Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), R. Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, SP, Brazil.
Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), R. Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, SP, Brazil.
Comput Biol Med. 2017 Dec 1;91:135-147. doi: 10.1016/j.compbiomed.2017.10.012. Epub 2017 Oct 16.
Non-Hodgkin lymphomas are a health problem that affects over 70,000 people per year in the United States alone. The early diagnosis and the identification of this lymphoma are essential for an effective treatment. The classification of non-Hodgkin lymphomas is a task that continues to rank as one of the main challenges faced by hematologists, pathologists, as well as in the producing of computer vision methods due to its inherent complexity. In this paper, we present a new method to quantify and classify tissue samples of non-Hodgkin lymphomas based on the percolation theory. The method consists of associating multiscale and multidimensional approaches in order to divide the image into smaller regions and then verifying color similarity between pixels. A cluster labeling algorithm was applied to each region of interest to obtain the values for the number of clusters, occurrence of percolation and coverage ratio of the largest cluster. The method was tested on different classifiers aiming to differentiate three different groups of non-Hodgkin lymphomas. The obtained results (AUC rates between 0.940 and 0.993) were compared to those provided by methods consolidated in the Literature, which indicates that the percolation theory is a suitable approach for identifying these three classes of non-Hodgkin lymphomas, those being: mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia.
非霍奇金淋巴瘤是一个健康问题，仅在美国每年就有超过 70000 人受到影响。早期诊断和识别这种淋巴瘤对于有效的治疗至关重要。非霍奇金淋巴瘤的分类是血液学家、病理学家以及计算机视觉方法的研究人员面临的主要挑战之一，这是由于其固有的复杂性所致。在本文中，我们提出了一种基于渗流理论的新方法来量化和分类非霍奇金淋巴瘤的组织样本。该方法包括结合多尺度和多维方法，以便将图像划分为更小的区域，然后验证像素之间的颜色相似性。然后对每个感兴趣的区域应用聚类标记算法，以获得聚类数量、渗流发生和最大聚类覆盖率的值。该方法在不同的分类器上进行了测试，旨在区分三种不同类型的非霍奇金淋巴瘤。所得结果（AUC 率在 0.940 和 0.993 之间）与文献中已有的方法进行了比较，这表明渗流理论是识别这三种非霍奇金淋巴瘤（套细胞淋巴瘤、滤泡性淋巴瘤和慢性淋巴细胞白血病）的一种合适方法。
