We propose a technique for the automated detection of malignant masses in screening mammography. The technique is based on the presence of concentric layers surrounding a focal area with suspicious morphological characteristics and low relative incidence in the breast region. Mammographic locations with high concentration of concentric layers with progressively lower average intensity are considered suspicious deviations from normal parenchyma. The multiple concentric layers (MCLs) technique was trained and tested using the craniocaudal views of 270 mammographic cases with biopsy proven malignant masses from the digital database of screening mammography. One-half of the available cases were used for optimizing the parameters of the detection algorithm. The remaining cases were used for testing. During testing, malignant masses were detected with 92%, 88%, and 81% sensitivity at 5.4, 2.4, and 0.6 false positive marks per image. Testing on 82 normal screening mammograms showed a false positive rate of 5.0, 1.7, and 0.2 marks per image at the previously reported operating points. Furthermore, additional evaluation on 135 benign cases produced a significantly lower detection rate for benign masses (61.6%, 58.3%, and 43.7% at 5.1, 2.8, and 1.2 false positives per image, respectively). Overall, MCL is a promising computer-assisted detection strategy for screening mammograms to identify malignant masses while maintaining the detection rate of benign masses considerably lower.
Cell lines are an important tool in understanding all aspects of cancer growth, development, metastasis, and tumor cell death. There has been a dramatic increase in the number of cell lines and diversity of the cancers they represent; however, misidentification and cross-contamination of cell lines can lead to erroneous conclusions. One method that has gained favor for authenticating cell lines is the use of short tandem repeats (STR) to generate a unique DNA profile. The challenge in validating cell lines is the requirement to compare the large number of existing STR profiles against cell lines of interest, particularly when considering that the profiles of many cell lines have drifted over time and original samples are not available. We report here methods that analyze the variations and the proportional changes extracted from tetra-nucleotide repeat regions in the STR analysis. This technique allows a paired match between a target cell line and a reference database of cell lines to find cell lines that match within a user designated percentage cut-off quality matrix. Our method accounts for DNA instability and can suggest whether the target cell lines are misidentified or unstable.
Previously we presented an unsupervised self-organizing map (SOM) for segmentation of the breast region in screening mammograms. This study improves upon our earlier technique by (1) enhancing the detection of the breast region near the skin line, as well as (2) reducing the computational complexity. Contrary to the initial technique, the improved one exploits global image properties extracted at different scales. These properties were used to both generate the SOM training samples and obtain a preliminary segmentation. Subsequently, a multi-step strategy was implemented to automatically outline a wide band around the skin line for further analysis. This additional step reduces the computational complexity by focusing the analysis on the set of pixels that creates clinically the highest ambiguity. Specifically, the same (already trained) SOM was applied to classify the ambiguous pixels around the skin line. The study was performed on 400 screening mammograms from the digital database for screening mammography (DDSM). Visual examination of the segmentation results confirmed an improvement in the detection of the low-contrast region near the skin line. The performance was consistent regardless of mammographic view and/or breast density. Furthermore, the computational cost of processing can be reduced by up to 80% of the original value.
Computer assisted detection systems (CAD) in mammography incorporate two critical stages: (i) prescreening to localize suspicious regions and (ii) detailed analysis of the regions for false positive reduction. In this work, we present a new technique for automatic detection of suspicious masses for prescreening mammograms. The hypothesis of the proposed technique is that malignant masses manifestate as superimposed concentric layers. Morphological characterization of these layers can form the foundation of an automated scheme for detection of suspicious masses. The study was based on fifty nine screening mammograms from the digital database of screening mammography (DDSM). Overall, the proposed scheme performs at 85.7% sensitivity with an average of 0.53 false positives per image. The scheme targets malignant masses and, thus it can provide a second opinion to radiologists without sending benign masses to unnecessary biopsy.
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