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, J; Dr. Tessamma, Thomas(Cochin University Of Science And Technology, March 5, 2013)
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Abstract:
In this thesis, different techniques for image analysis of high density
microarrays have been investigated. Most of the existing image analysis techniques
require prior knowledge of image specific parameters and direct user intervention
for microarray image quantification. The objective of this research work was to
develop of a fully automated image analysis method capable of accurately
quantifying the intensity information from high density microarrays images. The
method should be robust against noise and contaminations that commonly occur in
different stages of microarray development.
Description:
Department of Electronics
Cochin University of Science and Technology
Jaya, V.L; Dr Gopika Kumari(Cochin University of Science and Technology, May 20, 2015)
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Abstract:
Digital Image Processing is a rapidly evolving eld with growing applications
in Science and Engineering. It involves changing the nature
of an image in order to either improve its pictorial information
for human interpretation or render it more suitable for autonomous
machine perception. One of the major areas of image processing
for human vision applications is image enhancement. The principal
goal of image enhancement is to improve visual quality of an image,
typically by taking advantage of the response of human visual
system.
Image enhancement methods are carried out usually in the pixel
domain. Transform domain methods can often provide another way
to interpret and understand image contents. A suitable transform,
thus selected, should have less computational complexity. Sequency
ordered arrangement of unique MRT (Mapped Real Transform)
coe cients can give rise to an integer-to-integer transform, named
Sequency based unique MRT (SMRT), suitable for image processing
applications. The development of the SMRT from UMRT (Unique
MRT), forward & inverse SMRT algorithms and the basis functions
are introduced. A few properties of the SMRT are explored and its
scope in lossless text compression is presented.
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