Characterisation of focal liver lesions in multi-phase CT images using textural and pathological descriptors
Characterisation of focal liver lesions in multi-phase CT images using textural and pathological descriptors
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DOI：
10.1080/21681163.2022.2156390
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Saeed Moslehi;A. H. Foruzan;Yen-Wei Chen;Hongjie Hu
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文献类型：
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作者：
Saeed Moslehi;A. H. Foruzan;Yen-Wei Chen;Hongjie Hu
摘要利用影像学技术对肝脏局灶性病变进行定性是一种低风险的方法，可帮助医生进行诊断和制定治疗计划。现有技术主要依赖于深度网络的基于纹理的特征或传统的特征提取器技术。然而，这些技术没有充分利用语义属性来改善肿瘤类型的描述。在本文中，我们介绍了病理描述符，包括计算机断层扫描图像中的病变，肿瘤的边界，肿瘤的几何形状，和病变的增强质量。我们提出了一个模糊边缘模型来描述肿瘤的边界质量。肿瘤几何形状的不规则性或均匀性通过评估病灶边界周围的中心部分和边缘来确定。我们将肿瘤的外观与正常肝组织和血池的强度进行比较，以描述其增强行为。该算法标记了123个计算机断层扫描数据，包括五种病变类型，平均准确率为92.7%。这些结果证明了与纹理特征相比，基于病理学的描述符的优越性以及其未来用于区分脑和肺中病变的潜力。此外，来自“非对比”阶段的特征不会改善分类结果。
ABSTRACT Characterisation of Focal Liver Lesions using imaging techniques is a low-risk approach to help physicians in diagnosis and treatment planning. State-of-the-art techniques chiefly rely on texture-based features of deep networks or conventional feature extractor techniques. However, these techniques do not sufficiently exploit the semantic attributes to improve the description of the tumour types. In this paper, we introduce pathologic descriptors to characterise the lesions in Computer Tomography images including, the quality of the tumour’s boundary, the tumour’s geometry, and the lesion’s enhancement. We propose a blurred edge model to characterise the quality of a tumour’s border. The irregularity or homogeneity of a tumour’s geometry is determined by the assessment of the central part and a rim around the lesion’s boundary. We compare the tumour’s appearance with the intensity of normal liver tissue and the blood pools to describe its enhancement behaviour. The proposed algorithm labelled 123 Computer Tomography data consisting of five lesion types and achieved an average accuracy of 92.7%. These results prove the superiority of pathology-based descriptors compared to textural features and their future potential for the discrimination of the lesions in the brain and lung. Moreover, the features from the ‘Non-Contrast’ phase do not improve classification results.