Mini, M G; Dr. Tessamma, Thomas(Cochin University of Science And Technology, July 14, 2004)
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Abstract:
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.
Description:
Department of Electronics,
Cochin University of Science And Technology
Deepa, Sankar; Dr. Tessamma, Thomas(Cochin University of Science and Technology, August , 2011)
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Abstract:
After skin cancer, breast cancer accounts for the second greatest number of cancer diagnoses in women. Currently the etiologies of breast cancer are unknown, and there is no generally accepted therapy for preventing it. Therefore, the best way to improve the prognosis for breast cancer is early detection and treatment. Computer aided detection systems (CAD) for detecting masses or micro-calcifications in mammograms have already been used and proven to be a potentially powerful tool , so the radiologists are attracted by the effectiveness of clinical application of CAD systems. Fractal geometry is well suited for describing the complex physiological structures that defy the traditional Euclidean geometry, which is based on smooth shapes. The major contribution of this research include the development of
• A new fractal feature to accurately classify mammograms into normal and normal (i)With masses (benign or malignant) (ii) with microcalcifications (benign or malignant)
• A novel fast fractal modeling method to identify the presence of microcalcifications by fractal modeling of mammograms and then subtracting the modeled image from the original mammogram.
The performances of these methods were evaluated using different standard statistical analysis methods. The results obtained indicate that the developed methods are highly beneficial for assisting radiologists in making diagnostic decisions. The mammograms for the study were obtained from the two online databases namely, MIAS (Mammographic Image Analysis Society) and DDSM (Digital Database for Screening Mammography.
Description:
Department of Electronics, Cochin University of Science and Technology