Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition.

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition.

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.

Medical image analysis of 3D CT images based on extension of Haralick texture features.

PURPOSE: A new approach to the segmentation of 3D CT images is proposed in an attempt to provide texture-based segmentation of organs or disease diagnosis. 3D extension of Haralick texture features was studied calculating co-occurrences of all voxels in a small cubic region around the voxel. RESULTS: For verification, the proposed method was tested on a set of abdominal 3D volumes of patients. Statistically, the improvement in segmentation was significant for most of the organs considered herein. CONCLUSIONS: The proposed method has potential application in medical image segmentation, including diagnosis of diseases.

Thyroid autoimmunity occurs more frequently in women with breast cancer compared to women with colorectal cancer and controls but it has no impact on relapse-free and overall survival.

The aim of the study was to compare the prevalence of autoimmune thyroid diseases (AITD) in patients with breast and colorectal cancer and controls and to evaluate the impact of AITD on the outcome of patients with breast cancer. Serum levels of TSH (thyroid-stimulating hormone), FT4 (free thyroxine), TPOAb (antibodies to thyroid peroxidase), TgAb (antibodies to thyroglobulin), selenium and prolactin were investigated in 210 randomly chosen women (89 with breast cancer and 72 with colorectal cancer after breast or abdominal surgery and 49 controls without oncological diseases). Eighty-four women with breast cancer were followed for a median of 136.0 months. The prevalence of positive titres of TPOAb (>60 kIU.l(-1)) was higher in the women with breast cancer as compared to positive titres in women with colorectal cancer and the controls (29.8 vs. 12.5 and 12.2%, respectively, P=0.016 and 0.036, respectively). Similarly, the prevalence of clinical, ultrasound and laboratory documented AITD was higher in women with breast cancer as compared to that in women with colorectal cancer and the controls (35.7 vs. 18.1 and 16.3%, respectively, P=0.014 and 0.029, respectively). We did not find any prognostic significance of FT4, TSH, TgAb, TPOAb, prolactin and the presence of AITD on relapse-free and overall survival among women with breast cancer. A negative prognostic significance of body mass index and serum levels of selenium on relapse-free survival was found. In conclusion, the prevalence of euthyroid AITD was higher in women with breast cancer as compared to euthyroid AITD in women with colorectal cancer and controls. The presence of AITD did not have an impact on the outcome of women with breast cancer.

Computational elastography from standard ultrasound image sequences by global trust region optimization.

A new approach is proposed to estimate the spatial distribution of shear modulus of tissues in-vivo. An image sequence is acquired using a standard medical ultrasound scanner while varying the force applied to the handle. The elastic properties are then recovered simultaneously with the inter-frame displacement fields using a computational procedure based on finite element modeling and trust region constrained optimization. No assumption about boundary conditions is needed. The optimization procedure is global, taking advantage of all available images. The algorithm was tested on phantom, as well as on real clinical images.

Image texture analysis of sonograms in chronic inflammations of thyroid gland.

The current practice in assessing sonographic findings of chronic inflamed thyroid tissue is mainly qualitative, based just on a physician's experience. This study shows that inflamed and healthy tissues can be differentiated by automatic texture analysis of B-mode sonographic images. Feature selection is the most important part of this procedure. We employed two selection schemes for finding recognition-optimal features: one based on compactness and separability and the other based on classification error. The full feature set included Muzzolini's spatial features and Haralick's co-occurrence features. These features were selected on a set of 2430 sonograms of 81 subjects, and the classifier performance was evaluated on a test set of 540 sonograms of 18 independent subjects. A classification success rate of 100% was achieved with as few as one optimal feature among the 129 texture characteristics tested. Both selection schemes agreed on the best features. The results were confirmed on the independent test set. The stability of the results with respect to sonograph setting, thyroid gland segmentation and scanning direction was tested.

Relation between quantitative description of ultrasonographic image and clinical and laboratory findings in lymphocytic thyroiditis.

OBJECTIVE: Relations between measurable properties of B-mode ultrasound images of thyroid gland and clinical and laboratory findings in patients with chronic inflammation of thyroid gland were studied. METHODS: Data from 65 patients with lymphocytic thyroiditis (LT) and 38 control subjects were analysed. Raw values of individual B-mode image pixels and standard co-occurrence second-order texture features were selected as quantitative image features. Thyroid antibodies, thyrotropin level, thyroxine replacement therapy, and body mass index were used as clinical variables. RESULTS: In the LT group, significant differences (t-tests, p<0.05) in image features were found for body mass indices (BMI) under and over 25 kg.m(-2), for thyroxine replacement therapy, and for the presence and absence of thyroid antibodies. Forward stepwise multiple regression was performed for the clinical or laboratory values as dependent variables and image features as independent variables. The following correlations were found: 1. between BMI and four image features in the normal group; 2. between the dose of thyroxine replacement therapy and two of image features in the LT group; and 3. for the level of thyroid antibodies in the LT group: five image features have correlated with the level of anti-thyroglobulin and three image features with level of anti-thyroperoxidase. CONCLUSION: These findings suggest the possibility of using quantitative indicators of ultrasound image of thyroid gland as predictors of the presence or absence of thyroid antibodies in patient's blood or as an auxiliary tool for dose recommendation of thyroxine replacement therapy.

Quantitative indicators of sonographic image of thyroid gland and their relation to antithyroid antibodies in Hashimoto's lymphocytic thyroiditis.

Sonography is a widely used non-invasive diagnostic tool and its main advantage is low cost in comparison with other diagnostic methods such as immunological analyses. In this work it is presented the relation between a sonographic image of thyroid gland and an immunological status of the patients with Hashimoto's lymphocytic thyroiditis (chronic inflammation of the thyroid gland). The results, evaluated on a set of 740 B-mode sonographic images from 37 subjects, show that raw values of individual image pixels in sonogram of thyroid gland with presence and without presence of anti-thyroid antibodies are significantly different (means 31.87 and 44.56; standard deviations 8.6 and 11.82; t = 3.4; p = 0.0017) and that they can be used for the prediction of presence of anti-thyreoglobulin and anti-thyreoperoxidasis antibodies. This result suggests the possibility to use this method in clinical diagnostic process for reducing the costs. Also the correlation between the image features and the level of antibodies was examined. The highest correlation was found for inverse difference moment and the level of anti-thyreoperoxidasis (coefficient of determination 0.43).

Representing clinical guidelines in UMl: a comparative study.

Clinical guidelines can be represented using models, such as GLIF, specifically designed for healthcare guidelines. This paper demonstrates that they can also be modelled using a mainstream business modelling language such as UML. The paper presents a guideline in GLIF and as UML activity diagrams, and then presents a mapping of GLIF primitives to UML. The potential benefits of using a mainstream modelling language are outlined. These include availability of advanced modelling tools, transfer between modelling tools, and automation via business workflow technology.