Current status and future directions of computer-aided diagnosis in mammography

Nishikawa, Robert M.

doi:10.1016/j.compmedimag.2007.02.009

Cited by 167 publications

(121 citation statements)

References 61 publications

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“…The best fitted ellipse may be approached by minimizing the sum of square algebraic distance (2) where N is the number of the scattered points. As recommended by Fitzgibbon et al, 20 the minimization of the least-squares algebraic distances is achieved by solving a generalized eigenvalue system (3) where…”

Section: Iib Ellipse Fittingmentioning

confidence: 99%

An ellipse-fitting based method for efficient registration of breast masses on two mammographic views

Pu¹,

Leader²,

Gur³

2008

Med. Phys.

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When reading mammograms, radiologists routinely search for and compare suspicious breast lesions identified on two corresponding craniocaudal (CC) and mediolateral oblique (MLO) views. Automatically identifying and matching the same true-positive breast lesions depicted on two views is an important step for developing successful multiview based computer-aided detection (CAD) schemes. The authors developed a method to automatically register breast areas and detect matching strips of interest used to identify the matched mass regions depicted on CC and MLO views. The method uses an ellipse based model to fit the breast boundary contour (skin line) and set a local Cartesian coordinate system for each view. One intersection point between the major/minor axis and the fitted ellipse perimeter passed through breast boundary is selected as the origin and the majoraxis and the minoraxis of the ellipse are used as the two axis of the Cartesian coordinate system. When a mass is identified on one view, the scheme computes its position in the local coordinate system. Then, the distance is mapped onto the local coordinate of the other view. At the end of the mapped distance a registered centerline of the matching strip is created. The authors established an image database that includes 200 test examinations each depicting one verified mass visible on the two views. They tested whether the registered centerline identified on another view can be used to locate the matched mass region. The experiments show that the average distance between the mass region centers and the registered centerlines was ±8.3 mm and in 91% of testing cases the registered centerline actually passes through the matched mass regions. A matching strip width of 47 mm was required to achieve 100% sensitivity for the test database. The results demonstrate the feasibility of the proposed method to automatically identify masses depicted on CC and MLO views, which may improve future development of multiview based CAD schemes.

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Section: Iib Ellipse Fittingmentioning

confidence: 99%

An ellipse-fitting based method for efficient registration of breast masses on two mammographic views

Pu¹,

Leader²,

Gur³

2008

Med. Phys.

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“…Both TPF and FPI depend on the parameters of the mass detector as well as on the definitions of TPs and false negatives (FNs), which are discussed in more detail by Nishikawa [25]. A complete FROC curve is obtained by changing the threshold in the mass detector, similarly as was the case with the ROC.…”

Section: Results Of Mass Detectionmentioning

confidence: 99%

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Bator

Nieniewski

2011

J Digit Imaging

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Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF)=0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF=0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI)=2.

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“…Such convex formulation commonly solved using more sophisticated solver such as quadratic programming (QP) [14]. (1) Subject to the following equality constraints…”

Section: Background 21 Least Squares Support Vector Machinementioning

confidence: 99%

“…Presence of MCs is a key to an early detection and diagnosis of breast cancer. These challenges and the nature of MCs lead to unsatisfactory positive predictive value (PPV) of mammography-based breast cancer diagnosis [1]- [2]. Hence, computer aided diagnosis (CADx) technology is very crucial to assist radiologist in reading mammogram, improving the PPV of mammography by providing radiologist with second opinion when double reading stage is not feasible most of the time [1].…”

Section: Introductionmentioning

confidence: 99%

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

Zyout¹

2012

IJCA

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Feature selection and classifier hyper-parameter optimization are important stages of any computer-aided diagnosis (CADx) system for mammography. The optimal selection for shape features, kernel parameter, and classifier regularization constant is crucial to achieve a good generalization and performance of least-squares support vector machines (LSSVMs). This paper presents a morphology-based CADx that uses a computationally attractive and unified scheme for accomplishing the model selection task.A heuristic parameter search based on particle swarm optimization (PSO) not only reduces the dimensionality of the input feature space but also optimizes hyper-parameters of the classifier. The performance of the proposed shape-based CADx including PSO-LSSVM parameter selection method is examined using 60 microcalcification clusters. Using different cross-validation procedures, the proposed PSO-LSSVM demonstrated a good generalization ability by producing classification accuracies higher than 92%. The best classification accuracy of 97% was obtained using the leave-one-out cross-validation procedure. Comparing the performance of PSO-LSSVM with PSO-SVM method that uses conventional SVM formulation, results demonstrated the attractive computational complexity and classification performance of PSO-LSSVM.

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Current status and future directions of computer-aided diagnosis in mammography

Cited by 167 publications

References 61 publications

An ellipse-fitting based method for efficient registration of breast masses on two mammographic views

An ellipse-fitting based method for efficient registration of breast masses on two mammographic views

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least-Squares Support Vector Machine

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