Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound

Ultrasonography has great potential in differentiating malignant thyroid nodules from the benign ones. However, visual interpretation is limited by interobserver variability, and further, the speckle distribution poses a challenge during the classification process. This article thus presents an automated system for tumor classification in three-dimensional contrast-enhanced ultrasonography data sets. The system first processes the contrast-enhanced ultrasonography images using complex wavelet transform-based filter to mitigate the effect of speckle noise. The higher order spectra features are then extracted and used as input for training and testing a fuzzy classifier. In the off-line training system, higher order spectra features are extracted from a set of images known as the training images. These higher order spectra features along with the clinically assigned ground truth are used to train the classifier and obtain an estimate of the classifier or training parameters. The ground truth tells the class label of the image (i.e. whether the image belongs to a benign or malignant nodule). During the online testing phase, the estimated classifier parameters are applied on the higher order spectra features that are extracted from the testing images to predict their class labels. The predicted class labels are compared with their corresponding original ground truth to evaluate the performance of the classifier. Without utilizing the complex wavelet transform filter, the fuzzy classifier demonstrated an accuracy of 91.6%, while utilizing the complex wavelet transform filter, the accuracy significantly boosted to 99.1%

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

An automatic system for classification of breast cancer lesions in ultrasound images

Breast cancer is the most common of all cancers and second most deadly cancer in women in the developed countries. Mammography and ultrasound imaging are the standard techniques used in cancer screening. Mammography is widely used as the primary tool for cancer screening, however it is invasive technique due to radiation used. Ultrasound seems to be good at picking up many cancers missed by mammography. In addition, ultrasound is non-invasive as no radiation is used, portable and versatile. However, ultrasound images have usually poor quality because of multiplicative speckle noise that results in artifacts. Because of noise segmentation of suspected areas in ultrasound images is a challenging task that remains an open problem despite many years of research. In this research, a new method for automatic detection of suspected breast cancer lesions using ultrasound is proposed. In this fully automated method, new de-noising and segmentation techniques are introduced and high accuracy classifier using combination of morphological and textural features is used. We use a combination of fuzzy logic and compounding to denoise ultrasound images and reduce shadows. We introduced a new method to identify the seed points and then use region growing method to perform segmentation. For preliminary classification we use three classifiers (ANN, AdaBoost, FSVM) and then we use a majority voting to get the final result. We demonstrate that our automated system performs better than the other state-of-the-art systems. On our database containing ultrasound images for 80 patients we reached accuracy of 98.75% versus ABUS method with 88.75% accuracy and Hybrid Filtering method with 92.50% accuracy. Future work would involve a larger dataset of ultrasound images and we will extend our system to handle colour ultrasound images. We will also study the impact of larger number of texture and morphological features as well as weighting scheme on performance of our classifier. We will also develop an automated method to identify the "wall thickness" of a mass in breast ultrasound images. Presently the wall thickness is extracted manually with the help of a physician

Sistemas de análise de imagens de ecografia para reumatologia: técnicas baseadas na transformada de Wavelet para minimização de ruído Speckle

As doenças reumáticas revelam ser nos dias de hoje uma constante preocupação para os países desenvolvidos. Por se verificar um aumento da esperança média de vida e das taxas de sedentarismo nestes países, as doenças reumáticas apresentam e apresentarão ainda mais no futuro grandes problemas a nível da saúde pública e socioeconómico. Entre as doenças reumáticas, aquelas que causam maior redução da qualidade de vida dos pacientes, encontra-se a artrite reumatóide e a osteoartrite. Vários têm sido os esforços na tentativa de diagnosticar de forma precoce e eficaz estas doenças. Estas necessidades de diagnóstico conseguem ser satisfeitas pelo recurso à ecografia musculosquelética visto apresentar, para além das óbvias vantagens a nível físico do processo de aquisição de imagens, um resolução de imagem equiparável a ouras técnicas de imagem como a res-sonância magnética. Embora se verifiquem estas características vantajosas, a imagem ecográfica no geral e a imagem ecográfica do sistema musculosquelético em específico (pela elevada presença de conteúdo anatómico de fino detalhe) são corrompidas pela presença de um ruído bas-tante característico, o ruído speckle. O contraste e a qualidade da imagem são afetados pelo ruído speckle, o que dificulta a interpretação médica destas imagens. Um processamento deste ruído permite tornar a in-terpretação de uma ecografia mais fácil e clara, tentando uma aproximação à realidade. Uma parte deste trabalho foca-se no estudo do ruído speckle em imagens ecográficas musculosqueléticas com o objetivo de obter uma técnica de processamento de imagem capaz de reduzir este ruído tendo sempre em consideração um compromisso entre a redu-ção de ruído e a preservação de detalhes. Entre as várias técnicas já testadas até hoje, por se tratar de uma técnica de análise em multi-resolução, capaz de decompor uma imagem em subbandas, sendo possível analisar a mesma imagem como uma soma de detalhes e aproximações em diferentes escalas, o potencial da transformada de wavelet foi testado. vi Para avaliar o desempenho foram utilizadas métricas adequadas e os resultados numéricos demonstraram ser satisfatórios já que foi possível chegar a conclusões interessantes e in-clusive verificar qualitativamente através das imagens após processamento uma redução de ruído eficaz. O processo de aquisição de uma imagem ecográfica é um processo operador-dependente onde a experiência do utilizador é influente. A necessidade de estabelecer um método padrão para a aquisição de uma ecografia é uma realidade atual. Para além disso, verifica-se a falta de uma base de dados de ecografias musculosqueléticas disponível online que consiga satisfazer as necessidades de investigação em projetos e trabalhos idênticos a este. Portanto, a construção de uma base de dados de imagens ecográficas foi parte do objetivo deste trabalho.Nowadays, in the developed countries, the rheumatic diseases reveal to be a constant worry. Because of the crescent life expectation and sedentary life style verified in these countries, the rheumatic diseases represent and will represent in the future, complicated problems at the public health levels, social and economic ones. Among the rheumatic diseases, those which causes the biggest reduction of patient’s life quality, are the rheumatic arthritis and osteoarthritis. Many efforts have been done trying to diagnose these diseases precocious and effectively. These diagnostic needs can be en-sured using musculoskeletal ultrasounds, once it provides an image resolution equivalent to other images techniques as the example of magnetic resonance and because of all the physical advantages of ultrasound image acquisition process. While its numerous advantages, general ultrasound images and musculoskeletal ultra-sound images in particular case (due to their high level of details about anatomic struc-ture), are corrupted by the presence of a very characteristic noise, known as speckle noise. The image quality and contrast are affected by speckle noise, which makes the image interpretation task more difficult. A noise processing allows to have a clear and easier interpretation of an ultrasound image, reaching an approximation to the reality. Part of this work is focused in the study of speckle noise on musculoskeletal ultrasound images, aiming to obtain an image processing technique able to reduce this kind of noise, always considering a good compromise between noise reduction and detail preservation. Various techniques have been tested until now to reach an ideal speckle noise reduction in ultrasound images. By the ability to deal with multiresolution image analysis, to de-compose an image in different sub-bands, becoming possible to analyze the same image as a sum of details and approximations at different scales, made the wavelet transform a potential image processing tool, and its potential was tested in this work to deal with speckle noise. To evaluate the performance of noise reduction, appropriate metrics were used and the numerical results proved to be satisfactory, since it was possible to get conclusions and was also verified a qualitatively efficient noise reduction on the images. The ultrasound image acquisition process is an operator-dependent process where the ex-perience of the operator is very influent. The necessity to establish a standard method to medical ultrasound acquisition is an actual reality. Furthermore, there is a lack of data bases of musculoskeletal ultrasound images available online, which could satisfy some initial needs of investigations projects/works like this one. Therefore, an implementation of a data base to store such image data was other goal of this work

Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value