On the impact of local image texture parameters on search and localization in digital breast imaging

Nisbett, William H.; Kavuri, Amareswararao; Fredette, Nathaniel R.; Das, Mini

doi:10.1117/12.2256113

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Each study set consisted of a number of equally spaced projections, P, acquired over a 60 • angular span of the phantom. A total of 12 cases were considered, where the number of projections P ∈ {3, 7,11,15,19,21,25,31,35,41,45, 51} . For each of these simulations, a total dose of 1.5 mGy was evenly distributed across the P projections.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…Using texture features as predictors of risk relies on the features being able to accurately quantify structural information about the subject. However, research indicates that texture significantly varies not only between clinical systems and modalities, but also across the space of acquisition and reconstruction parameters [23][24][25][26] . In addition, the noise from the randomness in photon emission and detection processes also becomes a dominating factor.…”

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confidence: 99%

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On the correlation between second order texture features and human observer detection performance in digital images

Nisbett

Kavuri

Das

2020

Sci Rep

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image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. the impact of changes in image texture on human observer's ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection-localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. it can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.

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Section: Methods and Resultsmentioning

confidence: 99%

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confidence: 99%

On the correlation between second order texture features and human observer detection performance in digital images

Nisbett

Kavuri

Das

2020

Sci Rep

Self Cite

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“…Our recent efforts have been aimed at understanding the effects of quantum and anatomical noise in signal detection [8,9]. We also showed that changes in second order statistical texture features can influence signal detection difficulty in human observers [10]. Texture features are relevant in various imaging fields mainly due to their capability to characterize essential statistical, structural, and spatial information about the object under investigation.…”

Section: Introductionmentioning

confidence: 90%

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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“…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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On the impact of local image texture parameters on search and localization in digital breast imaging

Cited by 3 publications

References 8 publications

On the correlation between second order texture features and human observer detection performance in digital images

On the correlation between second order texture features and human observer detection performance in digital images

Image texture aided spectral CT material decomposition

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

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