Boundary Extraction in Images Using Hierarchical Clustering-based Segmentation

Hierarchical organization is one of the main characteristics of human segmentation. A human subject segments a natural image by identifying physical objects and marking their boundaries up to a certain level of detail [1]. Hierarchical clustering based segmentation (HCS) process mimics this capability of the human vision. The HCS process automatically generates a hierarchy of segmented images. The hierarchy represents the continuous merging of similar, spatially adjacent or disjoint, regions as the allowable threshold value of dissimilarity between regions, for merging, is gradually increased. HCS process is unsupervised and is completely data driven. This ensures that the segmentation process can be applied to any image, without any prior information about the image data and without any need for prior training of the segmentation process with the relevant image data. The implementation details of HCS process have been described elsewhere in the author's work [2]. The purpose of the current study is to demonstrate the performance of the HCS process in outlining boundaries in images and its possible application in processing medical images. [1] P. Arbelaez. Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Proceedings 5th IEEE Workshop on Perceptual Organization in Computer Vision (POCV'06). June 2006. New York, USA. [2] A. N. Selvan. Highlighting Dissimilarity in Medical Images Using Hierarchical Clustering Based Segmentation (HCS). M. Phil. dissertation, Faculty of Arts Computing Engineering and Sciences Sheffield Hallam Univ., Sheffield, UK, 2007.

[1]  Gerald Schaefer,et al.  Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[2]  Milan Sonka,et al.  "Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis " , 2000 .

[3]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[4]  Pablo Andrés Arbeláez,et al.  Boundary Extraction in Natural Images Using Ultrametric Contour Maps , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[5]  A. D. Arul Nirai Selvan.  Highlighting dissimilarity in medical images using hierarchical clustering based segmentation (HCS) , 2007 .

[6]  Thomas Martin Deserno,et al.  Hierarchical feature clustering for content-based retrieval in medical image databases , 2003, SPIE Medical Imaging.

[7]  Bidyut Baran Chaudhuri,et al.  An MLP-based texture segmentation method without selecting a feature set , 1997, Image Vis. Comput..

[8]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  Rob Procter,et al.  Drawing the Line Between Perception and Interpretation in Computer-Aided Mammography , 1997 .

[10]  Witold Pedrycz,et al.  Unsupervised hierarchical image segmentation with level set and additive operator splitting , 2005, Pattern Recognit. Lett..

[11]  Stella X. Yu,et al.  Segmentation induced by scale invariance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  M. Elter,et al.  CADx of mammographic masses and clustered microcalcifications: a review. , 2009, Medical physics.

[13]  Josef Bigün,et al.  Hierarchical image segmentation by multi-dimensional clustering and orientation-adaptive boundary refinement , 1995, Pattern Recognit..

[14]  Zhuowen Tu,et al.  Supervised Learning of Edges and Object Boundaries , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  L. Tabár,et al.  Teaching atlas of mammography. , 1983, Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. Erganzungsband.

[16]  Ying Sun,et al.  A hierarchical approach to color image segmentation using homogeneity , 2000, IEEE Trans. Image Process..

[17]  Keith Price,et al.  Picture Segmentation Using a Recursive Region Splitting Method , 1998 .