Browsing by Subject "Mutual information"

Now showing 1 - 4 of 4

Open Access
A comprehensive methodology for determining the most informative mammographic features
(2013) Wu, Y.; Alagoz O.; Ayvaci, M.U.S.; Munoz Del Rio, A.; Vanness, D.J.; Woods, R.; Burnside, E.S.
This study aims to determine the most informative mammographic features for breast cancer diagnosis using mutual information (MI) analysis. Our Health Insurance Portability and Accountability Act-approved database consists of 44,397 consecutive structured mammography reports for 20,375 patients collected from 2005 to 2008. The reports include demographic risk factors (age, family and personal history of breast cancer, and use of hormone therapy) and mammographic features from the Breast Imaging Reporting and Data System lexicon. We calculated MI using Shannon's entropy measure for each feature with respect to the outcome (benign/malignant using a cancer registry match as reference standard). In order to evaluate the validity of the MI rankings of features, we trained and tested naïve Bayes classifiers on the feature with tenfold cross-validation, and measured the predictive ability using area under the ROC curve (AUC). We used a bootstrapping approach to assess the distributional properties of our estimates, and the DeLong method to compare AUC. Based on MI, we found that mass margins and mass shape were the most informative features for breast cancer diagnosis. Calcification morphology, mass density, and calcification distribution provided predictive information for distinguishing benign and malignant breast findings. Breast composition, associated findings, and special cases provided little information in this task. We also found that the rankings of mammographic features with MI and AUC were generally consistent. MI analysis provides a framework to determine the value of different mammographic features in the pursuit of optimal (i.e., accurate and efficient) breast cancer diagnosis. © 2013 Society for Imaging Informatics in Medicine.
Open Access
Karşılıklı bilgi ölçütü kullanılarak giyilebilir hareket duyucu sinyallerinin aktivite tanıma amaçlı analizi
(IEEE, 2014-04) Dobrucalı, Oğuzcan; Barshan, Billur
Giyilebilir hareket duyucuları ile insan aktivitelerinin saptanmasında, uygun duyucu yapılanışının seçimi önem taşıyan bir konudur. Bu konu, kullanılacak duyucuların sayısının, türünün, sabitlenecekleri konum ve yönelimin belirlenmesi problemlerini içermektedir. Literatürde konuyla ilgili önceki çalışmalarda araştırmacılar, kendi seçtikleri duyucu yapılanışları ile diğer olası duyucu yapılanışlarını, söz konusu yapılanışlar ile insan aktivitelerini ayırt etme başarımlarına göre karşılaştırmışlardır. Ancak, söz konusu ayırt etme başarımlarının, kullanılan öznitelikler ve sınıflandırıcılara bağlı olduğu yadsınamaz. Bu çalışmada karşılıklı bilgi ölçütü kullanılarak duyucu yapılanışları, duyuculardan kaydedilen ham ölçümlerin zaman uzayındaki dağılımlarına göre belirlenmektedir. Bedenin farklı noktalarında bulunan ivmeölçer, dönüölçer ve manyetometrelerin ölçüm eksenleri arasından, gerçekleştirilen insan aktiviteleri hakkında en çok bilgi sağlayanları saptanmıştır.
Open Access
Object rigidity and reflectivity identification based on motion analysis
(IEEE, 2010) Zang, D.; Schrater P.R.; Doerschner, Katja
Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object. © 2010 IEEE.
Open Access
Video processing algorithms for wildfire surveillance
(2015-05) Günay, Osman
We propose various image and video processing algorithms for wild re surveillance. The proposed methods include; classi er fusion, online learning, real-time feature extraction, image registration and optimization. We develop an entropy functional based online classi er fusion framework. We use Bregman divergences as the distance measure of the projection operator onto the hyperplanes describing the output decisions of classi ers. We test the performance of the proposed system in a wild re detection application with stationary cameras that scan prede ned preset positions. In the second part of this thesis, we investigate di erent formulations and mixture applications for passive-aggressive online learning algorithms. We propose a classi er fusion method that can be used to increase the performance of multiple online learners or the same learners trained with di erent update parameters. We also introduce an aerial wild re detection system to test the real-time performance of the analyzed algorithms. In the third part of the thesis we propose a real-time dynamic texture recognition method using random hyperplanes and deep neural networks. We divide dynamic texture videos into spatio-temporal blocks and extract features using local binary patterns (LBP). We reduce the computational cost of the exhaustive LBP method by using randomly sampled subset of pixels in the block. We use random hyperplanes and deep neural networks to reduce the dimensionality of the nal feature vectors. We test the performance of the proposed method in a dynamic texture database. We also propose an application of the proposed method in real-time detection of ames in infrared videos. Using the same features we also propose a fast wild re detection system using pan-tilt-zoom cameras and panoramic background subtraction. We use a hybrid method consisting of speeded-up robust features and mutual information to register consecutive images and form the panorama. The next step for multi-modal surveillance applications is the registration of images obtained with di erent devices. We propose a multi-modal image registration algorithm for infrared and visible range cameras. A new similarity measure is described using log-polar transform and mutual information to recover rotation and scale parameters. Another similarity measure is introduced using mutual information and redundant wavelet transform to estimate translation parameters. The new cost function for translation parameters is minimized using a novel lifted projections onto convex sets method.