# Texture features in medical image analysis: a survey

arXiv: https://arxiv.org/abs/2208.02046 · HTML: https://mlsanity.com/paper/2208.02046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Texture image analysis and texture classification methods - A review](https://mlsanity.com/paper/1904.06554/) (2019)

## Built on by (most cited first, 1 of 1)

- [Adversarial-Robust Transfer Learning for Medical Imaging via Domain Assimilation](https://mlsanity.com/paper/2402.16005/) (2024)

## Abstract (arXiv)

The texture is defined as spatial structure of the intensities of the pixels in an image that is repeated periodically in the whole image or regions, and makes the concept of the image. Texture, color and shape are three main components which are used by human visual system to recognize image contents. In this paper, first of all, efficient and updated texture analysis operators are survived with details. Next, some state-of-the-art methods are survived that use texture analysis in medical applications and disease diagnosis. Finally, different approaches are compared in terms of accuracy, dataset, application, etc. Results demonstrate that texture features separately or in joint of different feature sets such as deep, color or shape features provide high accuracy in medical image classification.