Design of Multivariate Hotelling’s T2 Control Chart Based on Medical Images Processing


Mahmood Shahrabi 1 , Amirhossein Amiri ORCID 2 , * , Hamidreza Saligheh Rad 3 , Sedigheh Ghofrani 4

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

How to Cite: Shahrabi M, Amiri A, Saligheh Rad H, Ghofrani S . Design of Multivariate Hotelling’s T2 Control Chart Based on Medical Images Processing, Iran J Radiol. 2019 ; 16(Special Issue):e99146. doi: 10.5812/iranjradiol.99146.


Iranian Journal of Radiology: 16 (Special Issue); e99146
Published Online: December 8, 2019
Article Type: Abstract
Received: October 26, 2019
Accepted: December 8, 2019


Background: In the healthcare area of cancer patients, the diagnosis procedure of cancerous tumors and metastases is a valuable and popular research subject in magnetic resonance imaging. A highly accurate diagnosis procedure can be support for doctors in interpreting and diagnosing medical data.

Methods: To address this subject, we used a two-dimensional discrete wavelet transform. First, some features of the image texture were extracted by statistical and transform methods. Then, a genetic algorithm was used for data reduction and feature selection. Afterward, to diagnose bone marrow metastatic patients, we used two methods including a fuzzy c-Means clustering algorithm and a multivariate Hotelling’s T2 control chart. In this paper, we employed ADC and T1-weighted images of the pelvic region. From 204 bone marrow samples, 76 features were extracted, six of which were selected and a 204 × 6 feature vector matrix was generated. Finally, the performance of the two proposed methods was compared in terms of diagnosis and accuracy measures.

Results: The results showed that the diagnosis (100%) and accuracy (100%) of the multivariate Hotelling’s T2 control chart were better than those of the other method, with a diagnosis of 99.49% and accuracy of 99.51%.

Conclusion: In this paper, instead of classification and clustering methods, for the first time, we used a multivariate control chart with the Hotelling’s T2 statistic for the diagnosis of patients suspected of bone marrow metastasis. Then, using some patient samples, the performance of this phase I control chart was evaluated, and the results showed the validity of the proposed method. The validation results revealed that the accuracy and specificity metrics were better for the multivariate Hotelling’s T2 control chart than for the fuzzy clustering method.

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