A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Abstract

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

A Survey of Multi-Focus Image Fusion Methods

“…Figure 5 shows the image decomposition process of NSCT. [38]. Reprinted/adapted with permission from Ref.…”

Hybrid Intelligent Pattern Recognition Systems for Mass Segmentation and Classification: A Pilot Study on Full-Field Digital Mammograms

“…The implementation of a deep learning algorithm on infrared thermal breast images showed an accuracy of over 99% with AMs and 92.32% without. Du et al [33] presented a procedure that classifies architectural distortion, a feature of breast cancer. Enhancing the images with top-bottom hat and exponential transformation, reducing the noise with NSCT and finding a threshold for the segmentation of the image using the improved PCNN achieved an accuracy of 93.16% and an AUC of 0.93.…”

“…For the classifiers FCM and ANFIS, ROC curves were extracted, helping to extract the area under the ROC curve (AUC) that is an objective index [33]. AUC has values between 0 and 1.…”

“…The criterion to find the best combination of the features is to find the highest accuracy of the system. Once the highest accuracy is found, then the sensitivity and specificity are calculated[41,59].For the classifiers FCM and ANFIS, ROC curves were extracted, helping to extract the area under the ROC curve (AUC) that is an objective index[33]. AUC has values between 0 and 1.…”

“…ROC curve tests different thresholds for classifying the range of the outputs and suggests the best threshold value. The K-means method has as an output only two values if the clusters are two, and the ROC curve cannot be applied to it.The F1 score also provides an objective evaluation of a classifier[30,33].…”