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Abstract:
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Cancer treatment is most effective when it is detected early and the progress in
treatment will be closely related to the ability to reduce the proportion of misses in the
cancer detection task. The effectiveness of algorithms for detecting cancers can be
greatly increased if these algorithms work synergistically with those for characterizing
normal mammograms. This research work combines computerized image analysis
techniques and neural networks to separate out some fraction of the normal
mammograms with extremely high reliability, based on normal tissue identification and
removal.
The presence of clustered microcalcifications is one of the most important and
sometimes the only sign of cancer on a mammogram. 60% to 70% of non-palpable
breast carcinoma demonstrates microcalcifications on mammograms [44], [45], [46].WT based techniques are applied on the remaining mammograms, those are obviously
abnormal, to detect possible microcalcifications. The goal of this work is to improve the
detection performance and throughput of screening-mammography, thus providing a
‘second opinion ‘ to the radiologists.
The state-of- the- art DWT computation algorithms are not suitable for practical
applications with memory and delay constraints, as it is not a block transfonn. Hence in
this work, the development of a Block DWT (BDWT) computational structure having
low processing memory requirement has also been taken up. |