Automatic Skin Lesion Segmentation based onTexture Analysis and Supervised Learning

Yingding He and Fengying Xie∗

School of Astronautics, Beihang University, Beijing 100191, China

Abstract. Automatic skin lesion detection is a key step in computer-aided diagnosis (CAD) of Skin cancers, since the accuracy of the subse-quent steps in CAD crucially depends on it. In this paper, a novel methodof automatic skin lesion segmentation based on texture analysis and su-pervised learning is proposed. It firstly involve the clustering of trainingimage into homogeneous regions using Mean-shift; then fusion texturefeature are extracted from each clustered region based on Gabor andGLCM feature; next, the classifier model is generated through supervisedlearning base on LIBSVM; finally, lesion regions of the unseen image areautomatically predicted out by produced classifier. Comprehensive ex-periments have been performed on a dataset of 125 dermoscopy images.The proposed method is compared with three state-of-the-art methodsand results demonstrate that the presented method achieves both robustand accurate lesion segmentation in dermoscopy images.

1 Introduction

Melanoma is a cancer of pigment producing cells called melanocytesk, mostmelanomas originate from the skin. It is the seventh most common malignancyin women, the sixth most common in men and its incidence rates are increasingfaster than any other cancer [1]. For the past four decades, malignant melanomahas steadily increased its burden on health care in the whole world, especiallyin western countries [2]. Since therapy for advanced melanoma is poor [3], earlydiagnosis is particularly important because most tumors can be cured with asimple excision if detected early. In an attempt to reduce this burden, therehas recently been a considerable amount of research on automatic skin lesiondiagnosis (ASLD) from dermoscopy images [4]. Dermoscopy is a non-invasiveskin imaging technique which makes subsurface structures more easily visible.However, studies have shown that dermoscopy may actually lower the diag-nostic accuracy by inexperienced dermatologists. Therefore, researchers adoptcomputer-aided diagnosis (CAD) in ASLD in order to proper interpretation ofdermoscopy images. The complete skin cancer CAD procedures typically in-clude 5 steps: Image Acquisition, Pre-processing, Lesion Segmentation, Featureextraction and Recognition. Since the accuracy of the subsequent steps in CADdepends on lesion segmentation, it is crucial in CAD procedures. Although re-searchers have invested considerable effort to addressing this problem in the pasttwo decades, it is still not successfully resolved under ”challenging” conditions

2 Yingding He and Fengying Xie∗

such as: 1) Bubble, hair and other obstacles in the field of vision; 2) Irregularand fuzzy lesion border; 3) Low contrast between lesion and surrounding skin; 4)Smooth transition area between the lesion and skin; 5) Multi-fragment lesions orvariegated coloring inside the lesion. Unfortunately, most malignant melanomaclinical manifestations of a challenging condition. Obviously, human ability todistinguish lesion area between healthy skin in such cases leading computer a lot.Therefore, we utilize computer to simulate the human visual system, learningthe diagnosis experience from dermatologist, to solve this accurate segmentationproblem.

Paper is organized as follows: In Section 2, we review previous work andproposed our idea of segmentation. Section 3 describes the scheme of completemethod. In Section 4, we present experimental result and comprehensive assess-ment. Finally, conclusion in Section 5.

2 Related Work

In recent years, numerous methods have been developed to settle lesion segmen-tation task, most automatic segmentation methods can be mainly categorizedas 3 class: Histogram Thresholding [5–7], Clustering [8, 9] and Region Mergence[10]. Apart from these, a few segmentation methods applying supervised learn-ing to segmenting lesion in challenging conditions [4, 11]. Sometimes, segmentingwork encounter some extremely difficulties that automatic method can not han-dle with, people resort to interactive strategy in order to ensure the accuracy ofsegmentation [12].

A popular class of approaches to skin lesion segmentation is HistogramThresholding [5–7], which has been shown to be effective for situations wherethe lesions have consistent characteristics and the surrounding skin regions arehomogeneous in nature, as well as handling with situations characterized bymultiple regions. However, such approaches face difficulties in situations charac-terized by structural, illumination, and color variations, where no clear thresholdcan be found that separates the lesion regions from the surrounding skin regions,and resulting in poor segmentation accuracy [13].

Clustering approaches are based on the idea of clustering pixels with similarcharacteristics. It firstly involve the partitioning of a color (feature) space intohomogeneous regions(the subset) using unsupervised clustering algorithms, thenfinds the subset of clusters that minimizes an objective function (clustering cri-teria) which measures the distance between clusters [8, 9]. However, clusteringalgorithm may fail in the presence of occlusions (artifacts), smooth transitionarea between lesion and skin, and multi-fragment lesions or variegated coloringinside the lesion.

The recently developed method Statistical Region Merging (SRM) is a colorimage segmentation technique based on region growing and merging. The methodmodels segmentation as an inference problem, in which the image is treated asan observed instance of an unknown theoretical image, whose statistical (true)regions are to be reconstructed. The advantages of this method include sim-

Title Suppressed Due to Excessive Length 3

plicity, computational efficiency, and excellent performance without the use ofquantization or color space transformations [10]. Such an approach has diffi-culties dealing with images characterized by noise, artifacts, structural, colorvariations, multiple lesions, and weak boundary separation.

As mentioned above, most lesion segmentation method suffering from chal-lenging conditions more or less. Several segmentation scheme combine variety ofmethods and achieve limited improvement, however, the combination also leadto more parameters which are supposed to be tuned once for a given application.Unfortunately, almost each dermoscopy image has required different parametervalues, rendering the process tedious [6]. In medical image segmentation thereare often situations when automatic segmentation techniques fail or lead to asuboptimal solution. As a consequence an expert has to correct results in a man-ual way. Providing the expert with a tool that supports manual segmentationwhile speeds up this tedious process, gives immediate feedback, and makes theresults repeatable, is a far better choice in such a case. For interactive segmen-tation an effective strategy is to exploit the synergy between a human operatorwho is superior in object recognition and an algorithm which is better in exactobject delineation [12].

From the perspective of cognitive science, interactive segmentation utilizeda form of decomposition of multivariate information [14], and the subjectiveempirical information may be able to be extracted, mainly from human visualinformation, and adopt by computer. Inspired by this viewpoint, we considerapplying texture analysis approaches to representing the visual information andutilize supervised learning method to introduce the subjective empirical infor-mation in automatic segmentation.

3 Segmentation Method

In this section, the proposed method is described as two stages: training stageand segmenting stage. In the training stage, an experienced dermatologist de-lineate out the lesion accurate contour manually as the ground truth of lesionsegmentation. Then lesion and healthy skin part is labeled based on groundtruth, clustering techniques is employed to divide this two part into a series ofhomogenous regions respectively. Next, the labeled fusion feature is extractedfrom each region to represent the visual information of lesion area. At the lastphase of training stage, classifier model is generated through supervised learningin LIBSVM. In the segmentation stage, unseen images is implemented same clus-tering, feature extraction and classification step, and the final result is delineatedautomatically.

3.1 Manually Delineation

For ensuring the accuracy of ground truth, an experienced dermatologist is in-vited to delineate out the lesion contour. All training images are divided intolesion and healthy skin part through taking the ground truth as mask, this stepalso could be seen as manual labeling.

4 Yingding He and Fengying Xie∗

Ground

Truth

Training

Image

Delineation

SkinLesion

Masking

Fig. 1. Lesion Delineation and Masking Operation.

3.2 Clustering

Dermatologist identify lesion area rely on visual information and experience.Thus, before training computer to recognize lesion area, we have to interpret thevisual patterns into meaningful information. Due to the variation of color tex-ture and shape, the entire lesion area is hardly to describe, we using Mean-Shiftalgorithm [15] clustering decompose the labeled image into a certain number ofhomogeneous regions prior to feature extraction phase. As a result, the inter-pretation tasks are simplified. Next, clustered image is divided into a series ofhomogenous region images.

Lesion Regions

Skin Regions

Target

Non-TargetSkin

Lesion Clustering Dividing

Fig. 2. Labeling and Clustering.

3.3 Feature Extraction and Fusion

As earlier description in this section, in the clustering step labeled images are di-vided into several homogeneous regions. A fusion of feature representation, whichcan effectively discriminate region images belong to lesion or healthy skin, shouldbe investigated. At the feature fusion phase, we eminence to texture feature and

Title Suppressed Due to Excessive Length 5

carry out a genetic algorithm based fusion feature optimization approach in orderto achieve better fusion scheme.

Gabor Texture Feature Since frequency and orientation representations ofGabor filters are similar to those of the human visual system, and they havebeen found to be particularly appropriate for texture representation and dis-crimination, we employed Gabor feature to representing the region images. Inthe spatial domain, a 2D Gabor filter is a Gaussian kernel function modulatedby a sinusoidal plane wave and can be written as:

g(x, y) =

(1

2πσxσy

)exp[−1

2

(x2

σ2x

+y2

σ2y

)+ 2πjWx] (1)

A complete Gabor filter dictionary design scheme and feature extractionprocess has fully described in [16]. We set the same parameters UH = 0.4,UL = 0.05, orientation=6, and scale=4 which used in [16]. Given an imageI(x, y), its Gabor wavelet transform is then defined to be:

Wmn(x, y) =

∫I(x1, y1)gmn ∗ (x− x1, y − y1)dx1dy1 (2)

It is assumed that the local texture regions are spatially homogeneous, meanvalue µmn and standard deviation σmn of the magnitude of the transform coef-ficients are used to represent the region for classification purposes.

A feature vector is now constructed using mean value and variance as featurecomponents. In the experiments, we use 4 scales and 6 orientations, resulting ina 48-dimention (6*4*2) feature vector:

fGabor = {µ11, ...µ64;σ11, ..., σ64} (3)

Grey Level Co-occurrence Matrix The grey level co-occurrence matrix(GLCM) method proposed by Haralick et al. [17] is a common method for tex-ture feature extraction. In David A.Clausi and Huang Deng’s research [18], theyindicate in the presence of point noise, Gabor filters are able to generate con-sistent measurements in low and medium frequencies but generate inconsistentmeasurements for higher frequencies. The higher the frequency of a Gabor filter,the more sensitive the filter is to the point noise. This is due to the higher fre-quency filters having larger spatial-frequency bandwidth which covers relativelymore energy of the impulsive noise that is evenly distributed in the spatial-frequency domain. High-frequency Gabor filters created from a pseudo-waveletfilter bank implementation has very short duration with only a few significantweights. Such weights are more noise susceptible than the low and mid-frequencyfilters. Therefore, to replace the high-frequency Gabor filter features with GLCMfeatures is appropriate:

Pij = Pr(i, j|δ, θ) (4)

Since only high-frequency information should be represented, GLCM param-eters select as follow: the inter-pixel distance δ is set to 1 and 2, orientation

6 Yingding He and Fengying Xie∗

θ is set to 4(0◦,45◦,90◦,135◦), then selected 3 statistics of GLCM are contrast,entropy, and correlation.

contrast =N−1∑i,j=0

Pi,j(i− j)2 (5)

Entropy =

N−1∑i,j=0

Pi,j(−lnPi,j) (6)

correlation =N−1∑i,j=0

Pi,j [(i− µi)(j − µj)√

(σ2i )(σ

2j )

] (7)

The GLCM feature represented as a 24-dimention (3 · 2 · 4) vector:

fGLCM = {contrast1,0◦ , entropy1,0◦ , correlation1,0◦ , ...,

contrast2,135◦ , entropy2,135◦ , correlation2,135◦} (8)

Gray Value and Region Contrast Gray-value is regular and significant vi-sual information, we simply transform color image into a monochrome image byempowering corresponding coefficient to R/G/B channel(Y = 0.30 R + 0.59 G+ 0.11 B). And we also calculate region/entire mean ratio as region-ratio featureto improve effeteness of gray value feature in low contrast condition:

fGray = {gray, region− ratio} (9)

3.4 SVM Classifier Model

At the last phase of training stage. The well fused 74-dimention feature set isproposed to represent attributes of each region. Following task is to produce amodel (based on these training data) which can predicts the category of eachregion in the unseen image.

SVM Classifier Classifier performance depends greatly on the characteristicsof the data to be classified. There is no single classifier that works best on all givenproblems. Various empirical tests have been performed to compare classifierperformance and to find the characteristics of data that determine classifierperformance. Determining a suitable classifier for a given problem is howeverstill more an art than a science. In our experiment, LIBSVM [19] integratedsoftware (version 3.1) is employed for supervised learning and classification. SVM(Support Vector Machines) are a useful technique for data classification. Givena training set of instance-label pairs (xi,yi), i = 1, . . . , l where xi ∈ Rn andyi ∈ {−1, 1}l, the support vector machines (SVM) require the solution of thefollowing optimization problem [20]:

minw,b,ξ

1

2wTw + C

i∑i=1

ξi (10)

Title Suppressed Due to Excessive Length 7

Subject to:yi(w

Tϕ(xi) + b) ≥ 1− ξi, ξi ≥ 0 (11)

Here training vectors are mapped into a higher (maybe infinite) dimensionalspace by the function ϕ. SVM finds a linear separating hyperplane with themaximal margin in this higher dimensional space. C > 0 is the penalty parameterof the error term. We have adopted the recommended kernel in Guide to SupportVector Classification [19] to produce classifier model.

KRBF (xi, xj) = exp(−γ∥xi − xj∥2), γ > 0 (12)

Parameters Setting There are two parameters for an RBF kernel: C andγ, Grid Search is used to determining parameter value according to the clas-sification accuracy of ten-folder Cross-Validation in LIBSVM. This parametercalibration method includes loose and fine grid search steps, the prior find asmall target interval and the latter determine the final value [19]. As shown inTable 1., through the grid search, parameters are chosen as γ=1 and C=12500when single Gabor feature classification accuracy achieved 88.47%.

Fig. 3. Contour map of Loose Grid Search.

Data Optimization Finally, extracted feature should be transformed into theformat of an LIBSVM package, and we conduct a simple scaling on each com-ponent (Gabor, GLCM, Gray Value and Region Contrast) of fusion data tonormalization.

Typically, previous steps of data processing are enough access to qualifieddata. But taking into account the role played by each component under dif-ferent situations is slightly different, it makes sense to weighting componentrespectively. Consequently, we utilize genetic algorithm (GA) [21] to optimizethe weight coefficients. The classification accuracy of weighted fusion feature istaken as objective value, output also produced by ten-folder Cross-Validation inLIBSVM and effeteness of GA optimization is verified in followed experiment.

8 Yingding He and Fengying Xie∗

3.5 Segmentation stage

In segmentation stage, similar procedure which described previously in this sec-tion are implemented to processing unseen images, include: Mean-Shift cluster-ing, region extraction, feature extraction, classification, target region mergingand boundary delineation. In order to avoid unnecessary elaboration, the entiresegmenting process described in the Fig. 4.

Mean-

Shift

Unseen Image Cluetering

Lesion Healthy

Skin

Region

Extraction

Feature

Extraction

SVM

Classifier

Lesion Contour

Region

Feature

SetRegion

Images

Target

Labeled by classifier

Non-TargetRegion

Merge

Add-

Boundary

Segmentation

Result

Fig. 4. Segmentation Process.

4 Experimental Result and Analysis

The experimental dataset of 125 Malignant Melanoma dermoscopy images weredownload in website https://dermoscopy.k.hosei.ac.jp/DermoPerl/ whichestablished by M.E.Celebi and H.Iyatomi et al. Ground truth are delineated byexperienced dermatologists. Feature dataset using the 74-D vectors fusion featureset that extracted from 2021 regions which are clustered from 125 MalignantMelanoma dermoscopy images.

Experimental result and analysis include three parts: feature selection andoptimization; qualitative comparison of segmentation results; quantitative as-sessment.

4.1 Feature Selection and Optimization

In order to validate the rationality of feature fusion scheme and GA optimizedmethod, verification result are listed in followed table. Classification accuracyperformed on 7 different single and fusion feature were listed in Table 1., can beclearly seen, only Gabor feature obtain acceptable classification accuracy in the3 single features. Among fusion feature, the combination of all 3 features achievesthe highest precision. Should be noted that ”Dimensionality Curse” is already

Title Suppressed Due to Excessive Length 9

demonstrated not to affect classification (segmentation) using the proposed 74-D fusion feature, corresponding verification experiments are well presented inpaper [18]. It is worth mentioning that, weight distribution optimized by GAalgorithm lead to significant progress in accuracy.

Table 1. Classification Accuracy of Feature

Feature Accuracy (%)

Gray (2D) 69.251GLCM (24D) 82.385Gabor (48D) 88.372Gabor+Gray 89.856Gabor+GLCM 90.945Gabor+GLCM+Gray 91.242Optimized by GA 93.765

4.2 Segmentation in Challenging Conditions

As most image processing problem, there are two major evaluation methods: sub-jective and objective (or qualitative and quantitative). At this part, 6 samples ofchallenging dermoscopy images are selected for intuitive display of segmentationresults. Besides ground truth, three state-of-the-art segmentation methods andproposed method are performed on each sample.

For comparison purpose, Histogram Thresholding [5], FCM [9] and SRM [10]segmentation method were employed to segment the same dermoscopy imagedataset. It should be noted that, the parameters of the Histogram Thresholding,FCM and SRMmethods have been set based on that presented in their respectiveliteratures. All methods, besides the proposed method using morphological closeoperation smoothing segmented lesion boundary, are performed fully automaticsegmentation without manual intervention or pre (post) processing step.

Can be clearly seen, in Fig. 5.(a), the presence of bubble lead the comparisonmethod to reproduce the edge of bubble rather than lesion contour while theproposed method exactly delineate out the lesion contour. In Fig. 5.(b) FCMand proposed method successfully segmented entire border irregular lesion area,and other segmentation results seems smaller than actual lesion area. Similarresults also appeared in Fig. 5.(c), in the case of fuzzy border condition, FCMmethod appear not much better than Thresholding and SRM method, howeverthe proposed method still achieve good result. Appearance of multi-fragmentlesion always lead to under-segmentation result (part of the actual lesions arenot included), in Fig. 5.(d), both SRM and proposed method well handled thistask. What is more, the proposed method delineated a more accurate boundaryin the two correct segmentation results while SRM is slightly over-segmented(delineate healthy skin part as lesion area).

10 Yingding He and Fengying Xie∗

(b) Irregular Border and Low Contrast

Challenging

Conditions

Histogram

ThresholdingFCM SRM Proposed

(a) Bubble

(c) Fuzzy Border and Smooth Transition Area

(d) Multi-Fragment area

Ground

Truth

Fig. 5. (a)-(d) Segmentation results in variety of challenging conditions.

4.3 Quantitative Assessment of Segmentation

In this stage, to objectively evaluate the effectiveness of the proposed method, weadopted 3 quantitative assessment metrics are based on the concepts of true/falseand positive/negative given in Table 2.

Table 2. Definitions of true/false positive/negative

Pixel of Pixel of automatic segmentationGround Truth Lesion Healthy SkinLesion True Positive False NegativeHealthy Skin False Positive True Negative

Assessment metrics XOR (segmentation error), Sensitivity and Specificityare defined as followed:

XOR =FP + FN

TP + FN(13)

Sensitivity =TP

TP + FN(14)

Specificity =TN

TN + FP(15)

Title Suppressed Due to Excessive Length 11

Table 3. Quantitative assessment metrics with MSE

Metrics(%) XOR Sensitivity Specificity

Proposed 15.47(10.6) 92.61(8.3) 93.93(7.9)SRM 18.26(15.4) 87.9(15.0) 94.19(10)FCM 19.9(13.4) 81.27(13.9) 98.5(3.1)Threshold 21.06(13.9) 79.71(14.2) 99.27(1.7)

The segmentation performance metrics on 125 dermoscopy images (for SRMmethod produce 3 failed segmented result, just calculated 122 images) are shownin Table 3. Firstly, the proposed method achieved the lowest XOR value 15.47%,which indicates that the proposed method has the strongest overall performancewhen compared to the SRM, FCM, and Thresholding method. In addition theminimum MSE value of XOR also reflects the robustness of proposed method.Secondly, Sensitivity metric provides the indication of under-segmentation errorwhile Specificity provides an indication of over-segmentation error, proposedmethod achieved 92.61% and 93.93% respectively.

5 Conclusion

In this paper, a novel texture feature and supervised learning based lesion seg-mentation method is presented. Since the clustering step and region textureanalysis are employed, the SVM classifier can mimic human visual system andlearning to recognize lesion area based on the real skin lesion has delineated bydermatologist. Which means proposed method can be applied to variety types ofskin or disease through training images without parameter optimization process,and more accurate segmentation result will be achieved as the result of increasein disease cases.

In both qualitative and quantitative experimental analysis, our method yieldspromising results compared to three state-of-art methods. However, improve-ment still could be achieved with further investigation in clustering method,feature fusion scheme and classifier configuration. Besides Mean-Shift, Water-shed and other segmentation or clustering algorithms also seems to be powerful.Feature representation of human visual information has long been the focus ofresearcher’s attention, our team will follow closely progress in this field. Further-more, recently developed classifier and multi-classifier also could be applied insegmentation task.

Acknowledgement. This work was supported by the National Natural Sci-ence Foundation of China (Grant nos. 61071138 and 61027004) and the ScienceFoundation of Beihang University (Grant no. YWF-12- LXGY-013). The au-thors thank Dr.Rusong Meng of the General Hospital of the Air Force of PLAat Beijing for providing the dermoscopy images.

12 Yingding He and Fengying Xie∗

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