Wavelets for Computer-Aided Diagnosis in Radiographic Images

We have developed a novel method called local contralateral subtraction for reduction of false positives reported by a computer-aided diagnosis (CAD) scheme for detection of lung nodules in chest radiographs. Our method is based on the removal of normal structures in the regions of interest (ROIs), based on symmetry between the left and right lungs. In our method, two ROIs were extracted, one from the position where a candidate of a nodule is located, and the other from the anatomically corresponding location in the opposite lung, which contains similar normal structures. We employed a wavelet-based multiresolution image registration method to match the two ROIs, and subtraction was performed. A signal- to-noise ratio (SNR) between a central region and the adjacent background region was calculated for quantification of the remaining structures in the subtracted ROI. The SNR was then used for distinction between nodules and false positives. In an analysis of 550 ROIs consisting of 51 nodules and 499 false positives reported as detected nodules by our current CAD scheme, we were able to eliminate 44% of false positives with loss of only one nodule with this new method. This result indicates that our new method is effective in reducing false positives due to normal anatomic structures, and thus can improve the performance of our CAD scheme for detection of pulmonary nodules in chest radiographs.

The purpose of this study was to evaluate the performance of a computer-aided diagnosis (CAD) system on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images by using two independent datasets. We collected CT cases of 63 patients with 132 nodules ranging 4–30 mm in diameter from a hospital in Spain (20 patients) and a hospital in France (43 patients). CT examinations were acquired by using a SOMATOM Emotion CT scanner in Spain, and a dual-source SOMATOM Definition CT scanner in France (Siemens Medical System, Forchheim, Germany), with the following parameters: 6 × 1.0 mm collimation, 130 kVp, 70 mA (Emotion 6); or 64 × 0.6 mm collimation, 100–120 kVp, and 100–110 mAs (Definition 64). Nodules were detected independently by three experienced chest radiologists, and their detection results were used as the reference standard. The CAD scheme was developed with an advanced 3D iris filter for improving nodule detection. The performance of the CAD scheme was tested with an independent evaluation method based on the two databases. Free-response receiver operating characteristic curves, sensitivity and number of false-positive per scan, were employed to evaluate the performance of the CAD scheme. The study was approved by the Institutional Review Board. At an average false positive (FP) rate of 5 per scan, our CAD scheme achieved sensitivities of 79.5% for all nodules, 80.3% for solid, 60.0% for non-solid, 58.1% for spiculated, and 86.1% for non-spiculated nodules. In conclusion, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in CT. However, in this study we confirmed that significant differences could be found in the performance of the system depending on the testing database.

In this paper, we employed neural network ensemble for FPs reduction in detecting lung nodules in chest radiographs. In our scheme, the ensemble consists of four modified forward neural networks, each one of them was trained with the back propagation algorithm to distinct a different type of non-nodules from nodules. The outputs of all the individual neural networks were combined by a modified forward mixing ANN. The performance of our scheme for false positive reduction was evaluated by use of FROC. With neural network ensemble, the false positive rate of CAD scheme1 was reduced for 44% (from 2.86 to 1.6 positives per image), at an overall sensitivity of 60%. We also compared our scheme with other researches. The result demonstrates the superiority of it over other ones. We believe that the proposed method is useful in false positives reduction in the diagnosis of lung nodules in chest radiograph.

Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model

To assess whether a computer-aided, diagnosis (CAD) system can predict pathological CompleteResponse (pCR) to neoadjuvant chemotherapy (NAC) prior to treatment using texture features. Response to treatment of 44 patients was defined according to the histopatology of resected tumour and extracted axillary nodes in two ways: (a) pCR+ (Smith's Grade = 5) vs pCR- (Smith's Grade<5); (b) pCRN+ (pCR+ and absence of residual lymph node metastases) vs pCRN - . A CAD system was developed to: (i) segment the breasts; (ii) register the DCE-MRI sequence; (iii) detect the lesion and (iv) extract 27 3D texture features. The role of individual texture features, multiparametric models and Bayesian classifiers in predicting patients' response to NAC were evaluated. A cross-validated Bayesian classifier fed with 6 features was able to predict pCR with a specificity of 72% and a sensitivity of 67%. Conversely, 2 features were used by the Bayesian classifier to predict pCRN, obtaining a sensitivity of 69% and a specificity of 61%. A CAD scheme, that extracts texture features from an automatically segmented 3D mask of the tumour, could predict pathological response to NAC. Additional research should be performed to validate these promising results on a larger cohort of patients and using different classification strategies. Advances in knowledge: This is the first study assessing the role of an automatic CAD system in predicting the pathological response to NAC before treatment. Fully automatic methods represent the backbone of standardized analysis and may help in timely managing patients candidate to NAC.

We retrospectively evaluated the usefulness of computer-aided diagnosis (CAD) schemes to radiologist performance in the simultaneous detection of vertebral fractures and lung nodules on chest radiographs. We evaluated posteroanterior and lateral chest images of 21 patients with vertebral fractures, 31 patients with lung nodules, and 10 persons acting as controls. The total number of subjects was 60 because both lesions were present in four patients. Eighteen radiologists were asked to detect vertebral fractures and nodules simultaneously on posteroanterior and lateral images. The radiologists indicated their confidence level ratings regarding the presence or absence of lesions and the most likely location of each lesion on either posteroanterior or lateral images, first without and then with CAD output. The observers' performance was evaluated with use of receiver operating characteristic (ROC) and jackknife free-response ROC curves. With the CAD scheme, the average area under the ROC curve for detection of vertebral fractures improved from 0.906 to 0.951 (p = 0.002). That for lung nodules also improved, but the improvement was not statistically significant (0.804-0.816, p = 0.297). The figure-of-merit values obtained with the jackknife free-response ROC program improved from 0.585 to 0.680 (p < 0.001) for vertebral fractures and from 0.622 to 0.650 (p = 0.017) for nodules, both results having statistical significance. Average sensitivity in the detection of lesions improved from 59.8% to 69.3% for vertebral fractures and from 64.9% to 67.6% for nodules. In the detection of vertebral fractures and lung nodules on chest images, diagnostic accuracy among radiologists improves with the use of CAD.

A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database

Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. This study presents a new method to develop CAD schemes with significantly higher and robust performance.

This article presents an overview of the field of knowledge-based approaches in the processing and interpretation of medical images. It describes the major developments in artificial intelligence and image processing that have led up to the current potential to further the increasing interest in automated processing (knowledge-based systems) and interpretation (expert systems) of medical images. After a brief discussion of the principal scientific and engineering issues in the field of knowledge-based image processing, the process of building knowledge-based systems, and the frontiers of research and development, a number of reported systems for processing and interpretation of medical images are briefly reviewed. The development of an expert system for the analysis of scintigraphic images is then presented, emphasizing knowledge acquisition, implementation and evaluation. Finally, a perspective view on reasoning under uncertainty is presented.

Computer-aided Lung Nodule Detection in CT: Results of Large-Scale Observer Test 1

Observer studies are frequently performed to test new modalities. Correct study design is important to generate reliable results. Two most frequently used observer study designs are the sequential and the independent reading design. We investigated the effect of different observer study designs on reader performance results and statistical power. The study included multiple assessments of chest radiographs (CXR) with bone suppression images (BSI) for the detection of lung nodules. In a fully crossed study design 8 observers assessed first radiographs without and with BSI sequentially. Secondly they scored radiographs independently having BSI available from the beginning. Five months later, the same readers scored the same cases again in an independent reading session, completing the three scorings for CXRs with BSI. Observer performance was compared using multi reader multi case (MRMC) receiver operating characteristics (ROC). To estimate reader variance, Dorfman, Berbaum, Metz (DBM) variance component estimates were calculated. No significant difference between the sequential and the independent reading sessions could be found (p=0.51; p=0.61). Both reading designs showed increased performance with BSI, with a significant increase for the sequential and the independent reading session after five months (p=0.002; p=0.007). Total observer variance between sequential and independent reading design remained the same. A strong increase of uncorrelated components was found in the independent reading sessions, masking the ability to demonstrate differences in observer performance across modalities. In conclusion, results of the sequential and the independent study design did not show a significant difference. The independent study design had less power compared to the sequential study design due to a strong increase of uncorrelated variance components.

Automated Computer-Aided Detection of Lung Nodules in Metastatic Colorectal Cancer Patients for the Identification of Pulmonary Oligometastatic Disease

Diminution index: A novel 3D feature for pulmonary nodule detection

To develop a new computer-aided diagnosis (CAD) scheme that computes visually sensitive image features routinely used by radiologists to develop a machine learning classifier and distinguish between the malignant and benign breast masses detected from digital mammograms. An image dataset including 301 breast masses was retrospectively selected. From each segmented mass region, we computed image features that mimic five categories of visually sensitive features routinely used by radiologists in reading mammograms. We then selected five optimal features in the five feature categories and applied logistic regression models for classification. A new CAD interface was also designed to show lesion segmentation, computed feature values and classification score. Areas under ROC curves (AUC) were 0.786±0.026 and 0.758±0.027 when to classify mass regions depicting on two view images, respectively. By fusing classification scores computed from two regions, AUC increased to 0.806±0.025. This study demonstrated a new approach to develop CAD scheme based on 5 visually sensitive image features. Combining with a "visual aid" interface, CAD results may be much more easily explainable to the observers and increase their confidence to consider CAD generated classification results than using other conventional CAD approaches, which involve many complicated and visually insensitive texture features.

Computer Aided Diagnosis (CADx) systems are designed to assist doctors in medical image interpretation. However, a CADx is often thought of as a black box whose diagnostic decision is not intelligible to a radiologist. Therefore, a system that uses semantic image interpretation, and mimics human image analysis, has clear benefits. We propose a new method for automatic textual description of medical image findings, such as lesions in medical images. The method performs joint estimation of semantic features of lesions from image measurements. We formalize this problem as learning to map a set of diverse medical image measurements to a set of semantic descriptor values. We use a structured learning framework to model individual semantic descriptors and their relationships. The parameters of the model are efficiently learned using the Structured Support Vector Machine (SSVM). The proposed approach generates radiological lexicon descriptors used to make a diagnosis of various diseases. This can help radiologists easily understand a diagnosis recommendation made by an automatic system, such as CADx. We apply the proposed method to publicly available and to proprietary breast and brain imaging datasets, and show that our method generates more accurate descriptions, as compared to other alternative approaches.