Open Access

ARTICLE

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Jehyeok Rew, Hyungjoon Kim, Eenjun Hwang^*

School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Korea

* Corresponding Author: Eenjun Hwang. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2021, 69(1), 801-817. https://doi.org/10.32604/cmc.2021.017892

Received 16 February 2021; Accepted 03 April 2021; Issue published 04 June 2021

Abstract

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.

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Citations