors take some scores that help them to remember how the illness is evolving. The scores that doctors takes are usually the degree of redness, the degree of scaling and the thickness. However, these scores are strongly dependend of the doctor e.g. they can not be suitable if another doctor has to treat these patients. This work aims to develop an image based system which automatically can obtain the differents structures involved in psoriasis images. The obtained structures can be used for a posterior automatic scoring, independent of the doctors. keywords: Gabor Filters, discriminant analysis, Potts models, psoriais. 1

2 1 Introduction Psoriasis is a persistent skin disease with an unknown origin. The illnes is characterized by inflamed, red and thicknened areas in the skin with white scales. The illness can be found in different ways depending on some factors as severity, location or pattern of the scales. The most commom places where the illnes is found are in nails and body folds although it can be found in elbows, knees, arms, legs, etc. Dermatologists diagnose psoriasis by examinating the skin. For following the evolution of the treatment, they make scores of the redness, scaling and thickness of the deseased areas of the patient. However these scores are highly depending of the doctor. Differents doctors can have variations in these scores. So if e.g. one patient has to carry on the threatment with another doctor, the observations of the first one are not useful. Moreover, these scores can even be different with the same doctor and patient. The scores for a patient can be influenced by the previous one. E.g, if the previous patient has a strong psoriasis the scores for the second one can be smaller than they really are. The objective of this work is to make a preliminary work that can be used for a posterior automatic grading. The aim is to extract in an automatic way the different structures that can be found in a skin image with psoriasis. The main problem is the difficulty for automatically distinguishing some of the main structures due to their similarity. The scales and the normal skin are frequently hard to differentiate. The proposed system is made of two ingredients. The first one is a simple classifier in which a Gabor feature is included to improve the discrimination. The second one consists of an extended Potts model that takes into account the information provided by the geometry of the image. The extended Potts model claims to smooth the obtained results in the first step. A real experiment is conducted for testing the validity of the developed system. 2 Segmentation of the different structures As it can be checked in figure 1, the main problem for extracting properly the different structures is the similarity between them. The red area is quite similar to the brown one. However, the problem is harder with the scaling and the normal skin. For avoiding this problem a new feature has been introduced: a Gabor feature. 2.1 Gabor Filters As it has been mentioned above, one of the main problems in extracting the interesting areas, red and scaling, is the difficulties to distinguish them from others kind of structures. To make the system discriminate properly these two structure, Gabor filters are introduced. 2

3 Figure 1: Scaling and normal skin in a psoriasis image A two dimensional Gabor function g(x,y) is given via ( ) 1 g(x, y) = e 1 2 2πσ x σ y ( x 2 σ 2 x ) + y2 σ y 2 +2πjWx and the Fourier transform, that it s used for generating the Gabor filters, is given by where G(x, y) =e [ 1 2 (u W ) 2 σ 2 u ] + v2 σ v 2 σ u = 1 2 πσ xandσ v = 1 2 πσ y A collection of self similar functions called Gabor Wavelets defined by m,n integers g mn (x, y) =a m G(x,y )a>1 x = a m (xcosθ + ysinθ)andy = a m ( xsinθ + ycosθ) 3

4 where θ = n π K, K is the number of orientation and m is a scale factor. For eliminating the redudant information in the filtered images, the different parameters in the above expressions are choosen by ( π σ v = tan 2k a =( U h U l ) 1 S 1 σ u = (a 1)U h (a +1) 2ln2 ) [ U h 2ln σ2 u U h ][ 2ln2 (2ln2)2 σ 2 u U 2 h where K is the number of orientations, S the number of scales, W = U h and m=0,1,...s-1. The filtered images are obtained throught the integral of the convolutionated image with one of the Gabor wavelets ] W mn (x, y) = I(x, y) g mn (x x 1,y y 1)dx 1 dy 1 This produces m n new filtered images or from another point of view m n new features from which a classifier can extract information. 2.2 The Potts model As it will be shown in the next section, Gabor Features made a good classification of the different structures. However there are a few pixels that are misclassified. To try to fix these misclassifications, the use of the Potts Model is proposed. This model includes the local context in the classification in such way that if e.g. one pixel is classified as red skin, but all it is closest neighbours are classified as normal skin, the probability for this pixel also to be normal skin is raised. In the standard form, the Potts Model uses a four-neighbourhood and the number of pixels from this neighbourhood belonging to the different classes are included with some weight β to influence the probabilities. The way that the Potts model gives thess probabilities is given by the next equations P (f ij = K f kl N ij )=c(t )e (U(yij) fij=k)+βnij(k)/t ) U(y ij f ij = K) = 1 2 log(detσ K)+ 1 2 (y ij µ(k)) T Σ 1 K (y ij µ(k)) 4

5 where T(n) can be whatever decreasing function and n ij (K) isthenumber of pixels of the class K in a four neighbour. In this work T(n) has been choosen 3 as T (n) = log(1+n). In the initial state when no information about the neighbourhood of the pixels is available β = 0 and each pixel is assigned the label with the largest probability corresponding a Bayesian classification. As mentioned earlier, one of the problems with the classification of Psoriasis is that the scaling often erroneously is confused with normal skin and therefore in the simple classification appear in areas with white skin. In practice the scaling often appear on red areas, so a good idea might be in some way to enhance the probability for scaling in red areas and lower the probability in all other contexts. The proposed extension to the Potts Model is including the four next closest neighbours (belonging to the eight-neighbourhood but not the four-neighbourhood). For all the classes except for scaling, these extra pixels most likely are of the same class, thus the probability for selecting a given class should be enhanced if those neighbours are of the same class. In case of scaling, these extra pixels enhance the probability for scaling if they are of class red skin, thus enhancing the probability for small scales on a red background. The weights for this extension to the neighbourhood is set to β 2, so they influence half as much as the normal four-neighbourhood. The Potts Model itself is run iteratively, and for each iteration the class of every pixel is chosen randomly according to the probability for each class. By use of simulated annealing, in the beginning the probabilities for each class will contain a great deal of randomness. But as time goes, the probability for the correct class according to both the initial classification and the included neighbourhoods will be high, compared to the probability for any of the other classes. In this way the final classification will be close to the best possible with the used features and the currently used weights of the neighbourhoods. The reason for introducing simulated annealing and randomly choosing class instead of choosing the most probable in each iteration is not to end up in a local minimum, but hopefully find the best global classification. 3 Experiment and results To test the performance of the developed system a real experiment has been conducted in collaboration with doctors in dermatology of the Gentoffte Hospital. The experiment points out to find the different structures in psoriasis skin images for being used in a posterior automatic scoring. For this purpose, skin images of various patients with psoriasis has been taken. Videometerlab equipment 1 has been used to guarantee that the images are taken in a standard way. Moreover some extra precautions as to check the same lightning conditions in the imaging room has been realized

6 Figure 2: The three choosen images to validate the proposed method Figure 3: The Gabor features for the three test images The interesting areas for grading the psoriasis are the scaling and the red areas. For segmenting these two areas another four classes has been considered. The another four classes are brown skin, normal skin, background and other kinds of structures. Other kind of structure groups all the pixels that can not be considered in the five previous classes. The informations needed for the Potts model has been taken from four features extracted from pixels of the differents classes. These four features are the three classic trichromatic values and one feature obtained from of a combination of Gabor filters. The last feature is choosen as the sum of six filtered images obtained filtering the skin image with six Gabor filters of the same size but differents orientations. Due to the interesting fact of testing the performance of the Gabor feature no more variables as variances or means has been included. The number of extracted pattern is For testing the accuracy of the developed method, three images of the pool has been choosen. Figure 2 shows the images that have been selected for evaluating the proposed approach. Figure 3 shows the filtered image with the Gabor filters. It can be notice that the scales are easily recognizable. As it has been commented before, the first step of the model assigns to each pixel the label that gives largest likelihood. In this step the model still does not use spatial context. It is exactly a quadratic discriminant analysis. This preliminary classification can be checked in figure 4 6

7 Figure 4: A preliminary classification of the three images under study Figure 5: Final segmentation of the three test images These images show that with only these four features the classification given by a simple discriminant quadratic classifier is quite good. The main goal of distinguishing between scales and normal skin looks accomplished thanks to the Gabor filters. However there are some pixels mainly in the third images that still are classify as scale when they are normal skin. For fixing these missclassifications some iterations of the Potts model are realized as it has been described in the previous section. The final classification is showed in Figure 5. It can be noticed that most of the missclassification has been eliminated. This final classification of the different structures looks suitable for making an automatic classification of the redness and the scaling. 4 Summary The obtained results shows the godness of the developed method for segmentation of the interesting areas in skin image with psoriasis. The results exhibit that these interesting areas, red and scaling, are segmented in a suitable way for a posterior analysis. With the objetive of analyzing the behaviour of the proposed method only four features has been used.this suggests the possibility of improving the results even more with the inclusion of new features as variances or means. Moreover another kind of classifier as a neural network or decision tree for the first step in the Potts model could be improve the results too. The results point out to solve the first step to free the evaluation of skin images of 7

8 the subjective human opinion. 5 Acknowledgement We want to express our gratitude to the doctors of the Gentoffte Hospital Lone Skov and Bo Bang without their help this work would not have been possible. References [1] B.S. Manjunath and W.Y.Ma (1996). Texture Features for browsing and retrieval of Image Data. IEEE Transactions on pattern analysis and machine intelligence, vol 18, no. 8 August [2] Stan Z. LI (1). Markov Random Field Modelling in Image Analysis. chapters 1 and 2.Computer Science Workbench [3] Jens Michael Carstensens (1992) PhD thesis pages [4] Knut Conradsen(1) An introduction to Statistics Volume 2 Chapters 7 and 8 [5] Fausset L.(1994) Fundamentals of neural networks. Prentince Hall [6] Jorgen Folm-Hansen Ph.D. thesis Lyngby 8