Detection of Abnormality in Deterministic Compressive Sensed Breast Thermograms Using Bilateral Asymmetry

Abstract

The increased number of breast cancer cases worldwide necessitates the development of early breast abnormality detection techniques. Thermography serves as a promising imaging modality that can be used as an adjunctive tool with mammography for early breast abnormality detection. It can be particularly useful for breast abnormality detection in developing or underdeveloped countries that have a limited number of medical professionals and low-power processing units for diagnosis. Appropriate compression of breast thermal images reduces the data storage expenses and computational complexity of the algorithms for breast abnormality detection using thermography. Therefore, we are motivated to use deterministic compressive sensing (CS) for the compression of the red-plane extracted from the breast thermograms and detecting breast abnormality in the compressed domain using the compressed red plane. The deterministic CS technique employs a given deterministic binary block diagonal (DBBD) matrix that acts as a low-pass filter and downsampler and preserves the features needed for abnormality detection. We propose a bilateral asymmetry analysis-based breast abnormality detection technique in the compressed domain. A performance analysis of compressed domain breast abnormality detection technique with red-plane thermograms compressed using CS and non-CS compression techniques at different compression ratios (CRs) along with an analysis of computational complexities is presented. A comprehensive analysis of the performance of compressed domain breast abnormality detection is also explored when different types of common medical image noises (Gaussian, salt and pepper, and speckle noise) at different noise levels are present.