Aldridge, Ben

Ballerini, Lucia

Bevilacqua, Vitoantonio

Di Leo, Carlo

Fisher, Robert

Rees, Jonathan

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      Hierarchical Classification of Ten Skin Lesion ClassesCitation for published version:Di Leo, C, Bevilacqua, V, Ballerini, L, Fisher, R, Aldridge, B & Rees, J 2015, Hierarchical Classification ofTen Skin Lesion Classes. in Proc. SICSA Dundee Medical Image Analysis Workshop.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Proc. SICSA Dundee Medical Image Analysis WorkshopGeneral rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Hierarchical Classification of Ten Skin Lesion ClassesCarlo Di Leo, Vitoantonio Bevilacqua Department of Electric and Information Engineering,Polytechnic of Bari, Bari,ItalyLucia Ballerini, Robert Fisher School of Informatics,University of Edinburgh, Edinburgh, UKBen Aldridge, Jonathan Rees Dermatology,University of Edinburgh, Edinburgh, UKThis paper presents a hierarchical classification system based on the k-Nearest Neighbors (kNN) classifier for classification of ten different classesof Malignant and Benign skin lesions from color image data.Our key contribution is to focus on the ten most common classes ofskin lesions. There are five malignant: Actinic Keratosis (AK), BasalCell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Melanoma(MEL), Intraepithelial Carcinoma (IEC) and five benign: MelanocyticNevus / Mole (ML), Seborrhoeic Keratosis (SK), Dermatofibroma (DF),Haemangioma (VASC), Pyogenic Granuloma (PYO). Moreover, we useonly high resolution color images acquired using a standard camera (non-dermoscopy).Our image dataset contains 1300 lesions belonging to ten classes (45AK, 239 BCC, 331 ML, 88 SCC, 257 SK, 76 MEL, 65 DF, 97 VASC, 24PYO and 78 IEC).(a) AK (b) BCC (c) SCC (d) MEL (e) IEC(f) ML (g) SK (h) DF (i) VASC (j) PYOFigure 1: Examples of skin lesion images from the different classes usedin this workThe ground truth used for the experiments is based on agreed classifi-cations by two dermatologists and a pathologist. Images are acquired us-ing a Canon EOS 350D SLR camera. Lighting was controlled using a ringflash and all images were captured at the same distance (∼50 cm) result-ing in a pixel resolution of about 0.03 mm. Lesions are segmented usingthe method described in [3]. Specular highlights have been removed [1].The classification approach used in our research falls within the hi-erarchical model. Our approach divides the classification task into a setof smaller classification problems corresponding to the splits in the clas-sification hierarchy. Each of these subtasks is simpler than the originaltask, since the classifier at a node in the hierarchy need only distinguishbetween a smaller number of classes. Therefore, it may be possible toseparate the smaller number of classes with higher accuracy and morespecialized properties. Moreover, it may be possible to make this deter-mination based on a smaller set of features. The hierarchy is fixed a prioriby grouping image classes into two main groups. The first group containsmalignant and pre-malignant lesion classes (AK, BCC, SCC, MEL, IEC).The second group contains benign lesions and some Melanoma (ML, SK,MEL, DF, VASC, PYO). The two groups were constructed to enhanceclassification between malignant and benign lesions but all lesions insidea group have similar characteristics: benign lesions are brown or reddishand are characterized by uniform color and a regular shape, while malig-nant lesions have pink, white, red and brown colors, high color variationwithin the same lesion, and random shapes. While all non-melanomaskin lesions are well classified at the root node, some MEL have the samebrown color as ML and SK whose samples in the dataset are an order ofmagnitude more common than MEL samples. This means that half ofMEL were misclassified as Benign lesions and this is why, to overcomethis problem, Melanoma lesions are classified in both of the two groups.Thus, if a Melanoma is classified as a benign lesion by the first classifier,it is still possible to correct the misclassification. Each of the 3 decisionFigure 2: Hierarchical organisation of our skin lesion classesnodes uses a k-Nearest Neighbor Classifier (kNN).In this work three different measures have been combined to calculatethe distance between lesions: the Bhattacharyya Distance, the HistogramIntersection Distance and the Euclidean Distance. The features used in theclassification system are widely used features extracted from shape, colorand texture information [2]. The Bhattacharyya Distance has been usedfor comparing the lesion’s mean color and covariance matrix [1]. Thecolor histogram provides information about the image’s color distribution.It is invariant to image rotation and translation and tolerant to changes inthe image’s scale. A 3D color histogram uses the values of the three colorchannels. Features using the Euclidean Distance come from six differentfamilies: Texture features (extracted from the grayscale co-occurrencematrix of color channel pairs as given in [1]); Shape-related Features(Perimeter-Area Ratio, Form Factor, Ratio between the skin lesion areaand the area of the minimum bounding box, Eccentricity); Asymmetry;Fourier Descriptors; ad hoc Color Ratio Features; Invariant Moments;Histogram-based Descriptors [2].Sequential forward feature selection (SFS) was applied for each clas-sifier in the hierarchical system. SFS finds the features that maximizeclassification accuracy for a given level.Accuracy is 93% when discriminating malignant from benign lesionsand it reaches an overall classification accuracy of 67% over ten classesof skin lesions, five malignant and five benign.Malignant BenignMalignant 491 (93%) 35 (7%)Benign 115 (15%) 659 (85%)Table 1: Confusion matrix on distinguishing between malignant and be-nign classes: rows are true classes, columns are the selected classes. Theclassification rate is inserted within brackets[1] Lucia Ballerini, Robert B. Fisher, Ben Aldridge, and Jonathan Rees.A color and texture based hierarchical k-nn approach to the classifi-cation of non-melanoma skin lesions. In M. Emre Celebi and GeraldSchaefer, editors, Color Medical Image Analysis, volume 6 of Lec-ture Notes in Computational Vision and Biomechanics, pages 63–86.Springer Netherlands, 2013. ISBN 978-94-007-5388-4.[2] Carlo Di Leo. Design of a content-based medical image retrievaland classification system for ten skin lesions classes. Master’s the-sis, Polytechnic of Bari, 2013. URL http://dx.doi.org/10.13140/2.1.4067.6644.[3] Xiang Li, Ben Aldridge, Lucia Ballerini, Robert Fisher, and JonathanRees. Depth data improves skin lesion segmentation. In Guang-Zhong Yang, David Hawkes, Daniel Rueckert, Alison Noble, andChris Taylor, editors, Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), volume 5762 of Lecture Notesin Computer Science, pages 1100–1107. Springer Berlin Heidelberg,2009. ISBN 978-3-642-04270-6.

Hierarchical Classification of Ten Skin Lesion Classes

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Hierarchical Classification of Ten Skin Lesion Classes

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