Detection of Tumour Based on Breast Tissue Categorization

Background: Breast cancer originates in breast tissue, which is made up of glands for milk production (lobules),  and  the  ducts  that  connect  lobules  to  the  nipple.  Breasts  contain  both  dense  tissue (glandular  tissue  and  connective  tissue,  together  known  as  fibro-glandular  tissue)  and  fatty  tissue. Fatty tissue appears dark on a mammogram, whereas fibro-glandular tissue appears as white. Despite the benefits of Computer Aided Detection (CAD), false detection of breast tumour is still a challenging issue with oncologist. A mammography is a non-invasive screening tool that uses low energy X-rays to show the pathology structure of breast tissue. Interpreting mammogram visually is a time consuming process  and  requires  a  great  deal  of  skill  and  experience. Earlier  Computer  Aided  Techniques emphasis detection of tumour in breast tissues rather than categorization of breast into Breast Imaging Report and Data System (BI-RADS) which is the medically understandable method of reporting.

Aim: The work centred on developing a CAD system which is capable of not only detecting but also categorizing breast tissue in line with BI-RADS scale.

Methodology: The acquired images were pre-processed to remove unwanted contents. Two stage medical procedural approach was designed to categorize thetissue in breast images into low dense (fatty) and high dense. Tumours in the low dense breasts were segmented, and then classified as normal,  benign  and  malignant.  The  developed  system  was  evaluated  using  sensitivity,  specificity, false positive reduction, false negative reduction and overall performance.

Results: The  developed  CAD  achieved  90.65%  sensitivity,  73.59%  specificity,  0.02  positive reduction, 0.04 false negative reduction and 85.71% overall performance.

Conclusion: The  false  positive  reduction  result  obtained  shows  that  false  detection  has  been minimized as a result of categorization procedure of the breast tissue in mammograms. This article has  reported  breast  tumour  detection  from  breast  tissue  categorisation  using  Medical  procedural approach.  The  developed  system  assisted  in  identification  of  suspicious  mammograms  and identification  of  dense  and  fatty  breasts.  The  classification  of  the  segmented  mammogram  into normal,  benign  and  malignant  achieved  a  better  false  positive  reduction  (0.02)  andfalse  negative reduction  (0.04)  and  thus  provided  an  improved  method  for  detection  and  classification  of  breast tumour in terms of overall performance.

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