Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

• Published in: International journal for computer assisted radiology and surgery Vol. 12; no. 3; pp. 519 - 528
• Main Authors: Mabu, Shingo, Obayashi, Masanao, Kuremoto, Takashi, Hashimoto, Noriaki, Hirano, Yasushi, Kido, Shoji
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2017; Springer Nature B.V
• Subjects: Algorithms; Cluster Analysis; Clustering; Clusters; Computed tomography; Computer Imaging; Computer Science; Data Mining; Diagnosis, Computer-Assisted - methods; Diseases; Genetic algorithms; Health Informatics; Humans; Image classification; Imaging; Labeling; Labels; Lung - diagnostic imaging; Lung diseases; Lung Diseases - diagnostic imaging; Medicine; Medicine & Public Health; Object recognition; Original Article; Pattern recognition; Pattern Recognition and Graphics; Pattern Recognition, Automated - methods; Radiology; Surgery; Tomography, X-Ray Computed - methods; Unsupervised Machine Learning; Vision; X-rays; Diffuse lung diseases; Clustering; Data mining; Computer-aided diagnosis; Unsupervised learning; Evolutionary computation
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-016-1476-2

Purpose For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifier, a large number of images with correct labels are necessary. It is a very time-consuming and impractical task for radiologists to give correct labels for a large number of CT images. In this paper, to solve the above problem and realize an unsupervised class labeling mechanism without using correct labels, a new clustering algorithm for diffuse lung diseases using frequent attribute patterns is proposed. Methods A large number of frequently appeared patterns of opacities are extracted by a data mining algorithm named genetic network programming (GNP), and the extracted patterns are automatically distributed to several clusters using genetic algorithm (GA). In this paper, lung CT images are used to make clusters of normal and diffuse lung diseases. Results After executing the pattern extraction by GNP, 1,148 frequent attribute patterns were extracted; then, GA was executed to make clusters. This paper deals with making clusters of normal and five kinds of abnormal opacities (i.e., six-class problem), and then, the proposed method without using correct class labels in the training showed 47.7 % clustering accuracy. Conclusion It is clarified that the proposed method can make clusters without using correct labels and has the potential to apply to CAD, reducing the time cost for labeling CT images.