Skin Texture Analysis Using Morphological Dilation and...

Skin Texture Analysis UsingMorphological Dilation and Erosion

S. Sree Hari Raju1 and E. G. Rajan2

1Department of Computer Science,University of Mysore, Karnataka, India.

[email protected] Avatar MedVision US. LLC, USA,

Pentagram Labs Inc, California, USA,Helios & Matheson Analytics, USA

[email protected]

February 5, 2018

Abstract

Skin is one of the complex 3D tessellations. Therefore itis non-trivial to model skin textures of humans. The ratio-nale behind this is that the skin images may be collectedfrom different lighting conditions and other aspects thatdifferent same skin when captured. Morphological filter-ing process includes plethora of operations. Two importantoperations are dilation and erosion. In this paper we pro-posed a methodology to classify skin texture images. It isa supervised learning process containing both training andtesting phases. The training phase takes input skin textureimages and subjects them to erosion and dilation in orderto have transformed images. With the transformed imagesa classifier is built. The classifier has a resultant model thatis used for classification of testing set. In the classificationphase, the unknown skin texture images given as input aresubjected to morphological filtering. The resultant trans-formed images are compared with the help of classifier and

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International Journal of Pure and Applied MathematicsVolume 118 No. 19 2018, 205-223ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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the results of training phase. Thus the unknown skin tex-ture images are classified. MATLAB application is builtto demonstrate proof of the concept. The experimental re-sults revealed that the classification accuracy of the pro-posed methodology is increased when the size of trainingset is increased.

Key Words : Morphological filters, dilation, erosion,skin texture analysis.

1 INTRODUCTION

Skin is one of the complex parts of human body. Modelling it isvery challenging for different reasons. Reflection of light and inter-reflection of light have their impact on the skin while capturingit. Skin exhibits optical properties that are complex in nature. Itinvolves pores and wrinkles with different surface microgeometrybesides having different optical properties of layers of skin. Likeany other surface in the real world, skin is also affected by thedirection of light which illuminates it. This fact is illustrated inFigure 1 where lip part of human face is considered with threedifferent directions of light for illumination. The texture of lippart appears differently in each image due to direction of incidentillumination. For human eye, the images look differently thoughsame lip surface is captured with different directions in illuminatingthe surface while capturing the images.

Figure 1: Human face at lip region captured with different direc-tions of illumination source

As there is difference in appearance, it is understood that skinis very complex landscape in the human body which is difficult tomodel. An important observation is that the texture of skin is a

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3D texture. It does mean that the fine scale geometry of the skintexture affects overall appearance of the skin. Many researcherscontributed in modelling skin for different applications. Bidirec-tional texture function (BTF) is introduced in [27]. The term BTFis best used to describe texture of different imaging angles basedon the direction of light source. BTF is similar to BRDF stands forbidirectional reflectance distribution function. As a matter of factsimple models for skin texture are inadequate to satisfy demandsof real world algorithms in computer vision. For instance the ap-plications like facial feature tracking, face recognition, estimationof shape etc. need accurate prediction of appearance. A method-ology that can capture local variations in occlusions, shadowingand foreshortening is essential as the textures fine scale geometryshows skin surface differently against directions of lighting source.Rendering skin surface accurately is still an open research topic.Moreover accurate skin models can help in many industries includ-ing dermatology. With respect to dermatology, it is important todevelop methods for automatic, computer-aided diagnosis of skindisorders. In the same fashion, in the pharmaceutical industry thehealing process can be measured to compare different treatmentstaken from different healthcare service providers.

Skin modelling also helps cosmetic and consumer product in-dustries to provide skin illustrations to support their claims.

In this paper we proposed a method that is for skin texture anal-ysis using morphological filters such as dilation and erosion. Themotivation behind this is that morphological filters are efficient incapturing details of skin and there is scope for further research inhuman skin analysis. The method shows how human skin of differ-ent parts can be modelled and analyzed in order to support differentapplications in the real world. The remainder of the paper is struc-tured as follows. Section II provides review of literature. SectionIII presents details of morphological filtering. Section IV providesdetails of dataset used for experiments. Section V presents theproposed system in detail. Section VI shows experimental resultswhile section VII concludes the paper besides providing directionsfor future work.

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2 RELATED WORKS

This section provides review of literature on skin texture classifi-cation. Skin lesions are subjected to segmentation in [1] for jointstatistical texture distinctiveness. Detection of facial wrinkles isthe main focus in [2] with image morphology and geographic con-straints. A framework is built in [3] for morphological filtering ofbrain MRI images. Automatic human identification and scale andtexture analysis were made in [4] and [5] respectively. Both morpho-logical features and perspiration are combined in [6] for liveliness offinger print detection. Finger images for human identification andthree dimensional solid texture analyses are carried out in [7] and[8].

Neural network based approach for fruit classification coupledwith computer vision is explored in [9]. Usage of directional filtersis explored for detection of pigment network in [10]. There areimage processing based approaches found in [11]-15]. There is goodsurvey of gesture recognition methods in [16]. Classification andquantification of pigmented skin lesions is studied in [17]. Diagnosisof type 2 diabetes and automatic skin lesion analysis are exploredin [18] and [19] respectively. Other approaches are found in [20]-[25] but there is less information on the presence of dilation anderosion related to morphological filtering. In this paper we proposeda methodology for morphological filtering of skin textures for thepurpose of classification.

3 MORPHOLOGICAL FILTERING

Morphological filtering is one of the image processing techniques.It involves in a set of non-linear operations that are pertaining toshape or morphology of different features of given input image. Thefeatures include skeletons, boundaries and so on. There are manytechniques related to morphological filtering. In every techniquethere is a common thread which is nothing but probing an inputimage with structuring element. Structuring element is a templateor small shape which can define a neighbourhood or region of in-terest (ROI) around a pixel in the image. There are different mor-phological operations such as erosion, dilation, opening, closing,white tophat, black tophat, skeletonise and convex hull. Erosion

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is an operation that sets a pixel given location such as (i,j) to theminimum when compared with all other pixels in the neighbour-hood. A structuring element is computed and used in the erosionprocess. The structuring element is nothing but a Boolean arraywhich represents neighbourhood. The dilation on the other hand isanother morphological activity which sets a given pixel at position(i,j) to the maximum when compared with all other pixels in theneighbourhood. Dilation process shrinks dark regions and enlargesbright regions.

Opening is the process in which erosion takes place followed bydilation. It is meant for removing small regions containing brightspots or salt in other words. It also helps in connecting small darkcracks. Closing is a morphological operation which is nothing butthe dilation followed by erosion. It is used to remove small darkspots or pepper. It is also used to connect small bright cracks.White tophat operation is nothing but the result of an image whenmorphological opening is carried out. In the same fashion, blacktophat is nothing but morphological closing operation minus thegiven original input image. It is meant for returning dark spots ofgiven image which is smaller when compared to structuring element.Skeletonize is the process of thinning applied to a binary image toreduce each connected component. This operation is performed onbinary images only. Convex hull is representation of set of pixelsthat are part of a smallest convex polygon which are around thewhite pixels in an image. It is also done with binary images.

4 MORPHOLOGICAL DILATION AND

EROSION

Morphology denotes large set of operations in image processing. Itfocuses on processing images based on shapes. All morphologicalmethods make use of structuring element to an input image andcreate output image of the equivalent size. In any morphologicaloperation, each pixel of an input image is in comparison with theneighbouring pixels. The size of the shape of neighbourhood needsto be chosen to construct as morphological operations are sensitiveto shapes of given input images. Our methodology in this paperis based on dilation and erosion operations of morphological filters.

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Therefore, it is useful to understand these two operations in somedetail. They are most basic operations in morphology. Dilationresults in adding pixels to boundaries of objects present in an in-put image while the erosion does in reverse process. The numberof pixels subjected to adding or removing depends on structuringelement’s size. With respect to the two morphological operations,each pixel is subjected to a rule with respect to its neighbours inthe input image. The rule for dilation is that the output pixel valueis maximum of all the pixels in the neighbourhood. It does meanthat if any pixel is set to 1, the value 1 is set to output pixel. Inthe same fashion, the rule followed by erosion is that the outputpixel should have minimum value corresponding to its neighbour-hood pixels. Therefore, it any pixel has value 0, then 0 is set to theoutput pixel.

Figure 2: Illustrates dilation on binary image

Figure 2 illustrations dilation process in a binary image. Thestructuring element makes use of a pixel of interest (shown in circle).The dilation rule is applied on the input image with respect to pixelsand produce output pixels as per the rule. The output pixel valueis set to 1 according to the rule with respect to the illustration inFigure 2. Dilation can be done on gray scale images well.

Figure 3: Illustrates morphological dilation on gray scale image

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As shown in Figure 3, it is evident that the input image andoutput image are presented with values. It illustrates processingof a single pixel in given image. It makes use of highest value rulewhile processing the image. A set of values shown in the outputimage that are maximum values in the neighbourhood of given pixelare considered in the input image. With respect to processing pixelsat borders of an image padding behaviour is exhibited by dilationand erosion. The origin of structuring element is based on thepixel of interest for processing in the input image. With respectto pixels in the border of image, the neighbourhood of structuringelement extends past the border. Therefore, the pixels which areoutside the border and undefined in nature are assigned a valueby morphological filters. Such pixels are known a padding pixelsand their value differs in dilation and erosion. The padding rules forboth dilation and erosion are different. Dilation assigns a minimumvalue afforded by data type to padding pixels. For binary imagesthe value is set to 0 while for greyscale images the minimum valuefor uint8 image is 0. In case of erosion max value is assigned tothe padding pixels. In case of binary image the value is 1 and forgreyscale images max value that is 255 is assigned to uint8 images.The result of dilation is illustrated in Figure 4.

Figure 4: Result of dilation

As shown in Figure 4, it is evident that the white boundaryof input image thickens or dilated in other words, when size ofthe disk is increased. Another observation is that the size of twoblack ellipses is reduced in the middle while the light grey circle isthickened.

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Figure 5: Result of erosion

As per the rules of the erosion, the morphological operation iscarried out and the white boundary of the input image disappearedup on the increase of size of the disk. The size of the two ellipsesis increased in the output image and the three white spots disap-peared.

5 APPLICATIONS OF SKIN TEXTURE

ANALYSIS

Analysing skin textures and classification of the same have widerange of applications in multiple disciplines. For instance, skin tex-ture can be used as biometric in the form of face recognition invarious security applications. Skin appearance and modelling it ac-curately helps in biomedical evaluation and medical treatments. Itcan also be used to evaluate the effectiveness of treatments givento patients besides measuring progress from previous treatments.Skin texture analysis is especially useful in the computer aided di-agnosis in healthcare units of dermatology. Thus it is a potentialapplication in dermatology as dermatologist can use representationsof textures for preliminary diagnosis with respect to lesions, molesetc. It can be used in data mining applications as well. It can pro-vide representative image surface that can be used to match withtextures in databases. This kind matching is useful in e-commerceapplications also as customers can find materials from product im-agery. Texture analysis can also help in making advanced water-marking where fine scale skin texture can be used to identify objectsuniquely. With respect to military applications, camouflage designs

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can be improved further with different illumination angles and stillbe able to recognise textures accurately.

6 FACE TEXTURE DATABASE

Face texture database is captured from [27]. The database has2496 texture images related to human faces. They are related to20 humans with different locations such as nose, chin, cheek andforehead. They reflect samples exposed to image angles with 32combinations. They are related to 13 male and 7 female subjects.The texture images reflect different regions of face as mentionedearlier for all human subjects. There is visual difference across dif-ferent textures from subject to subject. The face texture databasealso exhibits feature like moles, wrinkles, freckles, and pores thatdifferent among the subjects in terms of shape and size thus makingthe modelling of skin very difficult task.

Moreover the appearance of skin differs due to factors like gen-der, and age of sample subjects. There is more complexity addedto the samples with variations in imaging parameters. Oblique di-rections of light and frontal illumination made them more complexas the former makes surface highly visible while latter shows bettercolour variation.

Figure 6: Same skin surface appearing differently

As shown in Figure 6, it is evident that the left image is resultof frontal illumination while the right image is the result of obliquedirections of light. Camera pose also can give rise to different effectslike appearance, rotation, foreshortening, and occlusion.

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Figure 7: Illustrates the three classes of locations on hand

As shown in Figure 7, it is evident that there are three locationsidentified on hand. And they are classified into class 1, class 2 andclass 3. This classification can help in using strategies to classifyskin texture.

Figure 8: Skin texture images corresponding to class 1(a), class2(b) and class 3 (c)

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As presented in Figure 8, there are three skin texture imagesthat correspond to class 1, class 2 and class 3.

Figure 9: Shows different face locations that can have texture dif-ferently in face texture database

As presented in Figure 9, it is evident that there are differentface locations that can have different texture. The textures of themare available in face texture database.

7 PROPOSED METHODOLOGY

Our methodology for skin texture classification is illustrated in Fig-ure 10. The methodology has two phases namely training phaseand testing phase. In the training phase, a set of unknown skintexture images are considered for input. Then the input images aresubjected to morphological filtering that comprises of erosion anddilation. With these morphological operations, the input imagesare transformed and a model is built. The resultant classifier isfurther used in the classification phase.

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Figure 10: Illustrates training and classification phases

Both phases do have morphological filtering process. There-fore, the dilation and erosion related information is provided here.Both operations are made in linear fashion. The emphasis is onthe transformations in the form of erosion and dilation. Let τ isthe complete lattice. Transformations are made with dilation anderosion processes.

δ(V Xi) = V δ(Xi), ε(∧Xi) = ∧ε(Xi), Xi ∈ τi (1)

The classes on the erosions and dilations on τ are nothing buttwo isomorphic lattices that contain duality relation and correspondto each other.

ε(X) ≤ Y ⇔ X ≤ ε(Y ), X, Y ∈ τi (2)

For each dilation, there is corresponding unique erosion.

ε(X) = V {B ε T, δ(B) ≤ X} (3)

In the same fashion, for each erosion there is corresponding and

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unique dilation.

δ(X) = ∧{B ∈ T, ε(B) ≥ X} (4)

In addition to have such mappings, the dilations and erosionsare capable of producing two comprehensive classes of increasingmappings. Towards that it is formulated like this. Any mappingsuch as ϕ : τ → τ in such a way that ϕ(E) = E is increasing whenit can be written as in Eq. 5.

ϕ = ∨{εB, B ∈ T} (5)

And with the erosions given by and dual result for the dilation:

εB(X) =

{ϕ(B)&if X ≥ B,

φ&otherwise(6)

These dilation and erosion operations are common for both thephases of the proposed methodology. When it comes to classifi-cation, the given unknown skin texture images are subjected tomorphological transformations. Then the transformations are sub-jected to matching. The matching process needs the help of clas-sifier and the classified images produced by the training phase areused. Once classification is performed, the classifier model is up-dated besides the database containing skin texture images.

8 EXPERIMENTAL RESULTS

We built a prototype application using MATLAB to demonstrateproof of the concept. Experiments are made a PC with Intel coreCPU @ 1.70GHz speed and 4 GB RAM. The results are evaluated interms of classification accuracy against training set size. It is foundthat the size of training set has its influence on the classificationaccuracy.

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Figure 11: Classification accuracy vs. size of training set

As presented in Figure 11, it is evident that the size of trainingset is shown in horizontal axis. The sizes used for experimentsinclude 50, 100, 150, 200, 250, 300 and 350. The classificationaccuracy in terms of percentage is provided in vertical axis. Theresults reveal the relationship between classification accuracy andsize of training set.

9 CONCLUSION AND FUTUREWORK

In this paper, we studied morphological filtering operations suchas dilation and erosion for transforming images. Our focus is onthe skin texture images as there are plenty of real world applica-tions that are based on skin texture classification. We proposed amethodology containing both training and testing phases. In thetraining phase, a set of unknown skin texture images are taken andsubjected to morphological filtering. Then the resultant classifier isused for testing new images. In the testing phase the given testingskin texture images are transformed using morphological filteringapproach and the help of classifier is taken to classify them. Theaccuracy of classification is measured and presented in the experi-mental results. The results revealed that the classification accuracyis influenced by the size of training set. In future we take more mea-

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sures to evaluate the proposed methodology and improve it furtherto make it robust and adapt it to specific applications.

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