Edge Detection of Noisy Abnormal Lung CT Image Based on Combined Wavelet Transform and Canny Operator

The development of computer-aided diagnosis (CAD) has been extended to various medical imaging modalities including computer Tomography (CT), magnetic resonance imaging, nuclear medicine etc. Medical image edge detection is an important work for diagnosis of various lung diseases in thoracic CT images. When detecting the edge based on wavelet transform, the edge detection result is poor because of the noise influence. This paper proposed a new edge detection algorithm based on wavelet transform and canny operator. In the wavelet domain, the low-frequency edges are detected by canny operator, while the high-frequency edges are detected by solving the maximum points of local wavelet coefficient model to restore edges after reducing the noise by wavelet. Then, both sub-images edges are fused according to fusion rules. A noisy CT image of abnormal lung infected by Honeycombing is used to evaluate the performance of algorithms.