Abstract
The use of computer-assisted decision system (CAD) for the diagnosis of skin cancer dermoscopy is aggravated by the potential gains of its excellent performance. It automates the skin lesion analysis and reduces the amount of repetitive and tedious tasks to be done by physicians. This research is mainly focused on the computer vision perspective to design a CAD system which will facilitate the physicians. An automated PR system includes four inter-related processes to analyze skin lesions by the clinicians: image preprocessing, segmentation, feature extraction, feature selection and classification. The dataset contains images and annotations provided by physicians. Segmentation is an imperative preprocessing step for CAD system of skin lesions. Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Physicians are interested in examining a specific clinically significant region in a lesion. Such a region is expected to have more information in the form of texture that can be relevant for detection. In case of detection of melanoma, various local features, for example, pigment network and streaks, usually occur in peripheral region of the lesion. This led to the extraction of peripheral part for feature extraction instead of whole lesion processing. We propose novel techniques for lesion detection and classification on peripheral part of the lesion using m-mediod classifier along with the contrast of patterns. Classification results obtained from the proposed feature matrix were compared with some other texture descriptors, showing the superiority of our proposed descriptor.
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Abbas Q, Garcia IF, Celebi ME, Ahmad W, Mushtaq Q (2013) A perceptually oriented method for contrast enhancement and segmentation of dermoscopy images. Skin Res Technol 19:e490–e497
Abuzaghleh O, Barkana BD, Faezipour M (2014) Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention. In: 2014 IEEE systems, applications and technology conference (LISAT), Long Island. IEEE, pp 1–6
Abuzaghleh O, Barkana BD, Faezipour M (2015) Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention. IEEE J Transl Eng Health Med 3:1–12
American Cancer Society (2015) Cancer facts and figures. American Cancer Society, Atlanta
Argenziano G, Soyer PH, De VG, Carli P, Delfino M (2002) Interactive atlas of dermoscopy CD. EDRA Medical Publishing New Media, Milan
Arroyo JLG, Zapirain BG (2014) Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis. Comput Biol Med 44:144–157
Barata C, Marques JS, Celebi ME (2013) Towards an automatic bag-of-features model for the classification of dermoscopy images: the influence of segmentation. In: 8th international symposium on image and signal processing and analysis (ISPA). IEEE, pp 274–279
Barata C, Ruela M, Francisco M, Mendona T, Marques J (2014) Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 8:965–979
Celebi ME, Kingravi HA, Iyatomi H et al (2008) Border detection in dermoscopy images using statistical region merging. Skin Res Technol 14:347–353
Dermoscopy Tutorial. http://www.dermoscopy.org/atlas/base.html
Emre Celebi M, Wen Q, Iyatomi H, Shimizu K, Zhou H, Schaefer G (2015) A state-of-the-art survey on lesion border detection in dermoscopy images. Chapter. September. https://www.researchgate.net/publication/282124553
Farhan M, Aslam M, Jabbar S, Khalid S, Kim M (2015) Real-time imaging based assessment model for improving teaching performance and student experience in e-learning. J Real-Time Image Process 13:491–504
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Englewood Cliffs
Jmail U, Khalid S (2015) Analysis of valuable techniques and algorithms used in automated skin lesion recognition systems. Int J Priv Health Inf Manag 3(2):95–111
Jamil U et al (2016) Computer based melanocytic and nevus image enhancement and segmentation. Biomed Res Int. Article ID 2082589, 13 p. https://doi.org/10.1155/2016/2082589
Khalid S et al (2016) Segmentation of skin lesion using Cohen–Daubechies–Feauveau biorthogonal wavelet. SpringerPlus 5:1603. https://doi.org/10.1186/s40064-016-3211-4
Khalid S, Sajjad S, Jabbar S, Chang H (2017) Accurate and efficient shape matching approach using vocabularies of multi-feature space representations. J Real-Time Image Process. ISSN: 1861-8200 (Print) 1861-8219 (Online)
Malik KR, Ahmad T, Farhan M, Aslam M, Jabbar S, Khalid S, Kim M (2016) Big-data: transformation from heterogeneous data to semantically-enriched simplified data. Multimed Tools Appl 75(20):12727–12747
Marques JS, Barata C, Rozeira J (2011) Detecting the pigment network in dermoscopy images: a directional approach. In: IEEE engineering in medicine and biology society, pp 5120–5123
Mirzaalian H, Lee TK, Hamarneh G (2012) Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature. In: Proceedings of the IEEE workshop on mathematical methods for biomedical image analysis, pp 97–101
Oka H, Hashimoto M, Iyatomi H, Tanaka M (2008) Computer-based classification of dermoscopy images of melanocytic lesions on Acral Volar skin. J Invest Dermatol 128:2049–2054
Osowski S, Kurek J, Sowiska M, Kruk M, Widerski B, Walecka I (2015) Melanoma recognition using extended set of descriptors and classiers. J Image Video Process. https://doi.org/10.1186/s13640-015-0099-9
Paul A, Ahmad A, Rathore MM, Jabbar S (2016) SmartBuddy: defining human behaviors using big data analytics in social Internet of Things. IEEE Wirel Commun 23(5):68–64
Rastgoo M, Morel O, Marzani F, Garcia R (2015) Ensemble approach for differentiation of malignant melanoma. In: The international conference on quality control by artificial vision 2015. International Society for Optics and Photonics, pp 953–415
Rastgoo M, Lemaitre G, Morel O, Massich J, Garcia R, Meriaudeau F, Marzani F, Sidie D (2016) Classification of melanoma lesions using sparse coded features and random forests. HAL Id: hal-01250955. https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01250955
Ruela M, Barata C, Mendonca T, Marques JS (2013a) On the role of shape in the detection of melanomas. In: 8th international symposium on image and signal processing and analysis (ISPA). IEEE, pp 268–273
Ruela M, Barata C, Marques JS (2013b) What is the role of color symmetry in the detection of melanomas. In: Advances in visual computing. Springer, Berlin, pp 1–10
Sadeghi M, Lee TK, McLean DI, Lui H, Atkins MS (2013) Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans Med Imaging 32(5):849–861
Siegel RL, Miller KD, Jemal A (2015) Cancer statistics. CA Cancer J Clin 65(1):529
Silveira M, Nascimento JC, Marques JS, Marçal AR, Mendonça T, Yamauchi S, Maeda J, Rozeira J (2009) Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Sel Topics Signal Process 3(1):35–45
Wighton P, Lee TK, Lui H, McLean DI, Atkins MS (2011) Generalizing common tasks in automated skin lesion diagnosis. IEEE Trans Inf Technol Biomed 15(4):622–629
Zhoua H, Lib X, Schaeferc G, Emre Celebid M, Millera P (2013) Mean shift based gradient vector flow for image segmentation. Comput Vis Image Underst 117:1004–1016
Acknowledgements
We are really thankful to Higher Education Commission of Pakistan to give the indigenous Ph.D. scholarship to Ms. Uzma Jamil to complete her studies that is the part of this research article. This assignment cannot be completed without the effort and cooperation of all group members.
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Jamil, U., Khalid, S., Akram, M.U. et al. Melanocytic and nevus lesion detection from diseased dermoscopic images using fuzzy and wavelet techniques. Soft Comput 22, 1577–1593 (2018). https://doi.org/10.1007/s00500-017-2947-2
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DOI: https://doi.org/10.1007/s00500-017-2947-2