Skip to main content
Springer Nature Link
Log in

Artificial intelligence techniques for enhanced skin lesion detection

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The timely diagnosis of skin lesion diseases is highly difficult for people living in rural or far flung areas due to dearth of qualified dermatologists. However, the dermatologists can diagnose skin lesion diseases by carefully examining the high-quality images at their clinics or from a distance area. Further, the computerized automatic diagnostic system may assist primary health professionals for quick and accurate diagnosis of these skin diseases. Thus, there is a need for medical image processing and analysis of skin lesion images to enhance their visibility properties. An efficient and effective skin lesion detection and identification software tool will provide a better classification system and may enhance the automation of skin lesion diagnosis. In this work, detection of skin lesions from human skin images is conducted by utilizing three image processing segmentation methodologies namely—Edge Detection using Ant Colony Optimization, Color Space-based Thresholding, Genetic Algorithm-based Segmentation and FCM-Based Image Segmentation. In order to quantitatively collate the working of three techniques, the entropy values of skin lesion images are considered. Application of FCM-based Segmentation yields in far better attribute of skin lesion images as compared to Genetic Algorithm-based Segmentation, Edge Detection using Ant Colony Optimization and Color Space-based Thresholding.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahn E (2017) Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J Biomed Health Inform 21(6):1685–1693

    Article  Google Scholar 

  • Ali A, Li J, O’Shea S et al (2019) A deep learning based approach to skin lesion border extraction with a novel edge detector in dermoscopy images. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), vol 1, pp 1–7

  • Ali A et al (2020) A novel fuzzy multilayer perceptron (F-MLP) for the detection of irregularity in skin lesion border using dermoscopic images. Front Med. https://doi.org/10.3389/fmed.2020.00297

    Article  Google Scholar 

  • Ali A et al (2020) Automating the ABCD rule for melanoma detection: a survey. IEEE Access 8:83333–83346

    Article  Google Scholar 

  • Ali A et al (2020) A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images. PeerJ Comput Sci 6:e268

    Article  Google Scholar 

  • Bi L, Kim J, Ahn E et al (2017) Dermoscopic image segmentation via multi-stage fully convolutional networks. IEEE Trans Biomed Eng 64:2065–2074

    Article  Google Scholar 

  • Caslellini P, Scalise A, Scalise L (2000) A 3-D measurement system for the extraction of diagnostic parameters in suspected skin nevoid lesions. IEEE Trans Instrum Meas 49:924–928

    Article  Google Scholar 

  • Chiem A, Jumaily A, Khushaba R (2007) A novel hybrid system for skin lesion detection. Intell Sens Sens Netw Inf 3:567–572

    Google Scholar 

  • Clawson K, Morrow P, Scotney B et al (2009) Analysis of pigmented skin lesion border irregularity using the harmonic wavelet transform. In: Proceedings of international machine vision and image processing conference, vol 13, pp 18–23

  • Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698

    Article  Google Scholar 

  • Denton W, Duller A, Fish P (1995). Robust boundary detection for skin lesions. In: Proceedings of annual conference engineering in medicine and biology society, vol 7, pp 407–408

  • Dorigo M, Birattari M, Stützle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1:28–39. https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

  • George G, Oommen R, Shelly S et al (2018) A survey on various median filtering techniques for removal of impulse noise from digital image. In: Proceedings of Conference on Emerging Devices and Smart Systems (ICEDSS), vol 1, pp 235–238

  • Gonzalez R, Woods R (2009) Digital image processing, 3rd edn. Pearson

    Google Scholar 

  • Hemalatha R, Thamizvani T, Josephin A et al (2018) Active contour based segmentation techniques for medical image analysis. Med Biol Image Anal. https://doi.org/10.5772/intechopen.74576

    Article  Google Scholar 

  • Humayun J, Malik A, Kamel N (2011) Multilevel thresholding for segmentation of pigmented skin lesions, vol 1, pp 310–314

  • Jaseema Y, Sathik M, Beevi S (2011) Robust segmentation algorithm using LOG edge detector for effective border detection of noisy skin lesions. In: International Conference on Computer, Communication and Electrical Technology (ICCCET), vol 2, no 1, pp 60–65

  • Konstantinos N, Venetsanopoulo A (2017) Color image processing and applications, digital signal processing. Springer. https://doi.org/10.1007/978-3-662-04186-4

    Book  Google Scholar 

  • Kavitha J, Suruliandi A (2016) Texture and color feature extraction for classification of melanoma using SVM. In: Proceedings of international conference on computing technologies and intelligent data engineering, vol 1, pp 1–6

  • Khan MA, Javed MY, Sharif M, Saba T, Rehman A (2019) Multi-model deep neural network based features extraction and optimal selection approach for skin lesion classification. In: 2019 international conference on computer and information sciences (ICCIS), pp 1–7. https://doi.org/10.1109/ICCISci.2019.8716400

  • Kwasnicka H, Paradowski M (2005) Melanocytic lesion images segmentation enforcing by spatial relations based declarative knowledge. In: International conference on intelligent systems design and applications, vol 5, pp 286–291

  • Linton CP (2011) Essential morphologic terms and definitions. J Dermatol Nurses’ Assoc 2:102–103

    Article  Google Scholar 

  • Lu J, Kazmierczak E, Manton J, Sinclair R (2013) Automatic segmentation of scaling in 2-D psoriasis skin images. IEEE Trans Med Imaging 4:719–730

    Article  Google Scholar 

  • Ma L, Huang K, Yan J, Wu K, Zhu L (2010). Boundary roughness analysis of skin lesions using local fractals and wavelet transforms. In: Proceedings of international conference on bioinformatics and biomedical engineering, vol 4, pp 1–4

  • Maglogiannis I, Pavlopoulos S, Koutsouris D (2005) An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans Inf Technol Biomed 9:86–98

    Article  Google Scholar 

  • Maglogiannis I, Zafiropoulos E, Kyranoudis C (2006) Intelligent segmentation and classification of pigmented skin lesions in dermatological images. In: Advances in artificial intelligence. Lecture notes in computer science, vol 1, pp 214–223

  • Mahmood F, Abdulaziz H (2015) Automatic triple—a segmentation of skin cancer images based on Histogram classification. AL-Rafdain Eng J (AREJ) 23(5):31–42

    Article  Google Scholar 

  • Masood N, Mashali H, Mohamed S (2008) Color segmentation for skin lesions classification. In: Proceedings of CIBEC Cairo International Biomedical Engineering Conference, vol 1, pp 1-4

  • Moore A, Prince S, Warrell J et al (2009) Scene shape priors for super pixel segmentation, vol 12, pp 771–778

  • Mittal N, Singh H, Gupta R (2015) Decomposition & reconstruction of medical images in MATLAB using different Wavelet parameters. In: Proceeding of international conference on futuristic trends on computational analysis and knowledge management, vol 1, pp 647–653

  • Mittal N, Tanwar S, Khatri S (2017) Identification & enhancement of different skin lesion images by segmentation techniques. In: Proceedings of International Conference on Reliability, Infocom Technologies and Optimization (trends and future directions) (ICRITO), Noida, pp 609–614. https://doi.org/10.1109/ICRITO.2017.8342500

  • Selva (2020) Color image segmentation using genetic algorithm (clustering). https://www.mathworks.com/matlabcentral/fileexchange/64223-color-image-segmentation-using-genetic-algorithm-clustering. MATLAB Central File Exchange. Retrieved January 29, 2020

  • Sengupta S, Mittal N (2017) Analysis of various techniques of feature extraction on skin lesion images. In: Proceedings of international conference on Reliability, Infocom Technologies and Optimization, vol 6, pp 651–656

  • Sengupta S, Mittal N, Modi M (2019a) Improved skin lesion edge detection method using ant colony optimization. Skin Res Technol 25:846–856

    Google Scholar 

  • Sengupta S, Mittal N, Modi M (2019b) Color space based thresholding for segmentation of skin lesion images. Int J Biomed Eng Technol. https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijbet. Accessed 31 July 2019

  • Singh S (2013) Microscopic image analysis of nanoparticles by edge detection using ant colony optimization. IOSR J Comput Eng (IOSR-JCE) 11:84–89

    Article  Google Scholar 

  • Singh K, Singh A (2010) A study of image segmentation algorithms for different types of images. Int J Comput Sci Issues 7(5):1–4

    Google Scholar 

  • Singh V et al (2019) FCA-Net: adversarial learning for skin lesion segme ntation based on multi-scale features and factorized channel attention. IEEE Access 7:552–565

    Google Scholar 

  • Takruri M, Jumaily A, Mahmoud K (2014) Automatic recognition of melanoma using support vector machines: a study based on wavelet, curvelet and color features. In: International Conference on Industrial Automation, Information and Communications Technology (IAICT), vol 1, pp 70–75

  • Tan T, Zhang L, Jiang M (2016) An intelligent decision support system for skin cancer detection from dermoscopic images. In: Proceedings of International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), vol 12, pp 2194–2199

  • Vennila G, Padma L, Shunmuganathan K (2012) Dermoscopic image segmentation and classification using machine learning algorithms. In: International Conference on Computing, Electronics and Electrical Technologies (ICCEET), vol 1, pp 1122–1127

  • Zagrouba E, Barhoumi W (2003) Objective and cost-efficient approach for skin lesions classification. In: International conference on computer systems and applications, vol 1, pp 135-139

  • Zhang H, Fritts J, Goldman S (2008) Image segmentation evaluation: a survey of unsupervised methods. Sci Direct Comput vis Image Underst 1:260–280

    Article  Google Scholar 

  • Zhang L et al (2019) Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons. J Med Imaging 6:024001. https://doi.org/10.1117/1.JMI.6.2.024001

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, 201301, India

    Sudhriti Sengupta & Neetu Mittal

  2. Yashoda Super Speciality Hospital, Ghaziabad, 201001, India

    Megha Modi

Authors
  1. Sudhriti Sengupta
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Neetu Mittal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Megha Modi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Sudhriti Sengupta.

Ethics declarations

Conflict of interest

There is no conflict of interest in the research work and manuscripts.

Human and animal rights

The article entitled "Artificial Intelligence Techniques for Enhanced Skin Lesion Detection" does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sengupta, S., Mittal, N. & Modi, M. Artificial intelligence techniques for enhanced skin lesion detection. Soft Comput 25, 15377–15390 (2021). https://doi.org/10.1007/s00500-021-06150-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06150-0

Keywords