Evaluating the Effect of Image Preprocessing on an Information-Theoretic CAD System in Mammography

Abstract

Rationale and Objectives-In our earlier studies we reported an evidence-based Computer Assisted Decision (CAD) system for location-specific interrogation of mammograms. A contentbased image retrieval framework with information theoretic (IT) similarity measures serves as the foundation for this system. Specifically, the normalized mutual information (NMI) was shown to be the most effective similarity measure for reduction of false positive marks generated by other, prescreening mass detection schemes. The objective of this work was to investigate the importance of image filtering as a possible preprocessing step in our IT-CAD system. Materials and Methods-Different filters were applied, each one aiming to compensate for known limitations of the NMI similarity measure. The study was based on a region-of-interest database that included true masses and false positive regions from digitized mammograms. Results-ReceiverOperating Characteristics (ROC) analysis showed that IT-CAD is affected slightly by image filtering. Modest, yet statistically significant performance gain was observed with median filtering (overall ROC area index Az improved from 0.78 to 0.82). However, Gabor filtering improved performance for the high sensitivity portion of the ROC curve where a typical false positive reduction scheme should operate (partial ROC area index 0.90 A z improved from 0.33 to 0.37). Fusion of IT-CAD decisions from different filtering schemes markedly improved performance (A z =0.90 and 0.90 A z =0.55). At 95% sensitivity, the system's specificity improved by 36.6%.Conclusion-Additional improvement in false positive reduction can be achieved by incorporating image filtering as a preprocessing step in our information-theoretic CAD system.

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

“…However, mammography is still far from being ideal, with its sensitivity only ranging from 70 % to 90 % [7]. The clinical significance of early breast cancer diagnosis and a clear need to reduce false-negative rate of screening mammography have motivated the development of computer-aided detection (CADe) systems for decision support [8][9][10][11][12][13][14][15][16][17]. These systems typically involve a series of steps; first applying a variety of image preprocessing to reduce the noise and/or to enhance suspicious structures in the image and then using morphological and textural analysis to better differentiate these structure between true positives and false positives.…”

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Adaptive learning for relevance feedback: Application to digital mammography

“…Therefore, a different weight was assigned for each stored image. Tourassi et al 29 presented several filtering techniques as preprocessing steps for improving the performance of an information-theoretic CAD ͑IT-CAD͒ system. In this approach, a ROI database was used, which included true masses and false-positive regions from digitized mammograms and the filters were selected to complement the similarity metric in the IT-CAD system.…”

“…[20][21][22][23][24][25][26][27][28][29][30][31] This was initiated by the pioneering work of Swett et al, 23 who developed a computer-based expert system called MAMMO/ICON for automated mammographic image retrieval based on speech recognition technology using findings in the textual report or in the dictation. Mazurowski et al 28 proposed an optimization framework for improving a case-based computer-aided decision ͑CAD͒ system that was developed for the classification of regions of interests ͑ROIs͒ in mammograms.…”