Open AccessHYBRID DISCRETE WAVELET TRANSFORM AND TEXTURE ANALYSIS METHODS FOR FEATURE EXTRACTION AND CLASSIFICATION OF BREAST DYNAMIC THERMOGRAM SEQUENCES
Author(s) -
Khaleel Al-Rababah,
Mas Rina Mustaffa,
Shyamala Doraisamy,
Fatimah Khalid,
Luís Filipe de Pina Júnior
Publication year - 2021
Publication title -
malaysian journal of computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.197
H-Index - 18
ISSN - 0127-9084
DOI - 10.22452/mjcs.sp2021no2.8
Subject(s) - artificial intelligence , computer science , discrete wavelet transform , pattern recognition (psychology) , breast cancer , support vector machine , local binary patterns , feature extraction , wavelet transform , thermography , wavelet , histogram , curvelet , computer vision , cancer , image (mathematics) , infrared , medicine , physics , optics
Breast cancer is a common cancer that hits women causing thousands of casualties every year. A cancerous tumor causes an increase of temperature near the region of the tumor. The heat generated by the temperature transferred to the skin surface. The temperature in the tumor area is warmer than in the healthy area. Detecting breast cancer in early stages can save women’s lives and lower the burden on the cost. Thermography is an imaging technique used for breast cancer detection. A dynamic thermography technique which is used to generate infrared images over a fixed time measured in minutes to detect the difference between the normal and cancerous areas in images. In this research, we propose a methodology to deal with the changes of temperature in patient's breasts by defining a set of efficient features resulted from extraction and reduction of coefficients obtained from breast thermogram images followed by classification. Texture feature methods (Histogram of Oriented Gradients (HOG) and Discrete Curvelet transform) are applied separately using the HH (high-high) and HL (high-low) sub band images of Discrete Wavelet transform (DWT). HOG-based features and Curvelet features are extracted by reducing coefficients’ vectors returned by the two methods. Finally, Support Vector Machine (SVM) binary classifier is used to classify the images to either normal or abnormal. The proposed work has successfully achieved an Accuracy of 98.2%, Sensitivity of 97.7%, and Specificity of 98.2% through empirical studies using dynamic breast thermogram dataset.