期刊论文详细信息
BMC Bioinformatics
Texture based skin lesion abruptness quantification to detect malignancy
Research
Mustafa Bayraktar1  Sinan Kockara2  Recep Erol2  Tansel Halic2  Sertan Kaya3 
[1] Bioinformatics, UA Little Rock, 72204, Little Rock, AR, USA;Department of Computer Science, UCA, 72034, Conway, AR, USA;HP, 92127, San Diego, CA, USA;
关键词: Pigmented lesions;    Skin lesion;    Level set;    Contour contraction;    Abrupt cutoff;   
DOI  :  10.1186/s12859-017-1892-5
来源: Springer
PDF
【 摘 要 】

BackgroundAbruptness of pigment patterns at the periphery of a skin lesion is one of the most important dermoscopic features for detection of malignancy. In current clinical setting, abrupt cutoff of a skin lesion determined by an examination of a dermatologist. This process is subjective, nonquantitative, and error-prone. We present an improved computational model to quantitatively measure abruptness of a skin lesion over our previous method. To achieve this, we quantitatively analyze the texture features of a region within the lesion boundary. This region is bounded by an interior border line of the lesion boundary which is determined using level set propagation (LSP) method. This method provides a fast border contraction without a need for extensive boolean operations. Then, we build feature vectors of homogeneity, standard deviation of pixel values, and mean of the pixel values of the region between the contracted border and the original border. These vectors are then classified using neural networks (NN) and SVM classifiers.ResultsAs lower homogeneity indicates sharp cutoffs, suggesting melanoma, we carried out our experiments on two dermoscopy image datasets, which consist of 800 benign and 200 malignant melanoma cases. LSP method helped produce better results than Kaya et al., 2016 study. By using texture homogeneity at the periphery of a lesion border determined by LSP, as a classification results, we obtained 87% f1-score and 78% specificity; that we obtained better results than in the previous study. We also compared the performances of two different NN classifiers and support vector machine classifier. The best results obtained using combination of RGB color spaces with the fully-connected multi-hidden layer NN.ConclusionsComputational results also show that skin lesion abrupt cutoff is a reliable indicator of malignancy. Results show that computational model of texture homogeneity along the periphery of skin lesion borders based on LSP is an effective way of quantitatively measuring abrupt cutoff of a lesion.

【 授权许可】

CC BY   
© The Author(s). 2017

【 预 览 】
附件列表
Files Size Format View
RO202311106551122ZK.pdf 2101KB PDF download
【 参考文献 】
  • [1]
  • [2]
  • [3]
  • [4]
  • [5]
  • [6]
  • [7]
  • [8]
  • [9]
  • [10]
  • [11]
  • [12]
  • [13]
  • [14]
  • [15]
  • [16]
  • [17]
  • [18]
  • [19]
  • [20]
  • [21]
  • [22]
  • [23]
  • [24]
  • [25]
  • [26]
  • [27]
  • [28]
  • [29]
  • [30]
  • [31]
  文献评价指标  
  下载次数:1次 浏览次数:1次