Ferreira de Lucena David Jones, Ferreira Junior José Raniery, Machado Aydano Pamponet, Oliveira Marcelo Costa
Laboratory of Telemedicine and Medical Informatics - HUPAA/UFAL/EBSERH, Computing Institute - Federal University of Alagoas -, Maceio, Brazil.
BMC Med Inform Decis Mak. 2016 Jul 21;16 Suppl 2(Suppl 2):79. doi: 10.1186/s12911-016-0313-4.
Cancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules.
In this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes.
The results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved.
Based on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.
癌症是一种以异常细胞不受控制地生长为特征的疾病，这些异常细胞会侵入邻近组织并将其破坏。肺癌是全球癌症相关死亡的主要原因，其诊断对于专家来说是一项复杂的任务，并且在医学图像解读过程、肺结节检测和分类方面存在一些重大挑战。为了帮助专家早期诊断肺癌，必须将计算机辅助技术融入到成像解读和肺结节分类过程中。基于内容的图像检索（CBIR）方法被认为是一种有前景的计算机辅助诊断技术，有望为放射科医生的图像解读提供第二种观点。然而，CBIR存在一些局限性：图像特征提取过程和适当的相似性度量。CBIR系统的效率取决于计算可能与病例相似性分析相关的图像特征。当专家对结节进行分类时，他们会得到来自检查、图像等信息的支持。但每个信息在关于结节恶性程度的决策中或多或少都有一定权重。因此，找到一种衡量权重的方法可以通过为最能表征结节的属性分配更高的权重来改进图像检索过程。
在此背景下，本研究的目的是提出一种基于局部学习自动计算属性权重的方法，以反映图像检索过程中的解读。该过程由两个阶段组成，这两个阶段依次循环执行：评估阶段和训练阶段。在每次迭代中，根据检索到的结节调整权重。经过几次迭代后，有可能得到一组优化相似结节恢复的属性权重。
更新权重后取得的结果很有前景，因为与在相似性度量中不关联属性权重的测试相比，在检索良性和恶性结节时，平均精度分别提高了10%至6%，召回率为25%。我们取得的最佳结果是，直到检索出第30个肺癌结节时，平均精度值超过100%。
基于这些结果，将加权编辑距离（WED）应用于使用的三个向量属性（3D TA、3D MSA和InV），通过该过程调整权重，始终比使用编辑距离（ED）取得更好的结果。与使用ED相比，使用权重后精度平均提高了17.3%。
