English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Texture is one of the most important properties of visual surface that helpsin discriminating one object from another or an object from background. Theself-organizing map (SOM) is an excellent tool in exploratory phase of datamining. It projects its input space on prototypes of a low-dimensional regulargrid that can be effectively utilized to visualize and explore properties ofthe data. This paper proposes an enhancement extraction method for accurateextracting features for efficient image representation it based on SOM neuralnetwork. In this approach, we apply three different partitioning approaches asa region of interested (ROI) selection methods for extracting differentaccurate textural features from medical image as a primary step of ourextraction method. Fisherfaces feature selection is used, for selectingdiscriminated features form extracted textural features. Experimental resultshowed the high accuracy of medical image categorization with our proposedextraction method. Experiments held on Mammographic Image Analysis Society(MIAS) dataset.

Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization