Skip to main content

Logarithmic Transformation based Local Binary Pattern (LT-LBP) for Medical Image Analysis

Project description

LT-LBP: Logarithmic Transformation Local Binary Pattern

PyPI Python License

LT-LBP (Logarithmic Transformation – Local Binary Pattern) is a lightweight Python library for texture analysis and feature extraction from grayscale images.


Mathematical Definition

$$ LT{-}LBP = \sum_{p=0}^{7} s(g_c - g_p) \cdot p $$

where:

s(x) = 1 if x > 0
s(x) = 0 otherwise

Here, g_c is the center pixel intensity and g_p are its 8 neighboring pixels (clockwise).

This method is robust for illuminated and low-light images, making it ideal for applications in:

  • Face recognition
  • Texture classification
  • Medical image analysis
  • Surveillance
  • Industrial inspection

Features

  • Compute LT-LBP for any grayscale image
  • Generate normalized histogram feature vectors for machine learning models
  • Fully vectorized NumPy implementation
  • Lightweight and easy to integrate
  • Suitable for research and reproducible experiments

Installation

pip install ltlbp


## License

MIT License



## Citation

If you use LT-LBP in your research, please cite:

@article{pankaja2023ltlbp,
  title   = {LT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray Images},
  author  = {Lakshmi, Pankaja and Sivagami, M.},
  journal = {Journal of Computer Science},
  volume  = {20},
  number  = {1},
  pages   = {106--120},
  year    = {2023},
  doi     = {10.3844/jcssp.2024.106.120},
  url     = {https://thescipub.com/abstract/jcssp.2024.106.120}
}

The LT-LBP Python implementation is publicly available at:
https://pypi.org/project/ltlbp/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ltlbp-1.1.1.tar.gz (3.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ltlbp-1.1.1-py3-none-any.whl (2.9 kB view details)

Uploaded Python 3

File details

Details for the file ltlbp-1.1.1.tar.gz.

File metadata

  • Download URL: ltlbp-1.1.1.tar.gz
  • Upload date:
  • Size: 3.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for ltlbp-1.1.1.tar.gz
Algorithm Hash digest
SHA256 7dd686f79ecd2ae31cb58e05ce07ab4a5986082896e778f416b788a26da64239
MD5 90716d52c74a0ad432348f52522e1da5
BLAKE2b-256 fb5c806beb8ca49b3a9c04e062b23ae30d06c8aea883e0ee6eb7e33a9d21bd02

See more details on using hashes here.

File details

Details for the file ltlbp-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: ltlbp-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 2.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for ltlbp-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 bde1da0924abeca0ba4dad903710e29cab2e50d5736edc3042b80f8e22b69b83
MD5 8b087618fd3d242c8b2f1e9e3bc3eae2
BLAKE2b-256 aff3266b129e513243f841294a99a6d74cd9a32fb7e206d0b5bc6a8cec4a199a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page