Morphological concentric layer analysis for automated detection of suspicious masses in screening mammograms

We present a mammographic computer aided diagnosis (CAD) system, which uses an adaptive level set segmentation method (ALSSM), which segments suspicious masses in the polar domain and adaptively adjusts the border threshold at each angle to provide high-quality segmentation results. The primary contribution of this paper is the adaptive speed function for controlling level set segmentation. To assess the efficacy of the system, 60 relatively difficult cases (30 benign, 30 malignant) from the Digital Database of Screening Mammography (DDSM) are analyzed. The segmentation efficacy is analyzed qualitatively via visual inspection and quantitatively via the area under the receiver operating characteristics (ROC) curve (AZ) and classification accuracies. For the ALSSM, the best results are 87% overall accuracy, A(Z)=0.9687 with 28/30 malignant cases detected. The qualitative and quantitative results show that the ALSSM provides excellent segmentation and classification results and compares favorably to previous CAD systems in the literature which also used the DDSM database.

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“…Therefore there are 864 = 144 x 6 features. [14] 0.92 TPF @ 1.33 FPPI te Brake et al [19] 0.90 TPF @ 1.0 FPPI Eltonsy et al [21] 85.7% TPF…”

Section: Methodsmentioning

confidence: 97%

Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation

Ball

Bruce

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…The suspicious candidates that survive the false-positive reduction stage serve as visual cues to the radiologist. Morphologic concentric layer analysis is a technique we developed to prescreen mammograms by creating a topological map of the breast region [16]. The underlying hypothesis of the technique is that cancer infiltration causes parenchymal changes that manifestate mammographically in concentric layers of successively changing pixel intensity.…”

Section: Overview Of the Mass Detection Problemmentioning

confidence: 99%

Automated detection of mammographic masses: preliminary assessment of an information-theoretic CAD scheme for reduction of false positives

et al. 2005

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The purpose of this work was to evaluate an information-theoretic computer-aided detection (CAD) scheme for improving the specificity of mass detection in screening mammograms. The study was based on images from the Lumisys set of the Digital Database for Screening Mammography (DDSM). Initially, the craniocaudal views of 49 DDSM mammograms were analyzed using an automated detection algorithm developed to prescreen mammograms. The prescreening algorithm followed a morphological concentric layer analysis and resulted in 319 false positive detections at 92% sensitivity. These 319 suspicious yet normal regions were extracted for further analysis with our information-theoretic CAD scheme. Our scheme follows a knowledge-based decision strategy. The strategy relies on information theoretic principles for similarity assessment between a query case and a knowledge databank of cases with known ground truth. Receiver Operating Characteristic (ROC) analysis was performed to determine how well the CAD scheme can discriminate the false positive regions from 681 true masses. The overall ROC area index of the information-theoretic CAD system was 0.75±0.02. At 97%, 95%, and 90% sensitivity, the system eliminated safely 20%, 30%, and 42% of the previously identified false positives respectively. Thus, information-theoretic CAD analysis can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity.

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“…Where, in the original algorithm the technique used to split regions at only the highest granule intervals as a basic step for any further investigation figure 1(d) represent the isolation of regions at certain granule interval before detecting candidate seeds. The detail of each step represented in [33]. In this study, and as the algorithm depends on certain decision parameters at each modular level, we analyzed those decision parameters to investigate the respective decision parameters that discriminate between the FPI detected seeds and the TPV detected seeds.…”

Section: B Extended Methodologymentioning

confidence: 99%

“…The original CAD algorithm was presented in [33]. The main step in the process is the identification of a candidate region for multiple concentric layer analysis.…”

Section: B Extended Methodologymentioning

confidence: 99%

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Error investigation of models for improved detection of masses in screening mammography

Eltonsy

Essock-Burns²,

Tourrasi³

et al.

Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.

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This study analyzes the performance of a computer aided detection (CAD) scheme for mass detection in mammography. We investigate the trained parameters of the detection scheme before any further testing. We use an extended version of a previously reported mass detection scheme. We analyze the detection parameters by using linear canonical discriminants (LCD) and compare results with logistic regression and multi layer perceptron Neural Network models. Preliminary results suggest that regression and multi layer perceptron Neural Network showed the best Receiver Operator Characteristics (ROC). The LCD analysis predictive function showed that the trained CAD scheme performance can maintain 99.08% sensitivity (108/109) with false positive rate (FPI) of 8 per image with ROC Az= 0.74±0.01. The regression and the multi layer perceptron Neural Network ROC analysis showed stronger backbone for the CAD algorithm viewing that the extended CAD scheme can operate at 96% sensitivity with 5.6 FPI per image. These preliminary results suggest that further logic to reduce FPI is needed for the CAD algorithm to be more predictive.

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Morphological concentric layer analysis for automated detection of suspicious masses in screening mammograms

Cited by 4 publications

References 17 publications

Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation

Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation

Automated detection of mammographic masses: preliminary assessment of an information-theoretic CAD scheme for reduction of false positives

Error investigation of models for improved detection of masses in screening mammography

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