Technologies in Texture Analysis – A Review

Paul, Tuhin Utsab; Banerjee, Pranavesh; Mukherjee, Anannya; Bandhyopadhyay, Samir Kumar

doi:10.9734/bjast/2016/19082

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research in the literature have tackled this problem, and hence utilized many approaches for texture analysis [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Multi-level Parallel System for Laws Masks Abnormality Lung Detection

Elnemr¹,

ElKabbany²

2018

IJISA

View full text Add to dashboard Cite

Lung is a vital organ that plays a pivotal role in every second of our lives. Lungs may be affected by a number of diseases, including pulmonary edema and cancer. These diseases deemed life-sustained diseases, so they possess high preferences in detection, diagnosis, and possible treatments. In this paper, we presented a textural feature analysis framework that is capable of detecting lung abnormalities (edema or cancer) using Laws masks texture features. Laws masks are conventional texture feature extractor, and considered as one of the best methods for texture analysis in image processing. However, computing and extracting the texture features through various masks are very time consuming, whereas lung diseases demand rapid yet accurate diagnosis. Today, increased efficiency is being achieved through parallelism, and this trend is believed to continue in the future, with all computing devices likely to have many processors. Therefore, our objective is to investigate a multi-level parallel algorithm on Laws masks to describe structural variations of lung abnormalities. To our knowledge, there are no published researches that employed parallel strategies for lung abnormalities detection using Laws method. The proposed system has been experimented on real CT lung images. The results indicate that Laws texture features are capable of discriminating among normal, edema and cancerous lungs. Furthermore, applying parallel processing approaches improves significantly the overall system performance.

show abstract

“…Research in the literature have tackled this problem, and hence utilized many approaches for texture analysis [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Multi-level Parallel System for Laws Masks Abnormality Lung Detection

Elnemr¹,

ElKabbany²

2018

IJISA

View full text Add to dashboard Cite

show abstract

Research on Texture Classification Based on Multi-Scale Information Fusion

Wang

2023

JACIII

View full text Add to dashboard Cite

Texture feature is an important visual cue for an image, which is the unified description of human visual attributes and sensory attributes. The inherent problem of texture image is that the difference of intra-class images is large and the disparity of inter-class images is small. This problem increases the difficulty of texture image recognition. Therefore, improving the relevance embedding of intra-class images can reduce the classification errors caused by this problem. To solve this problem, this paper proposes a multi-scale information fusion network algorithm, which adopts a cascade structure. It combines multi-scale feature information with the corresponding background information. The shallow background information guides the next stage of feature formation and enhances the similarity of intra-class images. The intra-class feature information obtained is more general. The algorithm has been tested on data sets describable texture database (DTD) and Flickr material dataset (FMD), which has achieved good results.

show abstract

Technologies in Texture Analysis – A Review

Cited by 2 publications

References 0 publications

A Multi-level Parallel System for Laws Masks Abnormality Lung Detection

A Multi-level Parallel System for Laws Masks Abnormality Lung Detection

Research on Texture Classification Based on Multi-Scale Information Fusion

Contact Info

Product

Resources

About