A Novel Statistical Based Methodology for the Feature Extraction of both MRI and CT Images
L. Malliga
L. Malliga, Professor, Department of ECE, Malla Reddy Engineering College for Women, Secundrabad (Telangana), India.
Manuscript received on 30 September 2019 | Revised Manuscript received on 12 November 2019 | Manuscript Published on 22 November 2019 | PP: 1788-1794 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13400986S319/19©BEIESP | DOI: 10.35940/ijeat.F1340.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Design of a common methodology for the diagnosis of different image types is the objective of the work presented in this paper. The software is developed and can be used to diagnose MRI and CT images by the laboratory technician The paper presents a statistical method for the feature extraction of MRI and CT images. About thirteen features are extracted using the methodology adopted for the proposed work. The thirteen features are based on texture, shape and intensity. The data dimensionality is reduced using the Principle Component Analysis (PCA). The common features are extracted using the Gray level co-occurrence matrix method. The software is developed using MATLAB and PYTHON for IoT support.
Keywords: MRI, CT, Principle Component Analysis, GLCM, Texture, Shape, Intensity, Dimensionality Reduction.
Scope of the Article: Image Security