Contrast enhancement in dense breast images using the modulation transfer function

Abstract

This work proposes a method aimed at enhancing the contrast in dense breast images in mammography. It includes a new preprocessing technique, which uses information on the modulation transfer function (MTF) of the mammographic system in the whole radiation field. The method is applied to improve the efficiency of a computer-aided diagnosis (CAD) scheme. Seventy-five regions of interest (ROIs) from dense mammograms were acquired in two pieces of equipment (a CGR Senographe 500t and a Philips Mammodiagnost) and were digitized in a Lumiscan 50 laser scanner. A computational procedure determines the effective focal spot size in each region of interest from the measured focal spot in the center for a given mammographic equipment. Using computational simulation the MTF is then calculated for each field region. A procedure that enlarges the high-frequency portion of this function is applied and a convolution between the resulting new function and the original image is performed. Both original and enhanced images were submitted to a processing procedure for detecting clustered microcalcifications in order to compare the performance for dense breast images. ROIs were divided into four groups, two for each piece of equipment-one with clustered microcalcifications and another without microcalcifications. Our results show that in about 10% of the enhanced images more signals were detected when compared to the results for the original dense breast images. This is important because the usual processing techniques used in CAD schemes present poor results when applied to dense breast images. Since the MTF method is a well-recognized tool in the evaluation of radiographic systems, this new technique could be used to associate quality assurance procedures with the processing schemes employed in CAD for mammography.

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

“…Digital mammograms with high spatial resolutions and low noise levels are required for accurate microcalcification detection by a CAD system [16]. For that reason, image processing algorithms for image enhancement and noise reduction have been utilized as preprocessing modules in CAD systems [20][21][22][23]. In this preprocessing step, the image contrast of breast lesions is enhanced, and CAD sensitivity in the detection of subtle cancers is improved.…”

Tumor Vascular Permeability to a Nanoprobe Correlates to Tumor-Specific Expression Levels of Angiogenic Markers

“…In the recent past, dynamic contrast-enhanced imaging (DCE-MRI) has been employed to measure tumor vascular permeability to sub 2 nm scale or 5–10 nm scale contrast agents [18]–[20]. Tumor signal enhancement is influenced by the degree of vascularization, vessel permeability, cellularity and interstitial pressure [21]. However, while these approaches are promising, small contrast agents are relatively promiscuous in their leakiness from vessels and diffuse away quickly requiring the use of mathematical models to correlate the dynamic changes in signal enhancement to the physiologic parameters associated with vascular function [22]–[25].…”

Preprocessing for improving CAD scheme performance for microcalcification detection based on mammography imaging quality parameters

“…Consequently this current work has considered the plane cutting the center of the compressed breast for calculating the distance between those structures and the image plane. In a previous work [10] compressed breast thickness (E/2), as shown in fig. 1(b).…”

“…Among the main developed techniques driven to mammography, the main attention was given to those intended to detect and/or classify microcalcifications [1][2][3][4][5] , suspicious masses and tumors [2,6,7] , besides preprocessing techniques designed to enhance mammographic images contrast [8][9][10][11] . The basic procedures associated to the mammographic image computerized processing generally begin with the original mammograms digitization and the subsequent application of processing techniques on the digital image.…”