Steepest changes of a probability-based cost function for delineation of mammographic masses: A validation study

Fissures segmentation using surface features: content-based retrieval for mammographic mass using ensemble classifier

RATIONALE AND OBJECTIVES

Accurate classification is critical in mammography computer-aided diagnosis using content-based image retrieval approaches (CBIR CAD). The objectives of this study were to: 1) develop an accurate ensemble classifier based on domain knowledge and a robust feature selection method for CBIR CAD; 2) propose three new features; and 3) assess the performance of the proposed method and new features by using a relatively large imaging data set.

MATERIALS AND METHODS

The data set used in this study consisted of 2114 regions of interest (ROI) extracted from a publicly available image database. The proposed ensemble classifier method we called E-DGA-KNN included four steps. In the first step, 804 ROIs depict masses were divided into five classes according to their boundary types. Then, each class of ROI with an equal number of negative ROIs were put together to create a sub-database. Second, a dual-stage genetic algorithm, which was called DGA, was applied on those five sub-databases for feature selection and weights determination respectively. In the third step, five base K-nearest neighbor (KNN) classifiers were created by using the results of the second step on 2114 ROIs, and five detection scores for a given queried ROI were obtained. Finally, these classifiers are combined to yield a final classification. The performances of the proposed methods were evaluated by using receiver operating characteristic (ROC) analysis. A comparison with eight different methods on the data set was provided which include the stepwise linear discriminative analysis algorithm (SLDA) and particle swarm optimization (PSO) algorithm with KNN classifier.

RESULTS

When four hybrid feature selection methods were applied with single KNN classifier (ie, DGA-KNN, SLDA-WGA-KNN, SLDA-PSO-KNN, GA-PSO-KNN) and the proposed E-DGA-KNN method to the data set, the computed areas under the ROC curve (Az) were 0.8782 ± 0.0080, 0.8675 ± 0.0081, 0.8623 ± 0.0083, 0.8725 ± 0.0079, and 0.8927 ± 0.0073, respectively. If all features and single KNN classifier were used, the Az value was 0.8478 ± 0.0088. Az values were 0.8592 ± 0.0083 and 0.8632 ± 0.0081 when SLDA or GA algorithm used alone.

CONCLUSIONS

In this study, an ensemble classifier based on domain knowledge and a dual-stage feature selection method was proposed. Evaluation results indicated that the proposed method achieved largest value of ROC compared to other algorithms. The proposed method shows better performance and has the potential to improve the performance of CBIR CAD in interpreting and analyzing mammograms.

Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms

PURPOSE

A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations.

METHODS

The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass-conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask.

RESULTS

The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (+/- 0.160) and 0.540 (+/- 0.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation.

CONCLUSIONS

The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.