Texture Based Segmentation

Zwiggelaar, Reyer; Denton, Erika R. E.

doi:10.1007/11783237_58

Cited by 12 publications

(7 citation statements)

References 23 publications

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“…A Gaussian kernel function modulated by a sinusoidal plane wave is a two-dimensional (2D) Gabor filter in the spatial domain. It is notable that frequency and orientation representations of Gabor filters are comparable to those of the human visual system and are suitable for texture discrimination and representation [ 29 ]. Gabor response segmentation assumes that all pixels within the same textured region will provide similar decomposition to different Gabor kernels.…”

Section: Related Workmentioning

confidence: 99%

Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures

Abuhussein

Robinson

2022

J. Imaging

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The benefits of autonomous image segmentation are readily apparent in many applications and garners interest from stakeholders in many fields. The wide range of benefits encompass applications ranging from medical diagnosis, where the shape of the grouped pixels increases diagnosis accuracy, to autonomous vehicles where the grouping of pixels defines roadways, traffic signs, other vehicles, etc. It even proves beneficial in many phases of machine learning, where the resulting segmentation can be used as inputs to the network or as labels for training. The majority of the available image segmentation algorithmic development and results focus on visible image modalities. Therefore, in this treatment, the authors present the results of a study designed to identify and improve current semantic methods for infrared scene segmentation. Specifically, the goal is to propose a novel approach to provide tile-based segmentation of occlusion clouds in Long Wave Infrared images. This work complements the collection of well-known semantic segmentation algorithms applicable to thermal images but requires a vast dataset to provide accurate performance. We document performance in applications where the distinction between dust cloud tiles and clear tiles enables conditional processing. Therefore, the authors propose a Gray Level Co-Occurrence Matrix (GLCM) based method for infrared image segmentation. The main idea of our approach is that GLCM features are extracted from local tiles in the image and used to train a binary classifier to provide indication of tile occlusions. Our method introduces a new texture analysis scheme that is more suitable for image segmentation than the solitary Gabor segmentation or Markov Random Field (MRF) scheme. Our experimental results show that our algorithm performs well in terms of accuracy and a better inter-region homogeneity than the pixel-based infrared image segmentation algorithms.

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Section: Related Workmentioning

confidence: 99%

Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures

Abuhussein

Robinson

2022

J. Imaging

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“…Texture Descriptors. Zwiggelaar and Denton [ 64 ] developed a texture classification approach using contrast information, based on the concept that texture can be discriminated from the contrast between key structural elements and their repeating patterns. The developed texture analysis method is related to local binary patterns (LBP) [ 145 ] and is similar to SUSAN [ 146 ].…”

Section: Automatic Breast Density and Parenchymal Segmentationmentioning

confidence: 99%

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Juette

Denton

et al. 2015

International Journal of Breast Cancer

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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.

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“…From the literature, it has been observed that the number of approaches has been reported in which texture, colour and intensity features are included for image segmentation as only intensity features are not able to do accurate segmentation of the regions [7–11]. As intensity feature is not sufficient alone in the presence of texture information, so there is a need of integrating texture information.…”

Section: Introductionmentioning

confidence: 99%

Texture‐based image segmentation using neutrosophic clustering

Koundal

2017

IET Image Processing

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Texture Based Segmentation

Cited by 12 publications

References 23 publications

Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures

Obscurant Segmentation in Long Wave Infrared Images Using GLCM Textures

A Review on Automatic Mammographic Density and Parenchymal Segmentation

Texture‐based image segmentation using neutrosophic clustering

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