Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-03-2017 | Original Article

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

Authors: Shingo Mabu, Masanao Obayashi, Takashi Kuremoto, Noriaki Hashimoto, Yasushi Hirano, Shoji Kido

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 3/2017

Abstract

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.

Clustering accuracy = (164+829+119+257+540+108+282+301+935+546+126+54+553)/10094 = 0.477.

Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference, Santiago, Chile, pp 487–499

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

Chen H, Xu Y, Ma Y, Ma B (2010) Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation. Acad Radiol 17(5):595–602CrossRefPubMed

Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Boston

Gonzales E, Mabu S, Taboada K, Shimada K, Hirasawa K (2010) Efficient pruning of class association rules using statistics and genetic relation algorithm. J Control Measurement Syst Integr 3(5):336–345CrossRef

Kim KG, Goo JM, Kim JH, Lee HJ, Min BG, Bae KT, Im JG (2005) Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience. Radiology 237(2):657–661CrossRefPubMed

Kuwahara M, Kido S, Shouno H (2009) Classification of patterns for diffuse lung diseases in thoracic ct images by adaboost algorithm. In: Proceedings of SPIE, medical imaging, computer-aided diagnosis. 7260:37–1–8

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444CrossRefPubMed

Mabu S, Hirasawa K, Hu J (2007) A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evol Comput 15(3):369–398CrossRefPubMed

10.

Machine Learning Group at the University of Waikato (2015) Waikato environment for knowledge analysis, open source project for machine learning. www.cs.waikato.ac.nz/ml/weka/

11.

Miranda GHB, Felipe JC (2015) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 64:334–346CrossRefPubMed

12.

Quinlan JR (1993) C4 5: programs for machine learning, vol 1. Morgan kaufmann, Burlington

13.

Rawat J, Singh A, Bhadauria H, Virmani J (2015) Computer aided diagnostic system for detection of leukemia using microscopic images. Procedia Comput Sci 70:748–756. In: Proceedings of the 4th international conference on eco-friendly computing and communication systems

14.

Rui X, Hirano Y, Tachibana R, Shoji K (2013) A bag-of-features approach to classify six types of pulmonary textures on high-resolution computed tomography. IEICE Trans Inf Syst 96(4):845–855

15.

Shimada K, Hirasawa K, Hu J (2006) Genetic network programming with acquisition mechanisms of association rules. J Adv Comput Intell Intell Inform 10(1):102–111

16.

Wedashwara W, Mabu S, Obayashi M, Kuremoto T (2016) Combination of genetic network programming and knapsack problem to support record clustering on distributed databases. Expert Syst Appl 46:15–23CrossRef

17.

Zhao W, Xu R, Hirano Y, Tachibana R, Kido S (2013) Classification of diffuse lung diseases patterns by a sparse representation based method on hrct images. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 5457–5460

Title: Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns
Authors: Shingo Mabu
Masanao Obayashi
Takashi Kuremoto
Noriaki Hashimoto
Yasushi Hirano
Shoji Kido
Publication date: 01-03-2017
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 3/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1476-2

At a glance: The STEP trials

Springer Medicine

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 3/2017

Haustral loop extraction for CT colonography using geodesics

Breast ultrasound image segmentation: a survey

Semiautomatic classification of acetabular shape from three-dimensional ultrasound for diagnosis of infant hip dysplasia using geometric features

MITK-OpenIGTLink for combining open-source toolkits in real-time computer-assisted interventions

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging

Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images