Sources of image texture variation in tomographic breast imaging

Andrade, Diego; Kavuri, Amar; Das, Mini

doi:10.1117/12.2614334

Cited by 3 publications

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“…Recently, our group proposed the viability of using these features to predict, human observer detection performance in digital images, to evaluate contributions of anatomical and quantum noise in their origins and tested its robustness in DBT images across different scenarios. [2][3][4][5][6] In this work we explore, as well, the modified version proposed by Löfstedt [7]. This version accounts for changes due to quantization and produces more robust texture values across the different binning levels.…”

Section: Introductionmentioning

confidence: 99%

Multi-modality GLCM image texture feature for segmentation and tissue classification

Andrade

Gifford

Das

2023

Medical Imaging 2023: Physics of Medical Imaging

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Humans and computer observer models often rely on feature analysis from a single imaging modality. We will examine benefits of new features that assist in image classification and detection of malignancies in MRI and X-Ray tomographic images. While the image formation principles are different in these modalities, there are common features like contrast that are often employed by humans (radiologist) in each of these when making decision. We will examine other features that may not be well-understood or explored such as grey level co-occurrence matrix (GLCM) texture features. As preliminary data, we show here the utility of some of these features along with classification methods aided by Gaussian mixture models (GMM) and fuzzy C-Means dimensionality reduction. GLCM maps characterize the image texture and provide a numerical and spatial tool of the texture signatures present in it. We will present pathways for using these in tissue classification, segmentation and development of task-based assessments.

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Section: Introductionmentioning

confidence: 99%

Multi-modality GLCM image texture feature for segmentation and tissue classification

Andrade

Gifford

Das

2023

Medical Imaging 2023: Physics of Medical Imaging

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Image texture-based classification methods to mimic perceptual models of search and localization in medical images

Andrade,

Gifford,

Das

2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

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This study explores the validity of texture-based classification in the early stages of visual search/classification. Initially, we summarize our group's prior findings regarding the prediction of signal detection difficulty based on secondorder statistical image texture features in tomographic breast images. Alongside the development of visual search model observers to accurately mimic search and localization in medical images, we continue examining the efficacy of texturebased classification/segmentation methods. We consider both first and second-order features through a combination of texture maps and Gaussian mixture model (GMM). Our aim is to evaluate the advantages of integrating these methods at the early stages of the visual search process, particularly in scenarios where target morphological features may be less apparent or known, as in clinical data. By merging knowledge of imaging physics and texture based GMM, we enhance classification efficiency and refine localization of regions suspected of containing target locations.

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Image texture aided spectral CT material decomposition

Luna,

Andrade,

Das

2024

Medical Imaging 2024: Physics of Medical Imaging

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Spectral capabilities of photon counting detectors (PCDs) can allow material decomposition. We recently showed multimaterial decomposition using high resolution photon counting detectors (Medipix3). High-resolution photon counting spectral detectors have unique advantages and challenges. Some of the challenges arise from noise properties as well as spectral distortions. We show benefits of an empirical correction method to obtain accurate attenuation values in a spectral CT even with high resolution detectors and a combination of spectral distortions. Aided with accurate spectral correction, we show that a Gaussian mixture model assisted iterative decomposition can separate multiple materials at once. Our group has been investigating the role of image texture features in signal detection performance in tomographic images. Here we will explore utilizing variations in image texture features in spectral CT material decomposition. Along with attenuation variations for each energy bin, second order statistical texture feature variations associated with spectral data will be used to reduce the number of energy bins and imaging dose to perform multi-material decomposition. With promising preliminary results, we will show a more thorough investigation of image texture variations in spectral data to assist efficient and low dose material decomposition in spectral CT.

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Sources of image texture variation in tomographic breast imaging

Cited by 3 publications

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Multi-modality GLCM image texture feature for segmentation and tissue classification

Multi-modality GLCM image texture feature for segmentation and tissue classification

Image texture-based classification methods to mimic perceptual models of search and localization in medical images

Image texture aided spectral CT material decomposition

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