Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Textural Approach for Mass Abnormality Segmentation in Mammographic
  Images   Mass abnormality segmentation is a vital step for the medical diagnostic
process 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 Level
Co-occurrence Matrix (GLCM) as texture features with a region-based approach.
These features come in previous phase for segmentation stage or are using as
inputs to classification stage. The work discussed in this paper attempts to
experiment the GLCM method under a contour-based approach. Besides, we
experiment the proposed approach on various tissues densities to bring more
significant results. At this end, we explored some challenging breast images
from BIRADS medical Data Base. Our first experimentations showed promising
results with regard to the edges mass segmentation methods. This paper
discusses first the main works achieved in this area. Sections 2 and 3 present
materials and our methodology. The main results are showed and evaluated before
concluding our paper.

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The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
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The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat