Cytoplasm Contour Approximation Based on Color Fuzzy Sets and Color Gradient

Here we propose a method for contour detection of cells on medical images. The problem that arises in such images is that cells' color is very similar to the background, because the cytoplasm is translucent and sometimes overlapped with other cells, making it difficult to properly segment the cells. To cope with these drawbacks, given a cell center, we use hue and saturation histograms for defining the fuzzy sets associated with cells relevant colors, and compute the membership degree of the pixels around the center to these fuzzy sets. Then we approach the color gradient (module and argument) of pixels near the contour points, and use both the membership degrees and the gradient information to drive the deformation of the region borders towards the contour of the cell, so obtaining the cell region segmentation.

[1]  Eduard Montseny,et al.  On the Reliability of the Color Gradient Vector Argument Approach , 2009, IFSA/EUSFLAT Conf..

[2]  Slawomir Wesolkowski,et al.  Comparison of color image edge detectors in multiple color spaces , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Stelios Krinidis,et al.  Fuzzy Energy-Based Active Contours , 2009, IEEE Transactions on Image Processing.

[4]  F. Gibou A fast hybrid k-means level set algorithm for segmentation , 2005 .

[5]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[6]  A R Smith,et al.  Color Gamut Transformation Pairs , 1978 .

[7]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[8]  James M. Keller,et al.  White blood cell detection in bone marrow images , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[9]  Xun Wang,et al.  A comparative study of deformable contour methods on medical image segmentation , 2008, Image Vis. Comput..

[10]  J. Velez,et al.  Improved Fuzzy Snakes Applied to Biometric Verification Problems , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[11]  Anthony Yezzi,et al.  Hybrid geodesic region-based curve evolutions for image segmentation , 2007, SPIE Medical Imaging.

[12]  Elena Casiraghi,et al.  Liver segmentation from computed tomography scans: A survey and a new algorithm , 2009, Artif. Intell. Medicine.

[13]  Alvy Ray Smith,et al.  Color gamut transform pairs , 1978, SIGGRAPH.

[14]  João Manuel R. S. Tavares,et al.  Segmentation of structures in medical images: review and a new computational framework , 2008 .