Automatic Quality Evaluation of Digital Mammographic Images Generated with Cdmam Phantom Correlated With the Human Vision - Abstract
Abstract The aim of this work was to develop a software tool that assists in the testing of image quality in mammography, addressing the challenges associated with the subjectivity and time-consuming nature of manual measurements. The software aims to correlate automated readings with the human visual system, eliminating the need for result correction, which is commonly required in many existing studies. To achieve this, a dataset of 46 images acquired from exposures of the phantom CDMAM to five computed radiography (CR) systems was used. The method employed for image quality assessment involved the use of circular correlator filters for detection. The correlation with human vision was based on Weber’s parameters, which describe how the visual system discriminates contrast in digital images. The classification of image disks as visible or not visible was performed using the WEKA (Waikato Environment for Knowledge Analysis) datamining tool in combination with the J48 algorithm, which facilitated the construction of decision tree models. The implementation of decision trees resulted in a software system that aids specialists in image quality assessment. The system provides stable and easily interpretable results, achieving accuracy rates of up to 95%. By automating the assessment process and reducing the dependence on observers, the software enhances the integrity of the evaluation and improves the accuracy of measurements.