An implementation of A simple texture feature for retrieval of medical images by Rushi Lan, Si Zhong, Zhenbing Liu, Zhuo Shi & Xiaonan Luo for applying texture features in medical images retrival and a comparison between the original filters and prebuilt descriptor Historgram of Orentation Gradients (HOG).
- To run this project, download the project locally using:
git clone https://github.com/LMech/Texture-Features-for-Medical-Images-Retrival.git. - Install the requirements using:
pip install -r requirements.
The script divided into five parts preprocessing, training, evaluating, and user interface.
- Preprocessing script
preprocess.pyfor preporcessing the dataset according to the specified descriptororiginalorhogto apply filters according to the chosen descriptor and saved atpreprocessed_data/{descriptor}/.python preprocess.py {descriptor} - Training script for training the data using KMeans according to specified descriptor and the trained model saved as
{descriptor}_kmeans.pkland the evaluated histogram saved as{descriptor}_histogram.npyatmodels/{descriptor}.python train.py {descriptor} - Label generator script for evaluating each image in the data set and calculate the number of matched images, percision, recall at
models/hog/{descriptor}_evaluation.csv.python evaluation/generate_label.py {descriptor} - UI script to run a QT desktop application to facilitate image retrival process.
python ui/ui.py
After applying both descriptorthe feature extration process from the original paper managed to achieve 90.83% ARP and the prebuild HOG descriptor achieved 86.3% ARP both trained and evaluated on the full data set.
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Cordelia Schmid. Constructing models for content-based image retrieval. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR ’01), Dec 2001, Kauai, United States. pp.11–39, ff10.1109/CVPR.2001.990922ff. ffinria-00548274f
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Lan, R., Zhong, S., Liu, Z. et al. A simple texture feature for retrieval of medical images. Multimed Tools Appl 77, 10853–10866 (2018).