A New Ensemble Classification System For Fracture Zone Prediction Using Imbalanced Micro-CT Bone Morphometrical Data.

Trabecular bone fractures constitute a major health issue for the modern societies, with the currently established prediction methods of fracture risk, such as bone mineral density (BMD), resulting in errors up to 40%. Fracture-zone prediction based on bone's microstructure has been recently proposed as an alternative prediction method of fracture risk. In this paper, a classification system (CS) for the automatic fracture-zone prediction based on an Ensemble of Imbalanced Learning methods is proposed, following the observation that the percentage of the actual fractured bone area is significantly smaller than the intact bone in the case of a fracture event. The sample is divided into Volumes of Interest (VOIs) of specific size and 29 morphometrical parameters are calculated from each VOI, which serve as input features for the CS in order for it to separate the input patterns in to two classes: fractured and nonfractured. To this end, two well-established Imbalanced Learning methods, namely Random Undersampling and Synthetic Minority Oversampling, and two popular classification algorithms, namely Multilayer Perceptrons and Support Vector Machines, are tested and combined accordingly, to provide the best possible performance on a dataset that contains 45 specimens' pre- and postfailure scans. The best combination is then compared with three well-established Ensembles of Imbalanced Learning methods, namely RUSBoost, UnderBagging and SMOTEBagging. The experimental results clearly show that the proposed CS outperforms the competition, scoring in some occasions more than 90% in G-Mean and Area under Curve metrics. Finally, an investigation on the significance of the various trabecular bone's biomechanical parameters is made using the sequential forward floating selection technique, in order to identify possible biomarkers for fracture-zone prediction.

An Independent Active Contours Segmentation framework for bone micro-CT images.

Micro-CT is an imaging technique for small tissues and objects that is gaining increased popularity especially as a pre-clinical application. Nevertheless, there is no well-established micro-CT segmentation method, while typical procedures lack sophistication and frequently require a degree of manual intervention, leading to errors and subjective results. To address these issues, a novel segmentation framework, called Independent Active Contours Segmentation (IACS), is proposed in this paper. The proposed IACS is based on two autonomous modules, namely automatic ROI extraction and IAC Evolution, which segments the ROI image using multiple Active Contours that evolve simultaneously and independently of one another. The proposed method is applied on a Phantom dataset and on real datasets. It is tested against several established segmentation methods that include Adaptive Thresholding, Otsu Thresholding, Region Growing, Chan-Vese (CV) AC, Geodesic AC and Automatic Local Ratio-CV AC, both qualitatively and quantitatively. The results prove its superior performance in terms of object identification capability, accuracy and robustness, under normal circumstances and under four types of artificially introduced noise. These enhancements can lead to more reliable analysis, better diagnostic procedures and treatment evaluation of several bone-related pathologies, and to the facilitation and further advancement of bone research.

A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia.

Primary and Secondary Polycythemia are diseases of the bone marrow that affect the blood's composition and prohibit patients from becoming blood donors. Since these diseases may become fatal, their early diagnosis is important. In this paper, a classification system for the diagnosis of Primary and Secondary Polycythemia is proposed. The proposed system classifies input data into three classes; Healthy, Primary Polycythemic (PP) and Secondary Polycythemic (SP) and is implemented using two separate binary classification levels. The first level performs the Healthy/non-Healthy classification and the second level the PP/SP classification. To this end, a novel wrapper feature selection algorithm, called the LM-FM algorithm, is presented in order to maximize the classifier's performance. The algorithm is comprised of two stages that are applied sequentially: the Local Maximization (LM) stage and the Floating Maximization (FM) stage. The LM stage finds the best possible subset of a fixed predefined size, which is then used as an input for the next stage. The FM stage uses a floating size technique to search for an even better solution by varying the initially provided subset size. Then, the Support Vector Machine (SVM) classifier is used for the discrimination of the data at each classification level. The proposed classification system is compared with various well-established feature selection techniques such as the Sequential Floating Forward Selection (SFFS) and the Maximum Output Information (MOI) wrapper schemes, and with standalone classification techniques such as the Multilayer Perceptron (MLP) and SVM classifier. The proposed LM-FM feature selection algorithm combined with the SVM classifier increases the overall performance of the classification system, scoring up to 98.9% overall accuracy at the first classification level and up to 96.6% at the second classification level. Moreover, it provides excellent robustness regardless of the size of the input feature subset used.