An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Automatic classification of MR scans in Alzheimer's disease.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Automatic selection of tidal volume, respiratory frequency and minute ventilation in intubated ICU patients as start up procedure for closed-loop controlled ventilation.

OBJECTIVE: Before a patient can be connected to a mechanical ventilator, the controls of the apparatus need to be set up appropriately. Today, this is done by the intensive care professional. With the advent of closed loop controlled mechanical ventilation, methods will be needed to select appropriate start up settings automatically. The objective of our study was to test such a computerized method which could eventually be used as a start-up procedure (first 5-10 minutes of ventilation) for closed-loop controlled ventilation. DESIGN: Prospective Study. SETTINGS: ICU's in two adult and one children's hospital. PATIENTS: 25 critically ill adult patients (age > or = 15 y) and 17 critically ill children selected at random were studied. INTERVENTIONS: To stimulate 'initial connection', the patients were disconnected from their ventilator and transiently connected to a modified Hamilton AMADEUS ventilator for maximally one minute. During that time they were ventilated with a fixed and standardized breath pattern (Test Breaths) based on pressure controlled synchronized intermittent mandatory ventilation (PCSIMV). MEASUREMENTS AND MAIN RESULTS: Measurements of airway flow, airway pressure and instantaneous CO2 concentration using a mainstream CO2 analyzer were made at the mouth during application of the Test-Breaths. Test-Breaths were analyzed in terms of tidal volume, expiratory time constant and series dead space. Using this data an initial ventilation pattern consisting of respiratory frequency and tidal volume was calculated. This ventilation pattern was compared to the one measured prior to the onset of the study using a two-tailed paired t-test. Additionally, it was compared to a conventional method for setting up ventilators. The computer-proposed ventilation pattern did not differ significantly from the actual pattern (p > 0.05), while the conventional method did. However the scatter was large and in 6 cases deviations in the minute ventilation of more than 50% were observed. CONCLUSIONS: The analysis of standardized Test Breaths allows automatic determination of an initial ventilation pattern for intubated ICU patients. While this pattern does not seem to be superior to the one chosen by the conventional method, it is derived fully automatically and without need for manual patient data entry such as weight or height. This makes the method potentially useful as a start up procedure for closed-loop controlled ventilation.

An active contour-based atlas registration model applied to automatic subthalamic nucleus targeting on MRI: method and validation.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Automatic prostate segmentation in cone-beam computed tomography images using rigid registration.

We propose to evaluate automatic three-dimensional gray-value rigid registration (RR) methods for prostate localization on cone-beam computed tomography (CBCT) scans. In total, 103 CBCT scans of 9 prostate patients have been analyzed. Each one was registered to the planning CT scan using different methods: (a) global RR, (b) pelvis bone structure RR, (c) bone RR refined by local soft-tissue RR using the CT clinical target volume (CTV) expanded with a 1, 3, 5, 8, 10, 12, 15 or 20-mm margin. To evaluate results, a radiation oncologist was asked to manually delineate the CTV on the CBCT scans. The Dice coefficients between each automatic CBCT segmentation - derived from the transformation of the manual CT segmentation - and the manual CBCT segmentation were calculated. Global or bone CT/CBCT RR has been shown to yield insufficient results in average. Local RR with an 8-mm margin around the CTV after bone RR was found to be the best candidate for systematically significantly improving prostate localization.

Computed tomography commissioning programmes: how to obtain a reliable MTF with an automatic approach?

The purpose of this study was to assess the spatial resolution of a computed tomography (CT) scanner with an automatic approach developed for routine quality controls when varying CT parameters. The methods available to assess the modulation transfer functions (MTF) with the automatic approach were Droege's and the bead point source (BPS) methods. These MTFs were compared with presampled ones obtained using Boone's method. The results show that Droege's method is not accurate in the low-frequency range, whereas the BPS method is highly sensitive to image noise. While both methods are well adapted to routine stability controls, it was shown that they are not able to provide absolute measurements. On the other hand, Boone's method, which is robust with respect to aliasing, more resilient to noise and provides absolute measurements, satisfies the commissioning requirements perfectly. Thus, Boone's method combined with a modified Catphan 600 phantom could be a good solution to assess CT spatial resolution in the different CT planes.

The development of an automatic recognition system for earmark and earprint comparisons

The value of earmarks as an efficient means of personal identification is still subject to debate. It has been argued that the field is lacking a firm systematic and structured data basis to help practitioners to form their conclusions. Typically, there is a paucity of research guiding as to the selectivity of the features used in the comparison process between an earmark and reference earprints taken from an individual. This study proposes a system for the automatic comparison of earprints and earmarks, operating without any manual extraction of key-points or manual annotations. For each donor, a model is created using multiple reference prints, hence capturing the donor within source variability. For each comparison between a mark and a model, images are automatically aligned and a proximity score, based on a normalized 2D correlation coefficient, is calculated. Appropriate use of this score allows deriving a likelihood ratio that can be explored under known state of affairs (both in cases where it is known that the mark has been left by the donor that gave the model and conversely in cases when it is established that the mark originates from a different source). To assess the system performance, a first dataset containing 1229 donors elaborated during the FearID research project was used. Based on these data, for mark-to-print comparisons, the system performed with an equal error rate (EER) of 2.3% and about 88% of marks are found in the first 3 positions of a hitlist. When performing print-to-print transactions, results show an equal error rate of 0.5%. The system was then tested using real-case data obtained from police forces.

Automatic quality assessment in structural brain magnetic resonance imaging

MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This work proposes a fully-automatic method for measuring image quality of three-dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T(1)-weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore.