Oliver Faust

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules.

Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization

Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.

A systematic approach to embedded biomedical decision making

An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model.

Systems engineering principles for the design of biomedical signal processing systems

Systems engineering aims to produce reliable systems which function according to specification. In this paper we follow a systems engineering approach to design a biomedical signal processing system. We discuss requirements capturing, specification definition, implementation and testing of a classification system. These steps are executed as formal as possible. The requirements, which motivate the system design, are based on diabetes research. The main requirement for the classification system is to be a reliable component of a machine which controls diabetes. Reliability is very important, because uncontrolled diabetes may lead to hyperglycaemia (raised blood sugar) and over a period of time may cause serious damage to many of the body systems, especially the nerves and blood vessels. In a second step, these requirements are refined into a formal CSP‖ B model. The formal model expresses the system functionality in a clear and semantically strong way. Subsequently, the proven system model was translated into an implementation. This implementation was tested with use cases and failure cases. Formal modeling and automated model checking gave us deep insight in the system functionality. This insight enabled us to create a reliable and trustworthy implementation. With extensive tests we established trust in the reliability of the implementation.

Compressed sampling for heart rate monitoring

For the first time compressed sampling (CS) has been applied to heart rate (HR) measurements. The signals can be reconstructed from samples far below the Nyquist rate with negligible small errors, a sampling reduction of 8 has been demonstrated in the paper. As a result, the bitrate of the CS sampler is half when compared to a normal sampler. A lower bitrate leads to a reduction in power consumption for HR measurement devices.

FORMAL AND MODEL DRIVEN DESIGN OF A HIGH SPEED DATA TRANSMISSION CHANNEL

This paper presents a formal and model driven design approach for a high speed data transmission channel. The formal process algebra communicating sequential processes (CSP) was used to specify the system functionality. Automated model checking established, beyond reasonable doubt, important system properties, such as freedom from deadlock and livelock. Once these properties were established, the functionality was translated into an implementation which realizes high speed data transmission between two independent IC chips. Normal and failure case testing ensured that the implementation complies with the specification. This work shows how formal and model driven design methodologies speed up the process of turning ideas into physical problem solutions. More specifically, formal modeling gave us firm understanding and deep insight of the system functionality which enabled us to implement and to select correct system components.