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| Abstract: | Active microwave imaging is explored as an imaging modality for early detection of breast cancer. When exposed to microwaves, breast tumor exhibits electrical properties that are significantly different from that of healthy breast tissues. The two approaches of active microwave imaging — confocal microwave technique with measured reflected signals and microwave tomographic imaging with measured scattered signals are addressed here. Normal and malignant breast tissue samples of same person are subjected to study within 30 minutes of mastectomy. Corn syrup is used as coupling medium, as its dielectric parameters show good match with that of the normal breast tissue samples. As bandwidth of the transmitter is an important aspect in the time domain confocal microwave imaging approach, wideband bowtie antenna having 2:1 VSWR bandwidth of 46% is designed for the transmission and reception of microwave signals. Same antenna is used for microwave tomographic imaging too at the frequency of 3000 MHz. Experimentally obtained time domain results are substantiated by finite difference time domain (FDTD) analysis. 2-D tomographic images are reconstructed with the collected scattered data using distorted Born iterative method. Variations of dielectric permittivity in breast samples are distinguishable from the obtained permittivity profiles. |
| URI: | http://dyuthi.cusat.ac.in/purl/2735 |
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| Dyuthi-P00369.pdf | (1.249Mb) |
| 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 |
| URI: | http://dyuthi.cusat.ac.in/purl/4543 |
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| Dyuthi-T1815.pdf | (11.18Mb) |
| 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 |
| URI: | http://dyuthi.cusat.ac.in/purl/3047 |
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| Dyuthi-T1021.pdf | (24.09Mb) |
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