Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA

• Published in: Procedia computer science 2020, Vol.167, p.1722-1731
• Main Authors: Priyanka, Kumar, Dharmender
• Format: Article
• Language: English
• Subjects: Feature reduction; Gray Level Co-occurrence Matrix; Pre-processing; Principal Component Analysis
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2020.03.382

Medical imaging applications in hospitals and laboratories have shown benefits in visualizing patient’s body for diagnosis and treatment of disease. Ultrasound (US) is considered as safest medical imaging technique and is therefore used extensively in medical and healthcare using computer aided system. However presence of some artifacts due to patient mobility and equipment limitations makes diagnosis of these US images difficult. There is need for some pre-processing methods to improve quality of images for the purpose of classification and segmentation while preserving pixels of interest. These pixels contain information about images known as image features which forms the data model for classification. So, feature extraction and selection is important phase in classification step of diagnostic system. Keeping this in mind, this study focuses on preprocessing and feature extraction and selection phase of ultrasound images of kidney for making a classification model. Four operations cropping, interpolation, rotation and background removal are applied as preprocessing methods to enhance the quality of images and for making diagnosis easy and effective. Afterwards, a number of second order statistical texture features including energy, entropy, homogeneity, correlation, contrast, dissimilarity are generated using GLCM. Finally obtained features are reduced to optimal subset using principal component analysis (PCA). The results show that GLCM in combination with PCA for feature reduction gives high classification accuracy when classifying images using Artificial Neural Network (ANN).