Data Mining - Concepts and Techniques.

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods. Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice. This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

https://link.springer.com/content/pdf/10.1007%2Fs13244-012-0196-6.pdf

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is now routinely used in oncological imaging for diagnosis and staging and increasingly to determine early response to treatment, often employing semiquantitative measures of lesion activity such as the standardized uptake value (SUV). However, the ability to predict the behaviour of a tumour in terms of future therapy response or prognosis using SUVs from a baseline scan prior to treatment is limited. It is recognized that medical images contain more useful information than may be perceived with the naked eye, leading to the field of “radiomics” whereby additional features can be extracted by computational postprocessing techniques. In recent years, evidence has slowly accumulated showing that parameters obtained by texture analysis of radiological images, reflecting the underlying spatial variation and heterogeneity of voxel intensities within a tumour, may yield additional predictive and prognostic information. It is hoped that measurement of these textural features may allow better tissue characterization as well as better stratification of treatment in clinical trials, or individualization of future cancer treatment in the clinic, than is possible with current imaging biomarkers. In this review we focus on the literature describing the emerging methods of texture analysis in 18FDG PET/CT, as well as other imaging modalities, and how the measurement of spatial variation of voxel grey-scale intensity within an image may provide additional predictive and prognostic information, and postulate the underlying biological mechanisms.

Quantifying Tumour Heterogeneity in 18F-FDG PET/CT Imaging by Texture Analysis

We give a methodology-oriented perspective on directional image analysis and rotation-invariant processing. We review the state of the art in the field and make connections with recent mathematical developments in functional analysis and wavelet theory. We unify our perspective within a common framework using operators. The intent is to provide image-processing methods that can be deployed in algorithms that analyze biomedical images with improved rotation invariance and high directional sensitivity. We start our survey with classical methods such as directional-gradient and the structure tensor. Then, we discuss how these methods can be improved with respect to robustness, invariance to geometric transformations (with a particular interest in scaling), and computation cost. To address robustness against noise, we move forward to higher degrees of directional selectivity and discuss Hessian-based detection schemes. To present multiscale approaches, we explain the differences between Fourier filters, directional wavelets, curvelets, and shearlets. To reduce the computational cost, we address the problem of matching directional patterns by proposing steerable filters, where one might perform arbitrary rotations and optimizations without discretizing the orientation. We define the property of steerability and give an introduction to the design of steerable filters. We cover the spectrum from simple steerable filters through pyramid schemes up to steerable wavelets. We also present illustrations on the design of steerable wavelets and their application to pattern recognition.

/pdf/transforms-and-operators-for-directional-bioimage-analysis-a-cz4ketezr8.pdf

Transforms and Operators for Directional Bioimage Analysis: A Survey

A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

The College of Computing and Digital Media (CDM) at DePaul University has recorded thousands of courses using an in-house system called Course Online (COL) since 2001. These recordings are available not only to students enrolled in online CDM courses, but also to students in traditional classrooms at CDM. In this study we analyzed survey responses and grade data to determine whether traditional students found COL recordings to be a valuable substitutional tool and whether the recordings had any impact on student performance. We found that a large majority of traditional CDM students find the recordings useful and believe that they improve performance. Somewhat counter to the students' perceptions, we found that there were no large differences in performance prior to the introduction of COL recordings and after COL recordings began to be available.

Does lecture capture make a difference for students in traditional classrooms

The research presented in this article is aimed at the development of an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using curvelet-based multi-resolution texture analysis. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The discriminating power of several curvelet-based texture descriptors are investigated. Tests indicate that Energy, Entropy, Mean and Standard Deviation signatures are the most effective descriptors for curvelets, yielding accuracy rates in the 97 - 98% range. A comparison with a similar algorithm based on wavelet and ridgelet texture descriptors clearly shows that using curvelet-based texture features significantly improves the classification of normal tissues in CT scans.

/pdf/curvelet-based-texture-classification-of-tissues-in-computed-3ba1xoxmlg.pdf

Curvelet-Based Texture Classification of Tissues in Computed Tomography

The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.

A Comparison of Wavelet-Based and Ridgelet-Based Texture Classification of Tissues in Computed Tomography.

The decreasing enrollment and high drop/fail rate of female students in introductory Computer Science (CS) courses over the last year, motivated us to create an integrated peer-and-mentor support group for female Information Technology (IT) students. The project aims at strengthening the confidence of female students by enveloping them in a community of learners. The design and implementation of the project is presented. Qualitative data showing its impact and potential is also discussed.

Lucia Dettori

Papers

A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

Does lecture capture make a difference for students in traditional classrooms

Curvelet-Based Texture Classification of Tissues in Computed Tomography

A Comparison of Wavelet-Based and Ridgelet-Based Texture Classification of Tissues in Computed Tomography.

Together is better: strengthening the confidence of women in computer science via a learning community