Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images
Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images
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DOI：
10.1088/1361-6560/aaa3af
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发表时间：
2018-02-01
影响因子：
3.5
通讯作者：
Lima, Carlos S.
中科院分区：
文献类型：
--
作者：
Freitas, Nuno R.;Vieira, Pedro M.;Lima, Carlos S.
膀胱镜图像的正确分类取决于口译员的经验。膀胱癌是一种常见的肿瘤，只能通过组织活检来确诊，因此，肿瘤的自动识别在早期诊断和准确性方面起着重要作用。据我们所知，使用白色光膀胱镜图像诊断膀胱肿瘤迄今尚未报道。本文提出了一种基于纹理分析的膀胱肿瘤诊断方法，假设肿瘤组织纹理发生变化。正如科学界所广泛接受的，纹理信息更多地存在于可以通过使用离散小波变换（DWT）来选择的中高频范围中。通过使用自动分割可以改善肿瘤增强，因为在理想条件下避免了与正常组织的混合。本文提出的分割模块利用小波分解树来丢弃较差的纹理信息，使得所提出的算法分割和分类的两个步骤都对纹理有相同的关注点。多层感知器和支持向量机与分层十倍交叉验证程序用于分类目的，通过使用色调饱和度值（HSV），红，绿，蓝，和CIELab颜色空间。通过使用基于DWT的预处理和分类步骤，获得了关于HSV颜色的91%的灵敏度和92.9%的特异性的性能。所提出的方法可以取得良好的性能，识别膀胱肿瘤帧。这些有前途的结果打开了更深入的研究，该算法在计算机辅助诊断的适用性的路径。
Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.