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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

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Wavelet Based Decomposition and Neural Network Classification for Melanoma Diagnosis V. Lalitha, G. Geetha

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Assisstant Professor, Faculty of Computing, Sathyabama University, Jeppiaar Nagar, Chennai Dean, School of Computer Science, Lovely Professional University, Phagwara, India

Abstract— Melanoma is a dangerous type of skin cancer which can be cured if diagnosed early. Finding the difference between melanoma and mole is a complex task.In this paper we have applied canny edge detection algorithm for segmentation and wavelet based decomposition for feature extraction purpose.

Then the extracted features are fed as input to neural network for classification and the results are proven to be better in melanoma diagnosis.

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Index Terms—melanoma, wavelet based decomposition, neural network

I. Introduction

Melanoma is a skin cancer in which malignant cells become visible on the outer skin layers. It is a fatal type of skin cancer which leads to death of thousands of people every year.[1] It is curable if diagnosed early. But differentiating melanoma with other pigmented skin lesion is a challenging task even for experienced dermatologists. But there are some of the fundamental symptoms of skin cancer, such as: AǦ Asymmetry, B Ǧ Border irregularity, C Ǧ Color variation and D Ǧ Dermoscopic structures. These symptoms known as ABCD rule [2] help to diagnose melanoma. This works almost correctly for thin melanocytic lesions. The accuracy rate is between 59% to 88% in melanoma identification but a more precise diagnosis is necessary.

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A novel method to melanoma diagnosis is based on using canny edge detection algorithm for segmentation and feature extraction by using wavelet transform.

II. Background

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There are many transforms available for skin cancer classification.One such method is Fourier transform [3]. But the main shortcoming of Fourier Transform is difficulty in extracting time information. Also it not suitable for images with sharp changes. Hence Windowed Fourier Transform (WFT) came into existence but unfortunately, this transform is difficult to invert, due to its extreme redundancy, and it does not capture short “pulses” accurately, unless a very small window is used[4].

III. Wavelet Transform

To evaluate signals and images, a new multi resolution analytical tool called Wavelet transform was used [5]. They depict an image at each scale and location. Using Wavelet transform helps to detect edges and sharp transitions very efficiently.

IV. Preprocessing

The preprocessing step removes the noise from image and enhances the image. Noise is undesirable parts of the melanoma and benign images. We have preferred the median filter from the different filters available as it is efficient than mean filter. Many algorithms are available for noise removal and based on the data set, devices used to collect image we have to select the right filter.

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

Fig 1 Read ding image frrom dataset

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We convvert into gray scale image for removing g hue and satu uration information and p preserving lum minance.

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Fig 2 Conveerting into graay scale imag ge

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We havee used median n filter is for p preprocessin ng. In median filtering, thee output pixell’s value is caalculated by median o of the neighb boring pixels, and not by m mean. For exttreme values,, the median is less reactivve than the mean. Hence it is bettter than meaan in removin ng these outlliers at the saame time it m maintains thee sharpness of the im mage[6]

Fig 3 A Applying med dian filter

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V. Segmentattion

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Segmenttation is the p process of sub bdividing thee image and b boundaries a are identified. It is an imp portant step as the en nd result depends on th he outcome o of this process. The quallity depends on the algorrithm used [7].Here canny edge detection alg gorithm is ussed as it deteermines the boundaries eeffectively esp pecially for melanom ma images.

F Fig 4. After ap pplying cannyy edge detecttion

VI. Fe eature Extrraction

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In this sttage, the key features of th he data are id dentified and d extracted fo or further pro ocessing. Thiss makes the raw dataa more usefu ul in processiing. By extraacting featurees, we narrow w down the image data to a set of features w which will bee input for fu urther processsing [8,9].

We havee used Biorth hogonal wavelets. In this c case, instead of having on ne scaling and d wavelet fun nction, two scaling fu unctions are used which m may produce two differentt wavelet fun nctions. Intereesting properrties can be derived b by using two wavelets, on ne for decomp position and the other forr reconstructtion instead o of the same single on ne.

There arre three stepss of decompo osition in this wavelet bassed feature eextraction. Th he results of the second u in prod duction of fin nal results. At A each step of decompo osition, the step werre more detaailed to be used wavelet o of primary im mage is divid ded into an aapproximate t that shows b basic informaation and thrree detailed images tthat shows tthe vertical, horizontal and diagonaal details, reespectively. D During next steps, the approxim mate image iss employed in nstead of the original imag ge and is deco omposed into o sub imagess.

The meaan and variaance of deco omposition ccoefficients o of the second d step are u utilized to prroduce the features. Each step o of decomposiition producees coefficientts for approxximation and details. Theere are four mean and four varian nce features.

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

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Fig 5. P Performing s single level waavelet decom mposition

Fig 6 P Performing seecond level w wavelet decom mposition

VIII. Classifica ation

During tthe neural neetwork stage, images are divided into o training, teesting and vaalidation dataa. After the initial round, the netw work replaces the subdiviisions of train ning and testiing, so that th he whole datta would be considered as the tessting subdivission. Finally the network k is fed with t the validation data and f final results are obtaiined. The neu ural network k is used to cclassify betweeen melanom ma and other skin lesion i images. We have used feed forwarrd neural nettwork for classsification pu urpose. melanoma and d implementting the system Neural nettwork toolbox and Matlab b are used.

For classification of m ulated resultss are shown b below.

The simu

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Proceedings of The National Conference on Man Machine Interaction 2014 [NCMMI 2014]

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Fig g 7. Neural neetwork patterrn recognition n tool

Fig 9 9. Performancce chart

Fig 10. ROC(Recciever Operating Characteeristic)

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Fig 111. Confusion Matrix

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VIII. Concclusion

F Finally the ad dvantage of u using wavelett transform fo or melanomaa images is d demonstrated d effectively b by applying it to the classification problem.

T The steps in this algorithm inccludes pre p processing,seg gmentation , feature extrraction and classification. The ROC and confusio on matrix h has shown 96% accuracy and the diff fference of trrue positive rate and faalse posittive rate

depends on the featurres extracted d by the differrent wavelet f functions.

IX X. Future W Work

wavelet transfform for melaanoma imagees. But the cu urvelet transfform which In this paaper we havee employed w is the exxtension of w wavelet transfform can be a applied for fe feature extracction to get b better resultss as it deals with curvved edges more effectivelly[11,12]. Thesse curvelets g gives location n and directio onal informaation which are moree important fo for dealing wiith curved im mages.

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Reference es

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Nachbar,F.,,W N W.,Merckle,T T.,Cognetta,A A.B,Vogt,T.,Laandthaler,M.,,Bilek,P.,Brau unǦFalco,O and P Plewig,G.(199 94) ‘The ABC CD rule of dermatoscop py: High pro ospective valu ue in the diagnosis of d doubtful mellanocytic skin n lesions’, Jo ournal of Am merican Acadeemy of Derm motology,Vol..30, pp.551– 5 559.

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Sokolowaski A,Pardela T,”Fourier trasnsform for melanoma imag S ge Classificaation”,IEEE I International l conference o on HSI,2013,p pp 386Ǧ389.

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SachinV.Patw S wardhan, Ataam P.Dhawn n, Patricia A A.Relue, “Classsification off melanoma using tree s structured wa avelet transfo orms”, Elsevieer, Computerr Methods an nd Programs in Biomediciine, Vol. 72, P Page no. 223Ǧ Ǧ239, 2003

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Xiaojing Yuan, Ning Situ,, George Zou X uridakis, “ A n narrow band graph partittioning meth hod for skin l lesion segme ntation”, Elseevier, Pattern n Recognition n, Vol.42, Pag ge no. 1017Ǧ10228, 2009

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9. M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, vol. 1, pp. 205–221, Apr. 1992.

10. William John Palm, McGraw Hill Professional, “Introduction To Matlab 7 for Engineers”, Technology & Engineering, Pages 682, 2005.

11. Tobias Geback and Petros Koumoutsakos, “Edge detection in microscopy images using curvelets” Journal BMC Bioinformatics, 2009,Volume 10, pp 75Ǧ89.

13. Candes E, Laurent Demanet, David Donoho and Lexing Ying,(2006),” Fast Discrete Curvelet Transforms”,Journal of multiscale modeling and simulation ,Vol. 5, No. 3, pp. 861–899.

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12. G Geetha,V Lalitha, “Boon and Bane of Curvelet Transform” at International Conference on Recent Trends in Business Administration and Information Processing, BAIP 2010,Trivandrum,Kerala,Springer,Volume 70, pp 545Ǧ551.

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