Document Type : Research Paper
Authors
1
Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
3
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
4
Department of Electrical and Electronic Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract
Background and Objectives: One of the currently important and widely used research subjects in the healthcare area of cancer patients is the diagnosis procedure of cancer tumors and metastases in magnetic resonance imaging such that it has a high level of accuracy and also be a support for doctors in interpreting and diagnosing medical data. To this aim, a multivariate Hotelling’s T2 control chart is used.
Methods: Using a two-dimensional discrete wavelet transform, some features of the image texture are extracted by using statistical and transform methods. Then, to reduce the data dimensions and feature selection, a genetic algorithm is used. Afterward, two methods including fuzzy c-Means clustering algorithm and a multivariate Hotelling’s T2 control chart are used to diagnose bone marrow metastasis patients.
Results: From 204 bone marrow samples, 76 features are extracted from which six ones are selected and a 204×6 feature vector matrix is generated. Finally, the performance of the proposed two methods is compared. The results show that the diagnosis and accuracy measures of multivariate Hotelling’s T2 control chart are better than the other method.
Conclusions: In the context of cancer, one of the current concerns for healthcare providers is to use non-invasive, short response time, and highly accurate methods in diagnosing tumors and metastases. The proposed method appropriately addresses these requirements.
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