Breast Cancer Detection Using Haralick Features of Images Reconstructed from Ultra Wideband Microwave Scans

Fleet, B.D.; Yan, Jinyao; Knoester, David B.; Meng, Yao; Deller, J.R.; Goodman, Erik D.

doi:10.1007/978-3-319-13909-8_2

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Similarly, Chaddad et al (2011) relied on five measurements of the Haralick features for classifying and segmenting colon cancer cells in multi-spectral bio-images. Fleet et al (2014) used Haralick features to capture textural patterns from image reconstructions of ultrawideband microwave scans. They demonstrated the feasibility of Haralick features for breast cancer detection by classifying between malignant tumor present and no malignancy found.…”

Section: Related Workmentioning

confidence: 99%

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

et al. 2020

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Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyze the data in microscopy images depicting plant growth, either locating or counting regions of cellular features in images. In this paper, we present to the plant community an introduction to and exploration of two machine learning approaches to address the problem of marker localization in confocal microscopy. First, a comparative study is conducted on the classification accuracy of common conventional machine learning algorithms, as a means to highlight challenges with these methods. Second, a 3D (volumetric) deep learning approach is developed and presented, including consideration of appropriate loss functions and training data. A qualitative and quantitative analysis of all the results produced is performed. Evaluation of all approaches is performed on an unseen time-series sequence comprising several individual 3D volumes, capturing plant growth. The comparative analysis shows that the deep learning approach produces more accurate and robust results than traditional machine learning. To accompany the paper, we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (FIJI) software. Network models and example datasets will also be available online.

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Section: Related Workmentioning

confidence: 99%

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

et al. 2020

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“…Haralick Textural Features. Haralick textural features (Haralick, 1979) are the most usual features for textural description (see e.g., Fleet et al, 2014). A feature consists of a set of 14 algebraic quantities (Fig.…”

Section: Zernike Momentsmentioning

confidence: 99%

Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates inSaccharomyces cerevisiaeUsing a Textural Approach

Marin¹,

Denimal²,

Bertheau³

et al. 2019

Microsc Microanal

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In the context of microbiology, recent studies show the importance of ribonucleo-protein aggregates (RNPs) for the understanding of mechanisms involved in cell responses to specific environmental conditions. The assembly and disassembly of aggregates is a dynamic process, the characterization of the stage of their evolution can be performed by the evaluation of their number. The aim of this study is to propose a method to automatically determine the count of RNPs. We show that the determination of a precise count is an issue by itself and hence, we propose three textural approaches: a classical point of view using Haralick features, a frequency point of view with generalized Fourier descriptors, and a structural point of view with Zernike moment descriptors (ZMD). These parameters are then used as inputs for a supervised classification in order to determine the most relevant. An experiment using a specific Saccharomyces cerevisiae strain presenting a fusion between a protein found in RNPs (PAB1) and the green fluorescent protein was performed to benchmark this approach. The fluorescence was observed with two-photon fluorescence microscopy. Results show that the textural approach, by mixing ZMD with Haralick features, allows for the characterization of the number of RNPs.

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“…This work aimed to select the most discriminating parameters for cancer cells [ 11 ]. A study to investigate the feasibility of using Haralick features to discriminate between “cancer” and “normal” subimages within a patient is illustrated in [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

Zayed

Elnemr

2015

International Journal of Biomedical Imaging

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The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

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Breast Cancer Detection Using Haralick Features of Images Reconstructed from Ultra Wideband Microwave Scans

Cited by 8 publications

References 14 publications

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

Automatic Counting of Intra-Cellular Ribonucleo-Protein Aggregates inSaccharomyces cerevisiaeUsing a Textural Approach

Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

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