A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Label-free breast histopathology using quantitative phase imaging

According to the latest World Health Organization (WHO) statistics, breast cancer is the most common type of cancer among women worldwide. The WHO has further emphasized that early diagnosis and treatment are key in mitigating the burden of disease. In spite of this assessment, the standard histopathology of breast cancer still relies on manual microscopic examination of stained tissue. Being qualitative and manual in nature, this standard diagnostic procedure can suffer from inter-observer variation and low-throughput. In addition, stain variation between different samples and different laboratories creates problems for supervised image analysis methods for automated diagnosis. A quantitative, label-free and automatable microscopic modality for breast cancer diagnosis is, thus, needed to address these shortcomings in the standard method. Furthermore, prognostic biomarkers are important tools used by clinicians in order assess the disease course in patients. Being correlated with outcomes, these markers allow pathologists to determine aggressiveness of disease and tailor treatment accordingly. However, the current set of biomarkers for breast cancer are ineffective in predicting outcomes in all patients and there is a need for additional markers of prognosis to better account for variation among individuals. Microscopic and imaging tools for extracting new, quantitative biomarkers during breast histopathology are, thus, also desirable. Although a number of new quantitative imaging modalities for diagnostic and prognostic evaluations have been proposed, a key challenge remains compatibility with the existing workflow for easier clinical translation. Quantitative methods that minimally affect the clinical pipeline already in place are expected to have a greater impact than those that require significant new infrastructure. During my graduate work I have approached these problems in modern breast histopathology by using quantitative phase imaging (QPI). QPI is a label-free microscopy technique where image contrast is generated by measuring the optical path-length difference (OPD) across the specimen. OPD refers to the product of the refractive index and thickness at a point in the field of view. Since this measurement relies on a physical property of tissue and is label-free, it provides an objective and potentially automatable basis for tissue assessment. We employ a QPI technique called Spatial Light Interference Microscopy (SLIM) for investigations carried out during this thesis research. The specific aims of my thesis research are: 1. Label-free quantitative evaluation of breast biopsies using SLIM: In this work, we show by imaging a tissue microarray (TMA) that our QPI based method can separate benign and malignant cases by relying on tissue OPD based features. By employing image processing and statistical learning, we demonstrate a label-free quantitative diagnosis scheme that can provide an objective basis for tissue assessment. A quantitative method like this can also, potentially, be automated, reducing case-load for pathologists by automatically flagging problematic cases that require further investigation. 2. Quantifying tumor adjacent collagen structure in breast tissue using SLIM: Recent evidence shows that the structure of tumor adjacent collagen fibers influences tumor progression. In particular, collagen fiber alignment and orientation can facilitate epithelial invasion to surrounding tissue. We demonstrate that SLIM can be used to detect this prognostic marker that in the past had been detected using Second Harmonic Generation Microscopy (SHGM). Our SLIM based method improves on the SHGM based method in terms of throughput and the fact that cellular information can be obtained, in addition to collagen fiber structure, in a single image. 3. Quantitative histopathology on stained tissue biopsies: The instruments and image analysis tools developed in Aims 1 and 2 are designed for unstained tissue biopsies. Since standard tissue histopathology inevitably requires staining, we aim to demonstrate that we can extend these tools to stained tissue biopsies. In this way, the standard diagnostic workflow will be minimally disrupted. In addition, from a single shot, both an OPD map and stained tissue bright field image will be obtainable for evaluation. We demonstrate that QPI images of stained tissue can be used to solve diagnostic and prognostic problems in breast tissue assessment, using quantitative markers

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Image Analysis Applications of the Maximum Mean Discrepancy Distance Measure

The need to quantify distance between two groups of objects is prevalent throughout the signal processing world. The difference of group means computed using the Euclidean, or L2 distance, is one of the predominant distance measures used to compare feature vectors and groups of vectors, but many problems arise with it when high data dimensionality is present. Maximum mean discrepancy (MMD) is a recent unsupervised kernel-based pattern recognition method which may improve differentiation between two distinct populations over many commonly used methods such as the difference of means, when paired with the proper feature representations and kernels. MMD-based distance computation combines many powerful concepts from the machine learning literature, such as data distribution-leveraging similarity measures and kernel methods for machine learning. Due to this heritage, we posit that dissimilarity-based classification and changepoint detection using MMD can lead to enhanced separation between different populations. To test this hypothesis, we conduct studies comparing MMD and the difference of means in two subareas of image analysis and understanding: first, to detect scene changes in video in an unsupervised manner, and secondly, in the biomedical imaging field, using clinical ultrasound to assess tumor response to treatment. We leverage effective computer vision data descriptors, such as the bag-of-visual-words and sparse combinations of SIFT descriptors, and choose from an assessment of several similarity kernels (e.g. Histogram Intersection, Radial Basis Function) in order to engineer useful systems using MMD. Promising improvements over the difference of means, measured primarily using precision/recall for scene change detection, and k-nearest neighbour classification accuracy for tumor response assessment, are obtained in both applications.1 yea

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease