Arpita Das

Abstract: Breast cancer is one of the leading causes of death for women Small clusters of micro calcifications appearing as collection of white spots on mammograms show an early warning of breast cancer In present paper a novel approach of segmentation implemented on X-ray mammograms for more accurate detection of microcalcification clusters has been introduced The method is based on discrete wavelet transform due to its multiresolution properties Morphological tophat algorithm is applied for contrast enhancement of the calcification clusters Finally fuzzy c-means clustering (FCM) algorithm has been implemented for intensity-based segmentation The proposed technique is compared with conventional global thresholding method and experimental results show the good properties of the proposed technique

Abstract: We present a non-linear 2-D/2-D affine registration technique for MR and CT modality images of section of human brain. Automatic registration is achieved by maximization of a similarity metric, which is the correlation function of two images. The proposed method has been implemented by choosing a realistic, practical transformation and optimization techniques. Correlation-based similarity metric should be maximal when two images are perfectly aligned. Since similarity metric is a non-convex function and contains many local optima, choice of search strategy for optimization is important in registration problem. Many optimization schemes are existing, most of which are local and require a starting point. In present study we have implemented genetic algorithm and particle swarm optimization technique to overcome this problem. A comparative study shows the superiority and robustness of swarm methodology over genetic approach.

Abstract: An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses/lesions appearing in breast in different groups of benignancy/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing in breast have been extracted for classification. The shape features represented by Fourier Descriptors, introduce a large number of feature vectors. Thus to classify different boundaries, a standard classifier needs a large number of inputs, and simultaneously to train the classifier a large number of training cycles are required. This may invite the problem of overlearning, followed by chance of misclassification. In proposed methodology, Genetic Algorithm (GA) has been used for searching of significant input feature vectors. Finally adaptive neuro fuzzy-based classifier has been introduced for classification of tumor masses in breast.

Abstract: Thin films of Zinc Oxide were deposited by the sol-gel technique on glass substrates. The films were doped with Al, Mg or co-doped with both by introduction of appropriate compounds in the solution before dip-coating and annealing in air at 500 °C. Energy Dispersive X-Ray Spectroscopy was employed to measure the dopant incorporation. X-ray diffraction studies indicate that Mg doping increases grain size, while Al doping reduces it. Photoluminescence (PL) measurements indicate that undoped and Al-doped films show, along with a broad near band-edge (NBE) peak, additional peaks at longer wavelengths related to various defect states. However Mg doped films show only a sharp NBE peak, which is blue shifted compared to undoped ZnO, and there are no prominent sub band gap luminescence peaks. This is also the case for Mg and Al co-doped ZnO samples, provided the Mg content is low. Photocurrent measurements were carried out using silver contacts using a De source under atmospheric conditions. Undoped and Mg doped ZnO films showed high resistances and low photocurrent levels. With low Al doping, both the dark current and the photocurrent increase significantly, but the films show very long photocurrent transients. With optimized concentration of Mg/Al co-doping in ZnO, the photocurrent increased by ~98 times compared to ZnO films doped only with Mg. Simultaneously, the photocurrent transients became ~44 times faster than ZnO films doped only with Al.

Abstract: Medical image fusion has been used to derive the useful information from multi modal medical images. The proposed methodology introduces evolutionary approaches for robust and automatic extraction of information from different modality images. This evolutionary fusion strategy implements multiresolution decomposition of the input images using wavelet transform. It is because, the analysis of input images at multiple resolutions able to extracts more fine details and improves the quality of the composite fused image. The proposed approach is also independent of any manual marking or knowledge of fiducial points and starts the fusion procedure automatically. The performance of the genetic based evolutionary algorithm is compared with fuzzy based fusion technique using mutual information as the similarity measuring metric. Experimental results show that genetic searching based fusion technique improves the quality of the fused images significantly over the fuzzy approaches.

Abstract: QUITE apart from the academic consideration that vital and medical statistics now form an obligatory part of the education of students seeking the University of London's diploma in public health, the demand for information about the methods of vital and medical statistics is increasing. The most casual reader of the newspapers is now aware that population problems are of serious practical importance and that the publications of the General Register Office cannot be ignored. It is scarcely an exaggeration to say that the recently issued preliminary report on the census of 1931 is one of the most sensational documents which has appeared for years, and that he who reads it intelligently will understand what is meant by saying that civilisation is in the melting pot. An Introduction to Medical Statistics. By Hilda M. Woods William T. Russell. Pp. x + 125. (London: P. S. King and Son, Ltd., 1931.) 7s. 6d.

Abstract: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Abstract: Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Abstract: This paper describes a quantitative assessment of respiratory motion of the heart and the construction of a model of respiratory motion. Three-dimensional magnetic resonance scans were acquired on eight normal volunteers and ten patients. The volunteers were imaged at multiple positions in the breathing cycle between full exhalation and full inhalation while holding their breath. The exhalation volume was segmented and used as a template to which the other volumes were registered using an intensity-based rigid registration algorithm followed by nonrigid registration. The patients were imaged at inhale and exhale only. The registration results were validated by visual assessment and consistency measurements indicating subvoxel registration accuracy. For all subjects, we assessed the nonrigid motion of the heart at the right coronary artery, right atrium, and left ventricle. We show that the rigid-body motion of the heart is primarily in the craniocaudal direction with smaller displacements in the right-left and anterior-posterior directions; this is in agreement with previous studies. Deformation was greatest for the free wall of the right atrium and the left ventricle; typical deformations were 3-4 mm with deformations of up to 7 mm observed in some subjects. Using the registration results, landmarks on the template surface were mapped to their correct positions through the breathing cycle. Principal component analysis produced a statistical model of the motion and deformation of the heart. We discuss how this model could be used to assist motion correction.

Abstract: The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.