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Iterative Fuzzy Clustering Regions of Interest in Skin Lesions

Iterative Fuzzy Clustering Regions of Interest in Skin Lesions. By R. Cucchiara, C. Grana, M. Piccardi Presented by Mohammed Jirari October 23 rd , 2002 Image Processing Lab. Introduction. Definition of Skin Melanoma Systems used for Melanoma diagnosis Recent research.

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Iterative Fuzzy Clustering Regions of Interest in Skin Lesions

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  1. Iterative Fuzzy Clustering Regions of Interest in Skin Lesions By R. Cucchiara, C. Grana, M. Piccardi Presented by Mohammed Jirari October 23rd, 2002 Image Processing Lab

  2. Introduction • Definition of Skin Melanoma • Systems used for Melanoma diagnosis • Recent research

  3. Method used in paper • Preprocessing • Karhunen-Loeve transform • Fuzzy c-means clustering • Topological tree

  4. Preprocessing • Convolve the image with a Gaussian kernel with standard deviation of one pixel. • Transform images from original RGB into CIE L*a*b* color coordinates.

  5. Karhunen-Loeve transform • Projection of the vectors to be reduced on the eigenvectors of their covariance matrix using the following:

  6. Karhunen-Loeve transform (cont.) • The Karhunen-Loeve transform of vector x is defined by:

  7. Fuzzy c-means clustering • Use the following 2 recurrent equations:

  8. Topological tree • Bright clusterHealthy skin Dark cluster Lesion

  9. Topological tree (cont.) • Def1:Skin region of Interest(Skin ROI) a set of pixels of the skin image exhibiting 3 properties: uniform color, connected pixels and significant area. • Def2:Topological Tree(TT) a tree whose nodes are skin ROIs and the arcs topological inclusion relationships between skin ROIs

  10. Pseudo-code of the algorithm AnalyzeRegion (region R, node N) {if(not StopCondition (R)){ [C1,C2]=FCM(R); [Cint,Cext]=VerifyInclusion([C1,C2]); if(exists([Cint,Cext])){ Cres=R-Cint-Cext; Nnew=AddNodeToTree(Cext,N); foreach C in ConnectedComponents(Cint) AnalyzeRegion(C+Cres,Nnew);} else { Analyzeregion(R-C1,N); AnalyzeRegion(R-C2,N); }} else AddNodeToTree(R,N);}

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