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Öğe Computer aided detection of spina bifida using nearest neighbor classification with curvature scale space features of fetal skulls extracted from ultrasound images(Elsevier, 2015) Konur, Umut; Gurgen, Fikret S.; Varol, Fusun; Akarun, LaleThis paper addresses the problem of detecting the common neural tube defect of spina bifida by a computer aided detection (CAD) system. We propose a Method which extracts the curvature scale space (CSS) features of fetal skull contours viewed in the ultrasound (US) modality and performs nearest neighbor (kNN) classification on those features having the desired properties of invariance with respect to translation, orientation and scale changes, thus improving robustness. The distance between two sets of CSS features, each set corresponding to the description of the contour of a particular skull, is measured as the cost of matching the two sets of CSS features. Such a CAD system may act as a second observer and help experts in prenatal diagnosis. Our data possess absolute and relative rarity. The experiments are performed with two different rare class handling methods and over a range of operating conditions. All experiments are based on a group of settings; associated with using either balanced or unbalanced datasets, employing different types of CSS features and how CSS matching costs are computed. Comparatively evaluating the classification performance of the settings is carried with the aid of the whole-curve metric of area under the receiver operating characteristics (ROC) curve (AUC). Optimal operating conditions for any setting can be identified and some settings reveal advantages over others. The observations indicate that using balanced datasets offers better performance and our proposed version of estimating CSS matching costs is generally superior to the classical method. Furthermore, using enhanced sets of CSS features improves classification accuracy. When classification is performed on balanced data using enhanced CSS features and the matching cost is computed with our proposed technique; one can observe an F-measure of 0.76 along with 70% TP rate (recall), 17% FP rate (false alarms) and 82% precision. (C) 2015 Elsevier B.V. All rights reserved.Öğe A two-view ultrasound CAD system for spina bifida detection using Zernike features(Spie-Int Soc Optical Engineering, 2011) Konur, Umut; Gurgen, Fikret; Varol, FusunIn this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both modalities are available, those decisions can be combined to keep matters more secure. Even more security can be attained by using more than two modalities and base the final decision on all those potential classifiers. Our current system relies on feature extraction from images for cases (for particular patients). The first step is image preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain, which is hopefully more representative for good classification performance. Next, a particular type of feature extraction, which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image modality being used. Either shape or texture information captured by moments may propose useful features. Finally, SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.