Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An ultrasound based computer-aided diagnosis tool for steatosis detection

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. In this paper, a new computer-aided diagnosis (CAD) system for steatosis classification, in a local and global basis, is presented. Bayes factor is computed from objective ultrasound textural features extracted from the liver parenchyma. The goal is to develop a CAD screening tool, to help in the steatosis detection. Results showed an accuracy of 93.33%, with a sensitivity of 94.59% and specificity of 92.11%, using the Bayes classifier. The proposed CAD system is a suitable graphical display for steatosis classification.

Avaliação da caracterização de lesões em mamografia com recurso a sistemas CAD (Computer-Aided Diagnosis)

Os sistemas Computer-Aided Diagnosis (CAD) auxiliam a deteção e diferenciação de lesões benignas e malignas, aumentando a performance no diagnóstico do cancro da mama. As lesões da mama estão fortemente correlacionadas com a forma do contorno: lesões benignas apresentam contornos regulares, enquanto as lesões malignas tendem a apresentar contornos irregulares. Desta forma, a utilização de medidas quantitativas, como a dimensão fractal (DF), pode ajudar na caracterização dos contornos regulares ou irregulares de uma lesão. O principal objetivo deste estudo é verificar se a utilização concomitante de 2 (ou mais) medidas de DF – uma tradicionalmente utilizada, a qual foi designada por “DF de contorno”; outra proposta por nós, designada por “DF de área” – e ainda 3 medidas obtidas a partir destas, por operações de dilatação/erosão e por normalização de uma das medidas anteriores, melhoram a capacidade de caracterização de acordo com a escala BIRADS (Breast Imaging Reporting and Data System) e o tipo de lesão. As medidas de DF (DF contorno e DF área) foram calculadas através da aplicação do método box-counting, diretamente em imagens de lesões segmentadas e após a aplicação de um algoritmo de dilatação/erosão. A última medida baseia-se na diferença normalizada entre as duas medidas DF de área antes e após a aplicação do algoritmo de dilatação/erosão. Os resultados demonstram que a medida DF de contorno é uma ferramenta útil na diferenciação de lesões, de acordo com a escala BIRADS e o tipo de lesão; no entanto, em algumas situações, ocorrem alguns erros. O uso combinado desta medida com as quatro medidas propostas pode melhorar a classificação das lesões.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers

To retrospectively analyze the performance of a commercial computer-aided diagnosis (CAD) software in the detection of pulmonary nodules in original and energy-subtracted (ES) chest radiographs.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Variability of ascending aorta diameter measurements as assessed with electrocardiography-gated multidetector computerized tomography and computer assisted diagnosis software.

Recently, morphometric measurements of the ascending aorta have been done with ECG-gated multidector computerized tomography (MDCT) to help the development of future novel transcatheter therapies (TCT); nevertheless, the variability of such measurements remains unknown. Thirty patients referred for ECG-gated CT thoracic angiography were evaluated. Continuous reformations of the ascending aorta, perpendicular to the centerline, were obtained automatically with a commercially available computer aided diagnosis (CAD). Then measurements of the maximal diameter were done with the CAD and manually by two observers (separately). Measurements were repeated one month later. The Bland-Altman method, Spearman coefficients, and a Wilcoxon signed-rank test were used to evaluate the variability, the correlation, and the differences between observers. The interobserver variability for maximal diameter between the two observers was up to 1.2 mm with limits of agreement [-1.5, +0.9] mm; whereas the intraobserver limits were [-1.2, +1.0] mm for the first observer and [-0.8, +0.8] mm for the second observer. The intraobserver CAD variability was 0.8 mm. The correlation was good between observers and the CAD (0.980-0.986); however, significant differences do exist (P<0.001). The maximum variability observed was 1.2 mm and should be considered in reports of measurements of the ascending aorta. The CAD is as reproducible as an experienced reader.

The performance of computer-aided detection when analyzing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection

PURPOSE: The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively. METHODS: All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed. RESULTS: 278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD. Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density. CONCLUSION: CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.

On the Feasibility of Steering Swallowable Microsystem Capsules Using Computer-Aided Magnetic Levitation

Swallowable capsule endoscopy is used for non-invasive diagnosis of some gastrointestinal (GI) organs. However, control over the position of the capsule is a major unresolved issue. This study presents a design for steering the capsule based on magnetic levitation. The levitation is stabilized with the aid of a computer-aided feedback control system and diamagnetism. Peristaltic and gravitational forces to be overcome were calculated. A levitation setup was built to analyze the feasibility of using Hall Effect sensors to locate the in- vivo capsule. CAD software Maxwell 3D (Ansoft, Pittsburgh, PA) was used to determine the dimensions of the resistive electromagnets required for levitation and the feasibility of building them was examined. Comparison based on design complexity was made between positioning the patient supinely and upright.

Using Computer-aided Drug Design and Medicinal Chemistry Strategies in the Fight Against Diabetes

The aim of this work is to present a simple, practical and efficient protocol for drug design, in particular Diabetes, which includes selection of the illness, good choice of a target as well as a bioactive ligand and then usage of various computer aided drug design and medicinal chemistry tools to design novel potential drug candidates in different diseases. We have selected the validated target dipeptidyl peptidase IV (DPP-IV), whose inhibition contributes to reduce glucose levels in type 2 diabetes patients. The most active inhibitor with complex X-ray structure reported was initially extracted from the BindingDB database. By using molecular modification strategies widely used in medicinal chemistry, besides current state-of-the-art tools in drug design (including flexible docking, virtual screening, molecular interaction fields, molecular dynamics. ADME and toxicity predictions), we have proposed 4 novel potential DPP-IV inhibitors with drug properties for Diabetes control, which have been supported and validated by all the computational tools used herewith.

Computer-aided Drug Design of Novel PLA(2) Inhibitor Candidates for Treatment of Snakebite

Phospholipases A(2) (PLA(2)) are enzymes commonly found in snake venoms from Viperidae and Elaphidae families, which are major components thereof. Many plants are used in traditional medicine its active agents against various effects induced by snakebite. This article presents the PLA(2) BthTX-I structure prediction based on homology modeling. In addition, we have performed virtual screening in a large database yielding a set of potential bioactive inhibitors. A flexible docking program was used to investigate the interactions between the receptor and the new ligands. We have performed molecular interaction fields (MIFs) calculations with the phospholipase model. Results confirm the important role of Lys49 for binding ligands and suggest three additional residues as well. We have proposed a theoretically nontoxic, drug-like, and potential novel BthTX-I inhibitor. These calculations have been used to guide the design of novel phospholipase inhibitors as potential lead compounds that may be optimized for future treatment of snakebite victims as well as other human diseases in which PLA(2) enzymes are involved.

Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy

We have used various computational methodologies including molecular dynamics, density functional theory, virtual screening, ADMET predictions and molecular interaction field studies to design and analyze four novel potential inhibitors of farnesyltransferase (FTase). Evaluation of two proposals regarding their drug potential as well as lead compounds have indicated them as novel promising FTase inhibitors, with theoretically interesting pharmacotherapeutic profiles, when Compared to the very active and most cited FTase inhibitors that have activity data reported, which are launched drugs or compounds in clinical tests. One of our two proposals appears to be a more promising drug candidate and FTase inhibitor, but both derivative molecules indicate potentially very good pharmacotherapeutic profiles in comparison with Tipifarnib and Lonafarnib, two reference pharmaceuticals. Two other proposals have been selected with virtual screening approaches and investigated by LIS, which suggest novel and alternatives scaffolds to design future potential FTase inhibitors. Such compounds can be explored as promising molecules to initiate a research protocol in order to discover novel anticancer drug candidates targeting farnesyltransferase, in the fight against cancer. (C) 2009 Elsevier Inc. All rights reserved.