Color energy as a seed descriptor for image segmentation with region growing algorithms on skin wound images

This paper presents a seed finding method for region growing segmentation approach using color channel energy in image regions. Instead of using the RGB system separated for each pixel, the proposal uses the energy on each color channel to improve the range of the possible values, then creates a more specific seed to detail different regions. Region size used to calculate energy was adjusted to verify the proposed method. Images used were real wound photos, taken from patients undergoing treatment at the university hospital. Results showed that energy on regions presents enough information to segment, leading to a high percentage of matching with experts marks.

[1]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  Jianqiang Li,et al.  Classification of retinal image for automatic cataract detection , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[3]  Aleksandra Mojsilovic,et al.  Adaptive image segmentation based on color and texture , 2002, Proceedings. International Conference on Image Processing.

[4]  Mounir Sayadi,et al.  Skin disease analysis and tracking based on image segmentation , 2013, 2013 International Conference on Electrical Engineering and Software Applications.

[5]  Jie Yang,et al.  Superpixel based color contrast and color distribution driven salient object detection , 2013, Signal Process. Image Commun..

[6]  P. R. Dahl,et al.  Skin ulcers misdiagnosed as pyoderma gangrenosum. , 2002, The New England journal of medicine.

[7]  Lei Zhang,et al.  Multidimensional analysis for Traditional Chinese Medicine diagnosis and treatment on hepatitis diseases , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[8]  Chi-Man Pun,et al.  Color image segmentation using adaptive color quantization and multiresolution texture characterization , 2014, Signal Image Video Process..

[9]  Rangaraj M. Rangayyan,et al.  Segmentation of dermatological ulcers using clustering of color components , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[10]  Jitendra Malik,et al.  Contour and Texture Analysis for Image Segmentation , 2001, International Journal of Computer Vision.

[11]  R. Frykberg,et al.  Diabetic foot ulcers: current concepts. , 1998, The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons.

[12]  André Aurengo,et al.  Evolving descriptors for texture segmentation , 1997, Pattern Recognit..

[13]  Baozong Yuan,et al.  A new algorithm for texture segmentation based on edge detection , 1991, Pattern Recognit..

[14]  Jie Yang,et al.  Texture Segmentation using LBP embedded Region Competition , 2005 .

[15]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[16]  Heng-Da Cheng,et al.  A novel automatic seed point selection algorithm for breast ultrasound images , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  S. M. Steve.  SUSAN - a new approach to low level image processing , 1997 .

[18]  Raúl Rojas,et al.  SIOX: simple interactive object extraction in still images , 2005, Seventh IEEE International Symposium on Multimedia (ISM'05).

[19]  H. B. Kekre,et al.  Color image segmentation using Kekre's fast codebook generation algorithm based on energy ordering concept , 2009, ICAC3 '09.

[20]  Adolfo Martínez Usó,et al.  A novel energy minimization criterion for color image segmentation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[21]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[22]  Paul F. Whelan,et al.  Image segmentation based on the integration of colour-texture descriptors - A review , 2011, Pattern Recognit..

[23]  Adel M. Alimi,et al.  An intelligent system for renal segmentation , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[24]  Petri Välisuo,et al.  Lower extremity ulcer image segmentation of visual and near‐infrared imagery , 2010, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[25]  Xavier Bresson,et al.  Fast Texture Segmentation Based on Semi-Local Region Descriptor and Active Contour , 2009 .

[26]  Filiberto Pla,et al.  A novel energy minimization criterion for color image segmentation , 2004, ICPR 2004.