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A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning

  • Image & Signal Processing
  • Published:
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Abstract

Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases. However, cutaneous vessels are highly variable in shape, size, color and architecture, which complicate the detection task. Considering the large variability of these structures, conventional vessel detection techniques lack the generalizability to detect different vessel types and require separate algorithms to be designed for each type. Furthermore, such techniques are highly dependent on precise hand-crafted features which are time-consuming and computationally inefficient. As a solution, we propose a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) for comprehensive detection of cutaneous vessels. Each training image is divided into small patches of either containing or non-containing vasculature. A multilayer SSAE is designed to learn hidden features of the data in hierarchical layers in an unsupervised manner. The high-level learned features are subsequently fed into a classifier which categorizes each patch into absence or presence of vasculature and localizes vessels within the lesion. Over a test set of 3095 patches derived from 200 images, the proposed framework demonstrated superior performance of 95.4% detection accuracy over a variety of vessel patterns; outperforming other techniques by achieving the highest positive predictive value of 94.7%. The proposed Computer-Aided Diagnosis (CAD) framework can serve as a decision support system assisting dermatologists for more accurate diagnosis, especially in teledermatology applications in remote areas.

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Funding

This study was funded by Natural Science and Engineering Research Council (NSERC) Canada (grant number 288194–11).

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Authors and Affiliations

  1. Biomedical Engineering Program, University of British Columbia, Vancouver, BC, Canada

    Pegah Kharazmi & Tim K. Lee

  2. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada

    Jiannan Zheng & Z. Jane Wang

  3. Department of Dermatology and Skin Science, University of British Columbia, Vancouver, BC, Canada

    Harvey Lui & Tim K. Lee

  4. Departments of Cancer Control Research and Integrative Oncology, British Columbia Cancer Agency, Vancouver, BC, Canada

    Harvey Lui & Tim K. Lee

Authors
  1. Pegah Kharazmi
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  2. Jiannan Zheng
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  3. Harvey Lui
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  4. Z. Jane Wang
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  5. Tim K. Lee
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Corresponding author

Correspondence to Pegah Kharazmi.

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The authors declare no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Image & Signal Processing

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Kharazmi, P., Zheng, J., Lui, H. et al. A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning. J Med Syst 42, 33 (2018). https://doi.org/10.1007/s10916-017-0885-2

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  • DOI: https://doi.org/10.1007/s10916-017-0885-2

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