Multiresolution detection of spiculated lesions in digital mammograms

In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from the coarsest resolution to the finest resolution using a binary tree classifier. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to ner resolutions. Experimental results using the MIAS image database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates

Reduction of Limited Angle Artifacts in Medical Tomography via Image Reconstruction

Artifacts are unwanted effects in tomographic images that do not reflect the nature of the object. Their widespread occurrence makes their reduction and if possible removal an important subject in the development of tomographic image reconstruction algorithms. Limited angle artifacts are caused by the limited angular measurements, constraining the available tomographic information. This thesis focuses on reducing these artifacts via image reconstruction in two cases of incomplete measurements from: (1) the gaps left after the removal of high density objects such as dental fillings, screws and implants in computed tomography (CT) and (2) partial ring scanner configurations in positron emission tomography (PET). In order to include knowledge about the measurement and noise, prior terms were used within the reconstruction methods. Careful consideration was given to the trade-off between image blurring and noise reduction upon reconstruction of low-dose measurements.Development of reconstruction methods is an incremental process starting with testing on simple phantoms towards more clinically relevant ones by modeling the respective physical processes involved. In this work, phantoms were constructed to ensure that the proposed reconstruction methods addressed to the limited angle problem. The reconstructed images were assessed qualitatively and quantitatively in terms of noise reduction, edge sharpness and contrast recovery.Maximum a posteriori (MAP) estimation with median root prior (MRP) was selected for the reconstruction of limited angle measurements. MAP with MRP successfully reduced the artifacts caused by limited angle data in various datasets, tested with the reconstruction of both list-mode and projection data. In all cases, its performance was found to be superior to conventional reconstruction methods such as total-variation (TV) prior, maximum likelihood expectation maximization (MLEM) and filtered backprojection (FBP). MAP with MRP was also more robust with respect to parameter selection than MAP with TV prior.This thesis demonstrates the wide-range applicability of MAP with MRP in medical tomography, especially in low-dose imaging. Furthermore, we emphasize the importance of developing and testing reconstruction methods with application-specific phantoms, together with the properties and limitations of the measurements in mind

Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient

Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Development of A Discrete Wavelet Transform and Artificial Neural Network Based Classification System for Mammogram Images

Pada masa ini, terdapat pelbagai sistem diagnosis bantuan komputer (CAD) yang dibangunkan sejak beberapa tahun lalu untuk membantu ahli radiologi dalam pengecaman lesi mamografi yang boleh menunjukkan kehadiran kanser payudara. Walau bagaimanapun, prestasi CAD terhad oleh dua isu utama iaitu (i) kawasan yang tidak diingini (seperti label segi empat tepat berintensiti tinggi, pita, artifak, antara muka kulit dan air, dan lain-lain) yang boleh mengganggu pengecaman kanser payudara dan mengurangkan kadar ketepatan CAD, (ii) ketidakteraturan tekstur mamogram yang meliputi ciri-ciri seperti entropi, tenaga, kepencongan, kurtosis, min dan sisihan piawai yang berhubung kait dalam domain ruang dan tidak penting untuk pengelasan. Oleh itu, bagi menangani masalah yang dinyatakan di atas, sistem CAD yang lebih baik untuk imej mamogram dicadangkan. CAD yang dicadangkan ini terdiri daripada tiga peringkat utama, iaitu prapemprosesan, pengekstrakan ciri dan pengelasan imej mamogram. Pada peringkat prapemprosesan, Adaptive Multilevel Threshold (AMLT), yang berjaya menyingkirkan kawasan yang tidak diingini seperti yang dinyatakan sebelum ini, dicadangkan. Hal ini memberikan kelebihan kepada sistem dengan membolehkan pencarian terhadap keabnormalan terkekang pada lingkungan tisu payudara tanpa menjejaskan kawasan yang tidak diingini dalam latar belakang imej. Pada peringkat pengekstrakan ciri, dua ciri baharu iaitu median maksimum dan minimum subjalur berfrekuensi tinggi dicadangkan untuk pengkelasan imej mamogram kepada kategori normal, benigna dan malignan. Analisis plot kotak membuktikan bahawa kedua-dua ciri baharu tiada hubung kait dan penting untuk pengelasan imej mamogram berbanding dengan ciri-ciri konvensional. Pada peringkat pengelasan, rangkaian perseptron berbilang lapis (MLP) digunakan untuk mengelaskan mamogram normal dan tidak normal pada fasa pertama dan mamogram benigna dan malignan pada fasa kedua. Keputusan purata yang terhasil daripada 322 imej mamogram pada fasa pertama merumuskan bahawa pendekatan yang dicadangkan berjaya mencapai keputusan yang boleh harap dengan ketepatan sebanyak 96,27%, kepekaan sebanyak 94,78% dan kekhususan sebanyak 96.60%. Di samping itu, keputusan purata yang terhasil daripada 115 imej yang tidak normal mempunyai ketepatan, kepekaan dan kekhususan, masing-masing sebanyak 95.65%, 96.18% dan 95.38%. Keputusan eksperimen akhir menunjukkan bahawa sistem pengelasan mamogram yang dibangunkan mampu mencapai pengelasan tertinggi berbanding dengan sistem terkini yang lain. Prestasi pengelasan yang menggalakkan ini menunjukkan bahawa sistem yang dicadangkan tersebut boleh digunakan untuk membantu ahli patologi dalam menjalankan proses diagnosis. ________________________________________________________________________________________________________________________ Nowadays, numerous computer-aided diagnosis (CAD) systems have been developed to assist radiologists in the recognition of mammographic lesions that may indicate the presence of breast cancer. However, the performance of CAD is limited by two main issues; (i) unwanted regions (i.e. high-intensity rectangular label, tape, artefact, skin-air interface, etc.) could disturb the detection of breast cancer and reduce the accuracy rate of CAD, (ii) the irregularity of mammograms’ texture in which features such as entropy, energy, skewness, kurtosis, mean, and standard deviation are correlated in the spatial domain and insignificant for classification. Therefore, to address the aforementioned problems, an improved CAD system for the mammogram image is proposed. The proposed CAD consists of three main stages, namely pre-processing, feature extraction, and classification of mammogram images. In pre-processing step, Adaptive Multilevel Threshold (AMLT) is proposed, which successfully removes the above-mentioned unwanted regions. It gives the advantage to the system where it allows the search for abnormalities to be constrained to the region of the breast tissue without the effect of the unwanted regions in the image background. In feature extraction stage, two new features, namely medians of maximum and minimum of high-frequency subbands have been proposed to classify the mammogram images into normal, benign and malignant. Box plot analysis has proven that both new features are uncorrelated and significant for classification of mammogram images as compared to the conventional features. In the classification stage, multilayer perceptron (MLP) network is employed to classify normal and abnormal mammograms in the first phase and benign and malignant in the second phase. The average results produced from 322 mammogram images in the first phase concluded that the proposed approach attained reliable results with an accuracy of 96.27%, sensitivity of 94.78% and specificity of 96.60%. In addition, the average results produced from 115 abnormal images for accuracy, sensitivity, and specificity are 95.65%, 96.18%, and 95.38% respectively. The final experimental results show that the developed mammogram classification system is able to achieve the highest classification as compared to the other state-of-the-art systems. These promising classification performances show that the proposed system could probably be used to assist pathologists in their diagnosis process

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

Modélisation pharmacocinétique en tomographie d'émission par positrons en utilisant la technique des ondelettes

Dans le cadre de ce travail de recherche, les objectifs étaient de mettre en oeuvre et de valider la technique des ondelettes dans la modélisation pharmacocinétique chez le rat par tomographie d'émission par positrons (TEP). En TEP, le métabolisme du glucose dans l'organe étudié est mesuré en injectant un analogue du glucose, le fluorodéoxyglucose ([indice supérieur 18]FDG). La quantité de radioactivité injectée est mesurée dans le plasma sanguin en fonction du temps et constitue la courbe d'entrée, tandis que la radioactivité mesurée dans les tissus à l'aide de la TEP constitue la réponse des tissus. Avec la courbe d'entrée et l'intensité de la radioactivité dans les tissus telle que mesurée par le tomographe, le métabolisme du glucose est calculé à l'aide d'un modèle mathématique compartimental. Ce calcul se fait habituellement sur des images reconstruites filtrées ou itérées. Cependant, ces images filtrées ont perdu la résolution spatiale ou contiennent encore du bruit dû à la faible dose de radioactivité injectée ou le temps restreint de la mesure. Dans ce travail, nous proposons la technique des ondelettes basée sur des algorithmes de compression et de filtrage qui s'avèrent performants et faciles à utiliser. De plus, à partir des images filtrées et compressées par les ondelettes, nous calculons le métabolisme du glucose pixel par pixel, afin de générer une image appelée l'image paramétrique qui permet une visualisation du métabolisme du glucose dans les différentes structures d'un organe. Nous avons appliqué la technique des ondelettes autant sur les images que sur les projections, c'est-à-dire directement sur les matrices de projections avant de reconstruire les images pour éviter le filtrage des mesures et les opérations de reconstruction. Les ondelettes ont l'avantage de réduire les matrices et de grouper les intensités des pixels, procurant une meilleure statistique, donc plus de précision, et par conséquent une meilleure qualité des images paramétriques. La technique des ondelettes a été introduite également pour la correction du volume partiel en imagerie TEP. L'effet du volume partiel survient lorsque la radioactivité des structures, dont la taille est inférieure à la résolution spatiale du tomographe, est sous-estimée. La méthode des ondelettes continues représente une alternative aux méthodes habituellement utilisées, basées sur les informations anatomiques qui proviennent de l'imagerie par résonance magnétique (IRM) ou de tomodensitométrie (TDM). L'approche des ondelettes continues consiste à caractériser les différentes structures par le couple échelle et position. En utilisant ces informations fournies par les ondelettes, toutes les intensités sous-estimées des petites structures sont rehaussées, ce qui permet d'améliorer la détection des lésions et des tumeurs en imagerie TEP. En conclusion, le travail de cette thèse démontre l'avantage de l'utilisation des ondelettes dans le calcul des paramètres physiologiques à partir des images et des sinogrammes TEP mesurés avec le [indice supérieur 18]FDG chez le rat. Enfin, les résultats obtenus sur les images avec les ondelettes ont montré moins de variation, moins de bruit tout en préservant la résolution spatiale. L'application de la transformée en ondelettes continues dans la correction de l'effet du volume partiel pour les images TEP en utilisant l'ondelette appropriée a montré le potentiel des ondelettes pour localiser les différentes structures permettant une bonne correction et une meilleure qualité d'image