Local image registration a comparison for bilateral registration mammography

Celaya-Padilaa, José M.; Rodríguez-Rojas, Juan; Treviño, Víctor; Tamez-Peña, José G.

doi:10.1117/12.2035516

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…The left breast mammographic images were mirrored and then registered to their corresponding right breast images. The registration used a standard image registration framework with a B-Spline multiresolution transformation optimizing the Mattes mutual information metric [ 28 , 29 ]. The B-Spline transform T ( x , y ) deforms an image by modifying a mesh of control points which pinpoints to the moving image to maximize of a similarity measure.…”

Section: Methodsmentioning

confidence: 99%

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Celaya-Padilla

Martínez-Torteya

Rodríguez-Rojas

et al. 2015

BioMed Research International

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Mammography is the most common and effective breast cancer screening test. However, the rate of positive findings is very low, making the radiologic interpretation monotonous and biased toward errors. This work presents a computer-aided diagnosis (CADx) method aimed to automatically triage mammogram sets. The method coregisters the left and right mammograms, extracts image features, and classifies the subjects into risk of having malignant calcifications (CS), malignant masses (MS), and healthy subject (HS). In this study, 449 subjects (197 CS, 207 MS, and 45 HS) from a public database were used to train and evaluate the CADx. Percentile-rank (p-rank) and z-normalizations were used. For the p-rank, the CS versus HS model achieved a cross-validation accuracy of 0.797 with an area under the receiver operating characteristic curve (AUC) of 0.882; the MS versus HS model obtained an accuracy of 0.772 and an AUC of 0.842. For the z-normalization, the CS versus HS model achieved an accuracy of 0.825 with an AUC of 0.882 and the MS versus HS model obtained an accuracy of 0.698 and an AUC of 0.807. The proposed method has the potential to rank cases with high probability of malignant findings aiding in the prioritization of radiologists work list.

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Section: Methodsmentioning

confidence: 99%

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Celaya-Padilla

Martínez-Torteya

Rodríguez-Rojas

et al. 2015

BioMed Research International

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“…Additionally, a series of image transformations are incorporated to enhance different characteristics of the breast in the mammograms. This work is based on and follows previous efforts [30][31][32]. Figure 2 shows how the bilateral asymmetry information was incorporated into the CADx system.…”

Section: Methodsmentioning

confidence: 99%

“…There are many approaches to deal with medical imaging registration, the most recent comparison of algorithms based on a retrospective evaluation was published by West et al [40], but it was constrained to do intrapatient rigid registration. Also recently, Diez et al [28] and Celaya-Padilla et al [30] compared registration algorithms with breast images as a source, and both concluded that the B-Splines approach was the most consistent.…”

Section: Registrationmentioning

confidence: 99%

Incorporating Breast Asymmetry Studies into CADx Systems

Padilla¹,

Valdivia²,

Tejada³

et al. 2017

New Perspectives in Breast Imaging

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Breast cancer is one of the global leading causes of death among women, and an early detection is of uttermost importance to reduce mortality rates. Screening mammograms, in which radiologists rely only on their eyesight, are one of the most used early detection methods. However, characteristics, such as the asymmetry between breasts, a feature that could be very difficult to visually quantize, is key to breast cancer detection. Due to the highly heterogeneous and deformable structure of the breast itself, incorporating asymmetry measurements into an automated detection system is still a challenge. In this study, we proposed the use of a bilateral registration algorithm as an effective way to automatically measure mirror asymmetry. Furthermore, this information was fed to a machine learning algorithm to improve the accuracy of the model. In this study, 449 subjects (197 with calcifications, 207 with masses, and 45 healthy subjects) from a public database were used to train and evaluate the proposed methodology. Using this procedure, we were able to independently identify subjects with calcifications (accuracy = 0.825, AUC = 0.882) and masses (accuracy = 0.698, AUC = 0.807) from healthy subjects.

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“…On the other hand, it is important to mention that in this work it was proposed the local image registration method because, according to the work of Celaya et al [42] , where a comparison for bilateral registration mammography was performed, comparing different types of registration in two images that theoretically should present the same characteristics but, due to the biological phenomena, they presented heterogenous tissue, demonstrating that for this type of problem, which is very similar with the problem presented here, the results obtained using this method were the most robust, which allows to confirm the hypothesis that the greater the error in the registry, the greater the deformation in one of the knees.…”

Section: Resultsmentioning

confidence: 99%

“…Initially, the left knee image was reflected and then co-registered with the image of the right knee corresponding. An algorithm of B-Spline multi resolution had the purpose of optimizing the Mattes mutual data measures in the bilateral image registration [29,30] . Then, a deformable B-Spline transform was used, this process based its function in the transformation of an image adjusting control points of a net in base on a similarity measure maximization, this method usually avoids local minimal in the parameter search space and decreases computational time [31,32] .…”

Section: Image Registrationmentioning

confidence: 99%

Image Registration Measures and Chronic Osteoarthritis Knee Pain Prediction: Data from the Osteoarthritis Initiative

Galván-Tejada

Tejada

López-Monteagudo

et al.

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Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it's proposed a CADx system that quantify the bilateral similarity of the patient's knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it's proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years' follow-up.

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Local image registration a comparison for bilateral registration mammography

Cited by 7 publications

References 13 publications

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Incorporating Breast Asymmetry Studies into CADx Systems

Image Registration Measures and Chronic Osteoarthritis Knee Pain Prediction: Data from the Osteoarthritis Initiative

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