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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Mass abnormality segmentation is a vital step for the medical diagnosticprocess and is attracting more and more the interest of many research groups.Currently, most of the works achieved in this area have used the Gray LevelCo-occurrence Matrix (GLCM) as texture features with a region-based approach.These features come in previous phase for segmentation stage or are using asinputs to classification stage. The work discussed in this paper attempts toexperiment the GLCM method under a contour-based approach. Besides, weexperiment the proposed approach on various tissues densities to bring moresignificant results. At this end, we explored some challenging breast imagesfrom BIRADS medical Data Base. Our first experimentations showed promisingresults with regard to the edges mass segmentation methods. This paperdiscusses first the main works achieved in this area. Sections 2 and 3 presentmaterials and our methodology. The main results are showed and evaluated beforeconcluding our paper.

Textural Approach for Mass Abnormality Segmentation in Mammographic Images