Accurate Segmentation and Registration of Skin Lesion Images to Evaluate Lesion Change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolution.

[1]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  M S Woolfson,et al.  Application of region-based segmentation and neural network edge detection to skin lesions. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[3]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[4]  Heng Huang,et al.  A New Hybrid Technique for Dermatological Image Registration , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[5]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[7]  W. Stolz,et al.  The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. , 1994, Journal of the American Academy of Dermatology.

[8]  Alejandro F. Frangi,et al.  Recent Advances in Computational Methods and Clinical Applications for Spine Imaging , 2015 .

[9]  David Dagan Feng,et al.  Saliency-Based Lesion Segmentation Via Background Detection in Dermoscopic Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[10]  S. Menzies,et al.  Short-term digital surface microscopic monitoring of atypical or changing melanocytic lesions. , 2001, Archives of dermatology.

[11]  S. Pavlopoulos,et al.  New hybrid stochastic-deterministic technique for fast registration of dermatological images , 2004, Medical and Biological Engineering and Computing.

[12]  Qiang Wang,et al.  Multiscale superpixel classification for tumor segmentation in breast ultrasound images , 2012, 2012 19th IEEE International Conference on Image Processing.

[13]  Ilias Maglogiannis.  Automated Segmentation and Registration of Dermatological Images , 2003, J. Math. Model. Algorithms.

[14]  Sing Bing Kang,et al.  Stereo for Image-Based Rendering using Image Over-Segmentation , 2007, International Journal of Computer Vision.

[15]  Cheng-Chang Lien,et al.  Blur image segmentation using iterative super-pixels grouping method , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[16]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  R Kaur,et al.  Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images , 2017, 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.

[18]  Chunhua Shen,et al.  Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Jeremy S Bordeaux,et al.  Early detection of melanoma: reviewing the ABCDEs. , 2015, Journal of the American Academy of Dermatology.

[20]  Zhen Ma,et al.  A Novel Approach to Segment Skin Lesions in Dermoscopic Images Based on a Deformable Model , 2016, IEEE Journal of Biomedical and Health Informatics.

[21]  T Burgdorf,et al.  The ABCD rule of dermatoscopy does not apply to small melanocytic skin lesions. , 2001, Archives of dermatology.

[22]  Dimitrios D. Vergados,et al.  Image registration of follow-up examinations in digital dermoscopy , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[23]  Do Hyun Chung,et al.  Segmenting skin lesions with partial differential equations based image processing algorithms , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[24]  Guillermo Sapiro,et al.  Segmenting skin lesions with partial-differential-equations-based image processing algorithms , 2000 .

[25]  Juha Röning,et al.  Registration of nevi in successive skin images for early detection of melanoma , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[26]  Gerald Schaefer,et al.  Lesion border detection in dermoscopy images , 2009, Comput. Medical Imaging Graph..

[27]  Rahil Garnavi,et al.  Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images , 2017, MICCAI.

[28]  Ghassan Hamarneh,et al.  Skin lesion tracking using structured graphical models , 2016, Medical Image Anal..

[29]  Jesús Bescós,et al.  SP-SIFT: enhancing SIFT discrimination via super-pixel-based foreground-background segregation , 2014 .

[30]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[31]  Bing Li,et al.  Salient Object Detection via Structured Matrix Decomposition. , 2017, IEEE transactions on pattern analysis and machine intelligence.

[32]  Rafael García,et al.  A New Total Body Scanning System for Automatic Change Detection in Multiple Pigmented Skin Lesions , 2015, IEEE Transactions on Medical Imaging.

[33]  Gerald Schaefer,et al.  Skin lesion segmentation using an improved snake model , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[34]  Gongping Yang,et al.  A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering , 2014, Comput. Math. Methods Medicine.

[35]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[36]  Scott Krig,et al.  Interest Point Detector and Feature Descriptor Survey , 2014 .

[37]  Mohammad Aldeen,et al.  Border detection in dermoscopy images using hybrid thresholding on optimized color channels , 2011, Comput. Medical Imaging Graph..

[38]  Hon Fung Li,et al.  Shapes Recognition Using the Straight Line Hough Transform: Theory and Generalization , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Junji Maeda,et al.  Comparison of Segmentation Methods for Melanoma Diagnosis in Dermoscopy Images , 2009, IEEE Journal of Selected Topics in Signal Processing.

[40]  Xuelong Li,et al.  Mean shift based gradient vector flow for image segmentation , 2013, Comput. Vis. Image Underst..

[41]  Célia A. Zorzo Barcelos,et al.  An automatic based nonlinear diffusion equations scheme for skin lesion segmentation , 2009, Appl. Math. Comput..

[42]  Bruce Mcgregor,et al.  Automatic registration of images of pigmented skin lesions , 1998, Pattern Recognit..

[43]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[45]  Alexandru Telea,et al.  An Unified Multiscale Framework for Planar, Surface, and Curve Skeletonization , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  D A Perednia,et al.  Automatic registration of multiple skin lesions by use of point pattern matching. , 1992, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[47]  Gerald Schaefer,et al.  Colour and contrast enhancement for improved skin lesion segmentation , 2011, Comput. Medical Imaging Graph..