Lung Disease Detection Using Frequency Spectrum Analysis
نویسندگان
چکیده
This paper introduces a novel approach for automatic detection of a type of diffuse lung pattern, known as honeycombing, from high resolution computed tomography (HRCT) scans of the lung. The algorithm, which is based on frequency spectrum analysis of the HRCT image, assesses and combines outputs from a pre-defined Gabor filter bank to form a preliminary lesion detection mask. Several morphological filters are then employed to remove noise from the detection mask. The algorithm is applied to a total of 352 images and the outputs are validated against lung HRCT images marked by 2 qualified radiologists. The algorithm achieved a sensitivity of 87.5% and a specificity of 84.4%, which compares favorably with other approaches.
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