Segmentation using vector-attribute filters: methodology and application to dermatological imaging

Attribute-based filters can be involved in analysis and processing of images by considering attributes of various kinds (quantitative, qualitative, structural). Despite their potential usefulness, they are quite infrequently considered in the development of real applications. A cause of this underuse is probably the difficulty to determine correct parameters for non-scalar attributes in a fast and efficient fashion. This paper proposes a general definition of vector-attribute filters for grey-level images and describes some solutions to perform detection tasks using vector-attributes and parameters determined from a learning set. Based on these elements, an interactive segmentation method for dermatological application has been developed.

[1]  Philippe Salembier,et al.  Antiextensive connected operators for image and sequence processing , 1998, IEEE Trans. Image Process..

[2]  Philippe Schmid-Saugeona,et al.  Towards a computer-aided diagnosis system for pigmented skin lesions. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[3]  E. R. Urbach,et al.  Shape-only granulometries and gray-scale shape filters , 2002 .

[4]  Michael H. F. Wilkinson,et al.  Connected Shape-Size Pattern Spectra for Rotation and Scale-Invariant Classification of Gray-Scale Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Michael W. Berry,et al.  Using dendronal signatures for feature extraction and retrieval , 2000, Int. J. Imaging Syst. Technol..

[6]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[7]  Michael H. F. Wilkinson,et al.  Mask-Based Second-Generation Connectivity and Attribute Filters , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Michel Couprie,et al.  Building the Component Tree in Quasi-Linear Time , 2006, IEEE Transactions on Image Processing.

[9]  Luc Vincent,et al.  Morphological Area Openings and Closings for Grey-scale Images , 1994 .

[10]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[11]  Ronald Jones,et al.  Attribute Openings, Thinnings, and Granulometries , 1996, Comput. Vis. Image Underst..

[12]  Ronald Jones,et al.  Connected Filtering and Segmentation Using Component Trees , 1999, Comput. Vis. Image Underst..

[13]  L. Vincent Grayscale area openings and closings, their efficient implementation and applications , 1993 .

[14]  Michael H. F. Wilkinson,et al.  Second-Order Connected Attribute Filters Using Max-Trees , 2005, ISMM.

[15]  P. Schmid,et al.  Analysis of skin lesions with pigmented networks , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[16]  Isabelle Bloch,et al.  opologically controlled segmentation of 3D magnetic resonance images of the head by using morphological operators , 2003, Pattern Recognit..