Classification and feature selection for craniosynostosis

Yang, Shulin; Shapiro, Linda G.; Cunningham, Michael L.; Speltz, Matthew L.; Le, Su-In

doi:10.1145/2147805.2147847

Cited by 13 publications

(16 citation statements)

References 9 publications

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“…Furthermore, as shown in Table III, when all features are used, accuracies reach 99.67%, 99.64%, 98.89% and 99.2% in four cases, respectively. These results show clear improvement over previous work [9]. …”

Section: Discussionsupporting

confidence: 66%

“…Yang et al [9] developed a plane-based retrieval system that produced a variation of Ruiz-Correa’s cranial image. To classify, Yang used logistic regression, L 1 -regularized logistic regression, the fused lasso and the clustering lasso classifiers but the method requires a high-dimensional 100 × 100 distance matrix to achieve mid-90% classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Classifying Craniosynostosis with a 3D Projection-Based Feature Extraction System

Lam

Cunningham²,

Speltz³

et al. 2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

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Craniosynostosis, a disorder in which one or more fibrous joints of the skull fuse prematurely, causes skull deformity and is associated with increased intracranial pressure and developmental delays. Although clinicians can easily diagnose craniosynostosis and can classify its type, being able to quantify the condition is an important problem in craniofacial research. While several papers have attempted this quantification through statistical models, the methods have not been intuitive to biomedical researchers and clinicians who want to use them. The goal of this work was to develop a general platform upon which new quantification measures could be developed and tested. The features reported in this paper were developed as basic shape measures, both single-valued and vector-valued, that are extracted from a single plane projection of the 3D skull. This technique allows us to process images that would otherwise be eliminated in previous systems due to poor resolution, noise or imperfections on their CT scans. We test our new features on classification tasks and also compare their performance to previous research. In spite of its simplicity, the classification accuracy of our new features is significantly higher than previous results on head CT scan data from the same research studies.

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Section: Discussionsupporting

confidence: 66%

Section: Related Workmentioning

confidence: 99%

Classifying Craniosynostosis with a 3D Projection-Based Feature Extraction System

Lam

Cunningham²,

Speltz³

et al. 2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

Self Cite

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show abstract

“…These include the automatic preoperative classification of the disease type based on skull properties [7][8][9], monitoring of changes in craniosynostosis head shapes before the surgery [7,[10][11][12] and 3D modeling of the skull after the surgical treatment [13,14]. Current researches increasingly pay attention to the estimation of the intracranial volume (ICV).…”

Section: Related Workmentioning

confidence: 99%

The quantitative assessment of the pre- and postoperative craniosynostosis using the methods of image analysis

Fabijańska

Węgliński

2015

Computerized Medical Imaging and Graphics

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“…The index of cranial suture fusion and curvature discrepancy was used to classify the CSO with promising performance. 25 Except for this study, 25 previous methods 7,[20][21][22][23][24]26 considered mainly the shape of the cranial vault while paid little attention to the sutures. Overall, these methods 7,[20][21][22][23][24][25][26] were used to develop automated classification system for CSO rather than sagittal CSO.…”

Section: Introductionmentioning

confidence: 99%

Objective classification system for sagittal craniosynostosis based on suture segmentation

et al. 2015

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Purpose: Spring-assisted surgery is an effective and minimally invasive treatment for sagittal craniosynostosis (CSO). The principal barrier to the advancement of spring-assisted surgery is the patient-specific spring selection. The selection of spring force depends on the suture involved, subtypes of sagittal CSO, and age of the infant, among other factors. Clinically, physicians manually judge the subtype of sagittal CSO patients based on their CT image data, which may cause bias from different clinicians. An objective system would be helpful to stratify the sagittal CSO patients and make spring choice less subjective. Methods: The authors developed a novel informatics system to automatically segment and characterize sutures and classify sagittal CSO. The proposed system is composed of three phases: preprocessing, sutures segmentation, and classification. First, the three-dimensional (3D) skull was extracted from the CT images and aligned with the symmetry of the cranial vault. Second, a "hemispherical projection" algorithm was developed to transform 3D surface of the skull to a polar two-dimensional plane. Through the transformation, an "effective" projected region can be obtained to enable easy segmentation of sutures. Then, the different types of sutures, such as coronal sutures, lambdoid sutures, sagittal suture, and metopic suture, obtained from the segmented sutures were further identified by a dual-projection technique of the midline of the sutures. Finally, 108 quantified features of sutures were extracted and selected by a proposed multiclass feature scoring system. The sagittal CSO patients were classified into four subtypes: anterior, central, posterior, and complex with the support vector machine approach. Fivefold cross validation (CV) was employed to evaluate the capability of selected features in discriminating the four subtypes in 33 sagittal CSO patients. Receiver operating characteristics (ROC) curves were used to assess the robustness of the developed system. Results: The segmentation results of the proposed method were clinically acceptable for the qualitative evaluation. For the quantitative evaluation, the fivefold CV accuracy of the classification for the four subtypes was 72.7%. This classification system was reliable with the area under curve (in ROC analysis) being greater than 0.8 for four two-class problems. Conclusions: The proposed hemispherical projection algorithm based on backtracking search can successfully segment sutures of the cranial vault. The classification system can also offer a desirable performance. As a result, the proposed segmentation and classification system is expected to bring insights into clinic research and the selection of the spring force to facilitate widespread application 5545

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Classification and feature selection for craniosynostosis

Cited by 13 publications

References 9 publications

Classifying Craniosynostosis with a 3D Projection-Based Feature Extraction System

Classifying Craniosynostosis with a 3D Projection-Based Feature Extraction System

The quantitative assessment of the pre- and postoperative craniosynostosis using the methods of image analysis

Objective classification system for sagittal craniosynostosis based on suture segmentation

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