Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis
Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis
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DOI：
10.1109/tmm.2018.2814346
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发表时间：
2017-11
影响因子：
7.3
通讯作者：
Thanh-Toan Do;Tuan Hoang;Victor Pomponiu;Yiren Zhou;Zhao Chen;Ngai-Man Cheung;D. Koh;Aaron Tan
中科院分区：
文献类型：
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作者：
Thanh-Toan Do;Tuan Hoang;Victor Pomponiu;Yiren Zhou;Zhao Chen;Ngai-Man Cheung;D. Koh;Aaron Tan
我们研究了一个完整的移动成像系统的设计，用于黑色素瘤的早期检测。与以往的工作不同，我们将重点放在智能手机拍摄的可见光图像上。我们的设计解决了两大挑战。首先，在控制松散的环境条件下使用智能手机获取的图像可能会受到各种失真，这使得黑色素瘤的检测更加困难。其次，在智能手机上执行的处理受到严格的计算和内存限制。在我们的工作中，我们提出了一个检测系统，该系统经过优化，完全可以在资源受限的智能手机上运行。我们的系统旨在通过结合轻量级的皮肤检测方法和使用两种快速分割方法的分层分割方法来定位皮肤病变。此外，我们研究了一组广泛的图像特征，并提出了新的数字特征来表征皮肤病变。此外，我们提出了一种改进的特征选择算法来确定最终轻量级系统所使用的一小部分区别性特征。此外，我们还研究了人机界面的设计，以了解所提出的系统的可用性和接受性问题。我们对新加坡国家皮肤中心提供的图像数据集(117个良性痣和67个恶性黑色素瘤)进行了广泛的评估，证实了所提出的黑色素瘤检测系统的有效性：89.09%的灵敏度和90%的特异度。
We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Our design addresses two major challenges. First, images acquired using a smartphone under loosely-controlled environmental conditions may be subject to various distortions, and this makes melanoma detection more difficult. Second, processing performed on a smartphone is subject to stringent computation and memory constraints. In our work, we propose a detection system that is optimized to run entirely on the resource-constrained smartphone. Our system intends to localize the skin lesion by combining a lightweight method for skin detection with a hierarchical segmentation approach using two fast segmentation methods. Moreover, we study an extensive set of image features and propose new numerical features to characterize a skin lesion. Furthermore, we propose an improved feature selection algorithm to determine a small set of discriminative features used by the final lightweight system. In addition, we study the human–computer interface (HCI) design to understand the usability and acceptance issues of the proposed system. Our extensive evaluation on an image dataset provided by National Skin Center - Singapore (117 benign nevi and 67 malignant melanoma) confirms the effectiveness of the proposed system for melanoma detection: 89.09% sensitivity at specificity $\geq$90%.