Suspicious Lesion Detection in Mammograms using Undecimated Wavelet Transform and Adaptive Thresholding

Abstract

Mammographic screening is the most effective procedure for the early detection of breast cancers. However, typical diagnostic signs such as masses are difficult to detect as mammograms are low-contrast noisy images. This paper proposes a systematic method for the detection of suspicious lesions in digital mammograms based on undecimated wavelet transform and adaptive thresholding techniques. Undecimated wavelet transform is used here to generate a multiresolution representation of the original mammogram. Adaptive global and local thresholding techniques are then applied to segment possible malignancies. The segmented regions are enhanced by using morphological filtering and seeded region growing. The proposed method is evaluated on 120 images of the Mammographic Image Analysis Society (MIAS) Mini Mammographic database, that include 89 images having in total 92 lesions. The experimental results show that the proposed method successfully detects 87 of the 92 lesions, performing with a sensitivity of 94.56% at 0.8 false positives per image (FPI), which is better than earlier reported techniques. This shows the effectiveness of the proposed system in detecting breast cancer in early stages.