Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

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

COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Semiautomatic mammographic parenchymal patterns classification using multiple statistical features.

RATIONALE AND OBJECTIVES: Our project was to investigate a complete methodology for the semiautomatic assessment of digital mammograms according to their density, an indicator known to be correlated to breast cancer risk. The BI-RADS four-grade density scale is usually employed by radiologists for reporting breast density, but it allows for a certain degree of subjective input, and an objective qualification of density has therefore often been reported hard to assess. The goal of this study was to design an objective technique for determining breast BI-RADS density. MATERIALS AND METHODS: The proposed semiautomatic method makes use of complementary pattern recognition techniques to describe manually selected regions of interest (ROIs) in the breast with 36 statistical features. Three different classifiers based on a linear discriminant analysis or Bayesian theories were designed and tested on a database consisting of 1408 ROIs from 88 patients, using a leave-one-ROI-out technique. Classifications in optimal feature subspaces with lower dimensionality and reduction to a two-class problem were studied as well. RESULTS: Comparison with a reference established by the classifications of three radiologists shows excellent performance of the classifiers, even though extremely dense breasts continue to remain more difficult to classify accurately. For the two best classifiers, the exact agreement percentages are 76% and above, and weighted kappa values are 0.78 and 0.83. Furthermore, classification in lower dimensional spaces and two-class problems give excellent results. CONCLUSION: The proposed semiautomatic classifiers method provides an objective and reproducible method for characterizing breast density, especially for the two-class case. It represents a simple and valuable tool that could be used in screening programs, training, education, or for optimizing image processing in diagnostic tasks

Automated detection of breast cancer using SAXS data and wavelet features

The overarching goal of this project was to improve breast cancer screening protocols first by collecting small angle x-ray scattering (SAXS) images from breast biopsy tissue, and second, by applying pattern recognition techniques as a semi-automatic screen. Wavelet based features were generated from the SAXS image data. The features were supplied to a classifier, which sorted the images into distinct groups, such as “normal” and “tumor”. The main problem in the project was to find a set of features that provided sufficient separation for classification into groups of “normal” and “tumor.” In the original SAXS patterns, information useful for classification was obscured. The wavelet maps allowed new scale-based information to be uncovered from each SAXS pattern. The new information was subsequently used to define features that allowed for classification. Several calculations were tested to extract useful features from the wavelet decomposition maps. The wavelet map average intensity feature was selected as the most promising feature. The wavelet map intensity feature was improved by using pre-processing to remove the high central intensities from the SAXS patterns, and by using different wavelet bases for the wavelet decomposition. The investigation undertaken for this project showed very promising results. A classification rate of 100% was achieved for distinguishing between normal samples and tumor samples. The system also showed promising results when tested on unrelated MRI data. In the future, the semi-automatic pattern recognition tool developed for this project could be automated. With a larger set of data for training and testing, the tool could be improved upon and used to assist radiologists in the detection and classification of breast lesions

Colorectal Cancer Tissue Classification Based on Machine Learning

For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since histological images generally contain more than one tissue type, multi-class texture analysis plays a critical role to solve this problem. This study examines the important statistical features including Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filters, Haralick features, fractal filters, and local binary pattern (LBP) for colorectal cancer tissue identification by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieve high identification rate which is significantly superior to the existing known methods. In summary, the proposed method based on machine learning outperforms the techniques described in the literatures and achieve high classification accuracy rate at 93.17% for eight classes and 96.02% for ten classes which demonstrate promising applications for cancer tissue classification of histological image

Development of Features and Feature Reduction Techniques for Mammogram Classification

Breast cancer is one of the most widely recognized reasons for increased death rate among women. For reduction of the death rate due to breast cancer, early detection and treatment are of utmost necessity. Recent developments in digital mammography imaging systems have aimed to better diagnosis of abnormalities present in the breast. In the current scenario, mammography is an effectual and reliable method for an accurate detection of breast cancer. Digital mammograms are computerized X-ray images of breasts. Reading of mammograms is a crucial task for radiologists as they suggest patients for biopsy. It has been studied that radiologists report several interpretations for the same mammographic image. Thus, mammogram interpretation is a repetitive task that requires maximum attention for the avoidance of misinterpretation. Therefore, at present, Computer-Aided Diagnosis (CAD) system is exceptionally popular which analyzes the mammograms with the usage of image processing and pattern recognition techniques and classify them into several classes namely, malignant, benign, and normal. The CAD system recognizes the type of tissues automatically by collecting and analyzing significant features from mammographic images. In this thesis, the contributions aim at developing the new and useful features from mammograms for classification of the pattern of tissues. Additionally, some feature reduction techniques have been proposed to select the reduced set of significant features prior to classification. In this context, five different schemes have been proposed for extraction and selection of relevant features for subsequent classification. Using the relevant features, several classifiers are employed for classification of mammograms to derive an overall inference. Each scheme has been validated using two standard databases, namely MIAS and DDSM in isolation. The achieved results are very promising with respect to classification accuracy in comparison to the existing schemes and have been elaborated in each chapter. In Chapter 2, hybrid features are developed using Two-Dimensional Discrete Wavelet Transform (2D-DWT) and Gray-Level Co-occurrence Matrix (GLCM) in succession. Subsequently relevant features are selected using t-test. The resultant feature set is of substantially lower dimension. On application of various classifiers it is observed that Back-Propagation Neural Network (BPNN) gives better classification accuracy as compared to others. In Chapter 3, a Segmentation-based Fractal Texture Analysis (SFTA) is used to extract the texture features from the mammograms. A Fast Correlation-Based Filter (FCBF) method has been used to generate a significant feature subset. Among all classifiers, Support Vector Machine (SVM) results superior classification accuracy. In Chapter 4, Two-Dimensional Discrete Orthonormal S-Transform (2D-DOST) is used to extract the features from mammograms. A feature selection methodology based on null-hypothesis with statistical two-sample t-test method has been suggested to select most significant features. This feature with AdaBoost and Random Forest (AdaBoost-RF) classifier outperforms other classifierswith respect to accuracy. In Chapter 5, features are derived using Two-Dimensional Slantlet Transform (2D-SLT) from mammographic images. The most significant features are selected by utilizing the Bayesian Logistic Regression (BLogR) method. Utilizing these features, LogitBoost and Random Forest (LogitBoost-RF) classifier gives the better classification accuracy among all the classifiers. In Chapter 6, Fast Radial Symmetry Transform (FRST) is applied to mammographic images for derivation of radially symmetric features. A t-distributed Stochastic Neighbor Embedding (t-SNE) method has been utilized to select most relevant features. Using these features, classification experiments have been carried out through all the classifiers. A Logistic Model Tree (LMT) classifier achieves optimal results among all classifiers. An overall comparative analysis has also been made among all our suggested features and feature reduction techniques along with the corresponding classifier where they show superior results